U.S. patent application number 17/064400 was filed with the patent office on 2021-07-29 for wellbore interference operation control.
This patent application is currently assigned to HALLIBURTON ENERGY SERVICES, INC.. The applicant listed for this patent is HALLIBURTON ENERGY SERVICES, INC.. Invention is credited to Joshua Lane CAMP, Ronald Glen DUSTERHOFT, Ajish POTTY, William Owen Alexander RUHLE.
Application Number | 20210231009 17/064400 |
Document ID | / |
Family ID | 1000005169725 |
Filed Date | 2021-07-29 |
United States Patent
Application |
20210231009 |
Kind Code |
A1 |
RUHLE; William Owen Alexander ;
et al. |
July 29, 2021 |
WELLBORE INTERFERENCE OPERATION CONTROL
Abstract
Aspects of the subject technology relate to systems and methods
for controlling a hydraulic fracturing job. Systems and methods are
provided for receiving diagnostics data of a hydraulic fracturing
completion of a wellbore, accessing a fracture formation model that
models formation characteristics of fractures formed through the
wellbore into a formation surrounding the wellbore during the
hydraulic fracturing completion with respect to surface variables
of the hydraulic fracturing completion, selecting one or more
subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore, and
applying the fracture formation model based on the diagnostics data
to determine values of the surface variables.
Inventors: |
RUHLE; William Owen Alexander;
(Denver, CO) ; DUSTERHOFT; Ronald Glen; (Katy,
TX) ; POTTY; Ajish; (Missouri City, TX) ;
CAMP; Joshua Lane; (Friendswood, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HALLIBURTON ENERGY SERVICES, INC. |
Houston |
TX |
US |
|
|
Assignee: |
HALLIBURTON ENERGY SERVICES,
INC.
Houston
TX
|
Family ID: |
1000005169725 |
Appl. No.: |
17/064400 |
Filed: |
October 6, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62965698 |
Jan 24, 2020 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V 1/226 20130101;
E21B 43/26 20130101; G01V 99/005 20130101; E21B 47/06 20130101;
E21B 49/00 20130101; E21B 47/07 20200501; G01V 1/40 20130101; E21B
2200/20 20200501 |
International
Class: |
E21B 49/00 20060101
E21B049/00; E21B 43/26 20060101 E21B043/26; G01V 1/22 20060101
G01V001/22; G01V 99/00 20060101 G01V099/00; G01V 1/40 20060101
G01V001/40 |
Claims
1. A method comprising: receiving diagnostics data of a hydraulic
fracturing completion of a wellbore; accessing a fracture formation
model that models formation characteristics of fractures formed
through the wellbore into a formation surrounding the wellbore
during the hydraulic fracturing completion with respect to surface
variables of the hydraulic fracturing completion; selecting one or
more subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore; and
applying the fracture formation model based on the diagnostics data
to determine values of the surface variables for controlling the
formation characteristics of the fractures to converge on the one
or more subsurface objective functions.
2. The method of claim 1, further comprising measuring the
interference information from the neighboring wellbore with a
distributed acoustic sensing fiber optic cable at the wellbore.
3. The method of claim 2, wherein the interference information from
the neighboring wellbore includes data relating to at least one of
pressure, temperature, and strain measured at the wellbore.
4. The method of claim 1, further comprising receiving pressure
data from pressure gauges at the neighboring wellbore to determine
pressure changes at the neighboring wellbore.
5. The method of claim 1, wherein the interference information from
the neighboring wellbore includes drainage data measured at the
wellbore, the drainage data including a change in drainage rate
based on interference from the neighboring wellbore.
6. The method of claim 1, further comprising measuring
micro-seismic data at the wellbore to determine projected geometry
of the fractures.
7. The method of claim 6, wherein the projected geometry of the
fractures includes at least one of length, width, height, and
velocity of the fractures.
8. The method of claim 1, wherein the objective function for
wellbore interference is determined for each stage of a plurality
of stages of the wellbore.
9. The method of claim 8, further comprising determining which
stages of the plurality of stages of the wellbore are out-target
and in-target to determine if the neighboring wellbore is
interfering with the wellbore.
10. The method of claim 1, further comprising modifying the surface
variables based on the fracture formation model applied according
to the diagnostics data to converge on the one or more subsurface
objective functions including the objective function for wellbore
interference.
11. The method of claim 1, further comprising forming a completion
plan for performing the hydraulic fracturing completion of the
wellbore based on the values of the surface variables.
12. The method of claim 11, wherein the completion plan is a
modified completion plan formed by modifying a previous completion
plan for the hydraulic fracturing completion based on the values of
one or more of the surface variables.
13. The method of claim 1, wherein the formation characteristics
include either or both subsurface fracture geometry of the
fractures during formation and growth behavior of the fractures
during formation.
14. The method of claim 1, wherein the plurality of subsurface
objective functions include one or more of an objective function
for fracture complexity, an objective function for cluster
efficiency, and an objective function for one or more surface costs
associated with changing one or more of the formation
characteristics of the fractures during the hydraulic fracturing
completion.
15. The method of claim 1, wherein the selecting of the one or more
subsurface objective functions includes assigning a weight to each
of the one or more subsurface objective functions that is greater
than a corresponding weight assigned to each other subsurface
objective functions of the plurality of subsurface objective
functions.
16. A system comprising: one or more processors; and at least one
computer-readable storage medium having stored therein instructions
which, when executed by the one or more processors, cause the
system to: receive diagnostics data of a hydraulic fracturing
completion of a wellbore; access a fracture formation model that
models formation characteristics of fractures formed through the
wellbore into a formation surrounding the wellbore during the
hydraulic fracturing completion with respect to surface variables
of the hydraulic fracturing completion; select one or more
subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore; and
apply the fracture formation model based on the diagnostics data to
determine values of the surface variables for controlling the
formation characteristics of the fractures to converge on the one
or more subsurface objective functions.
17. The system of claim 16, wherein the instructions which, when
executed by the one or more processors, cause the system to measure
the interference information from the neighboring wellbore with a
distributed acoustic sensing fiber optic cable at the wellbore.
18. The system of claim 17, wherein the interference information
from the neighboring wellbore includes data relating to at least
one of pressure, temperature, and strain measured at the
wellbore.
19. A non-transitory computer-readable storage medium comprising:
instructions stored on the non-transitory computer-readable storage
medium, the instructions, when executed by one or more processors,
cause the one or more processors to: receive diagnostics data of a
hydraulic fracturing completion of a wellbore; access a fracture
formation model that models formation characteristics of fractures
formed through the wellbore into a formation surrounding the
wellbore during the hydraulic fracturing completion with respect to
surface variables of the hydraulic fracturing completion; select
one or more subsurface objective functions from a plurality of
subsurface objective functions for changing one or more of the
formation characteristics of the fractures, the one or more
subsurface objective functions including an objective function for
wellbore interference, the objective function for wellbore
interference including interference information from a neighboring
wellbore; and apply the fracture formation model based on the
diagnostics data to determine values of the surface variables for
controlling the formation characteristics of the fractures to
converge on the one or more subsurface objective functions.
20. The non-transitory computer-readable storage medium of claim
19, wherein the instructions, when executed by the one or more
processors, cause the one or more processors to measure the
interference information from the neighboring wellbore with a
distributed acoustic sensing fiber optic cable at the wellbore.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 62/965,698, filed on Jan. 24, 2020, entitled
"HYDRAULIC FRACTURING MODELLING AND CONTROL," the contents of which
are incorporated herein by reference in their entirety.
TECHNICAL FIELD
[0002] The present technology pertains to controlling a hydraulic
fracturing completion of a wellbore based on completion
characteristics of the completion, and more particularly, to
selecting one or more completion plans from a plurality of
completion plans based on wellbore interference data and
facilitating completion of the wellbore according to the one or
more selected completion plans.
BACKGROUND
[0003] Completion of a wellbore through hydraulic fracturing is a
complex process. The hydraulic fracturing process includes a number
of different variables that can be altered to perform a well
completion. Specifically, parameters related to perforation
initiation and creation, e.g. through a plug-and-perf technique,
can be altered during a hydraulic fracturing process to perform a
well completion. Further, parameters related to fracture creation
and stabilization can be altered during a hydraulic fracturing
process to perform a well completion. Currently, fracturing jobs
are performed by operators that rely heavily on their own knowledge
and experience to complete a well. Hydraulic fracturing
technologies have developed to provide real time fracturing data to
operators performing a fracturing job. However, operators still
rely on their own knowledge and experience to interpret this real
time fracturing data and perform a well completion. This is
problematic as operators are often unable to properly interpret the
wealth of real time fracturing data that is gathered and provided
to them in order to control a hydraulic fracturing job.
Specifically, as the hydraulic fracturing process is complex and
encompasses a number of different variables that can be altered to
perform a well completion, it becomes difficult for operators to
alter the variables of the hydraulic fracturing process based on
real time fracturing data to properly control a hydraulic
fracturing job. As a result, operators tend to rely more heavily on
their own knowledge and experience instead of real time fracturing
data to control a hydraulic fracturing process, often leading to
detrimental effects on a well completion job.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] In order to describe the manner in which the features and
advantages of this disclosure can be obtained, a more particular
description is provided with reference to specific embodiments
thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only exemplary embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0005] FIG. 1 shows an example schematic diagram of a fracturing
system, in accordance with various aspects of the subject
technology;
[0006] FIG. 2 shows a well during a fracturing operation in a
portion of a subterranean formation of interest surrounding a
wellbore, in accordance with various aspects of the subject
technology;
[0007] FIG. 3 shows a portion of a wellbore that is fractured using
multiple fracture stages, in accordance with various aspects of the
subject technology;
[0008] FIG. 4 shows an example flow of a method for controlling a
fracturing completion job based on both surface observations and
subsurface diagnostics, in accordance with various aspects of the
subject technology;
[0009] FIG. 5 shows an example flow of a method for performing a
fracturing completion job with a fracture completion model, in
accordance with various aspects of the subject technology;
[0010] FIGS. 6A-6C show example diagrams of a fracturing system for
controlling a fracturing completion job based on wellbore
interference, in accordance with various aspects of the subject
technology;
[0011] FIG. 7 shows an example fracturing operation utilizing set
points, in accordance with various aspects of the subject
technology;
[0012] FIG. 8 shows an example fracturing operation with multiple
wellbores and respective fracturing interactions between the
multiple wellbores, in accordance with various aspects of the
subject technology;
[0013] FIG. 9 shows an example process for controlling a fracturing
completion job, in accordance with various aspects of the subject
technology; and
[0014] FIG. 10 shows an example computing device architecture which
can be employed to perform various steps, methods, and techniques
disclosed herein.
DETAILED DESCRIPTION
[0015] Various embodiments of the disclosure are discussed in
detail below. While specific implementations are discussed, it
should be understood that this is done for illustration purposes
only. A person skilled in the relevant art will recognize that
other components and configurations may be used without parting
from the spirit and scope of the disclosure.
[0016] Additional features and advantages of the disclosure will be
set forth in the description which follows, and in part will be
obvious from the description, or can be learned by practice of the
principles disclosed herein. The features and advantages of the
disclosure can be realized and obtained by means of the instruments
and combinations particularly pointed out in the appended claims.
These and other features of the disclosure will become more fully
apparent from the following description and appended claims or can
be learned by the practice of the principles set forth herein.
[0017] It will be appreciated that for simplicity and clarity of
illustration, where appropriate, reference numerals have been
repeated among the different figures to indicate corresponding or
analogous elements. In addition, numerous specific details are set
forth in order to provide a thorough understanding of the
embodiments described herein. However, it will be understood by
those of ordinary skill in the art that the embodiments described
herein can be practiced without these specific details. In other
instances, methods, procedures, and components have not been
described in detail so as not to obscure the related relevant
feature being described. The drawings are not necessarily to scale
and the proportions of certain parts may be exaggerated to better
illustrate details and features. The description is not to be
considered as limiting the scope of the embodiments described
herein.
[0018] Subterranean hydraulic fracturing is conducted to increase
or "stimulate" production from a hydrocarbon well. To conduct a
fracturing process, pressure is used to pump special fracturing
fluids, including some that contain propping agents ("proppants"),
down-hole and into a hydrocarbon formation to split or "fracture"
the rock formation along veins or planes extending from the
well-bore. Once the desired fracture is formed, the fluid flow is
reversed and the liquid portion of the fracturing fluid is removed.
The proppants are intentionally left behind to stop the fracture
from closing onto itself due to the weight and stresses within the
formation. The proppants thus literally "prop-apart", or support
the fracture to stay open, yet remain highly permeable to
hydrocarbon fluid flow since they form a packed bed of particles
with interstitial void space connectivity. Sand is one example of a
commonly-used proppant. The newly-created-and-propped fracture or
fractures can thus serve as new formation drainage area and new
flow conduits from the formation to the well, providing for an
increased fluid flow rate, and hence increased production of
hydrocarbons.
[0019] To begin a fracturing process, at least one perforation is
made at a particular down-hole location through the well into a
subterranean formation, e.g. through a wall of the well casing, to
provide access to the formation for the fracturing fluid. The
direction of the perforation attempts to determine at least the
initial direction of the fracture.
[0020] A first "mini-fracture" test can be conducted in which a
relatively small amount of proppant-free fracturing fluid is pumped
into the formation to determine and/or confirm at least some of the
properties of the formation, such as the permeability of the
formation itself. Accurately knowing the permeability allows for a
prediction of the fluid leak-off rate at various pressures, whereby
the amount of fracturing fluid that will flow into the formation
can be considered in establishing a pumping and proppant schedule.
Thus, the total amount of fluid to be pumped down-hole is at least
the sum of the cased volume of the well, the amount of fluid that
fills the fracture, and the amount of fluid that leaks-off into the
formation during the fracturing process itself. Leak-off rate is an
important parameter because once proppant-laden fluid is pumped
into the fracture, leak-off can increase the concentration of the
proppant in the fracturing fluid beyond a target level. Data from
the mini-fracture test then is usually used by experts to confirm
or modify the original desired target profile of the fracture and
the completion process used to achieve the fracture.
[0021] Fracturing then begins in earnest by first pumping
proppant-free fluid into the wellbore or through tubing. The
fracture is initiated and begins to grow in height, length, and/or
width. This first proppant-free stage is usually called the
"pre-pad" and consists of a low viscosity fluid. A second fluid
pumping stage is usually then conducted of a different viscosity
proppant-free fluid called the "pad." At a particular time in the
pumping process, the proppant is then added to a fracturing and
propping flow stream using a continuous blending process, and is
usually gradually stepped-up in proppant concentration. The
resultant fractures are then filled or partly filled with proppant
to stabilize the fractures.
[0022] This process can be repeated in a plurality of fracturing
stages to form a plurality of fractures through a wellbore, e.g. as
part of a well completion phase. In particular and as will be
discussed in greater detail later, this process can be repeatedly
performed through a plug-and-perf technique to form the fractures
throughout a subterranean formation. After the fractures are
formed, resources, e.g. hydrocarbons, can be extracted from the
fractures during a well production phase.
[0023] The hydraulic fracturing process includes a number of
different variables, e.g. surface variables, that can be altered to
perform a well completion. However, it is very difficult to control
these variables and achieve a desired fracture geometry design for
the completion. Specifically, it is difficult to control the large
number of surface variables of a hydraulic fracturing completion,
e.g. in real time, to cause actual fracture geometries and growth
behaviors to converge on planned fracture geometries and growth
behaviors. More specifically and because of the large number of
variables that can be manipulated for controlling fracture
geometries and growth behaviors, it is difficult for a human
operator to control such variables, e.g. in real time, to cause
actual fracture geometries and growth behaviors to converge on
planned fracture geometries and growth behaviors.
[0024] In planning a hydraulic fracturing completion for a
reservoir asset, the reservoir can be divided into geometric
spacing units that delineate hydrocarbon drainage patterns for each
wellbore. Fracture geometry designs can then be planned based on
the spacing units. Upon finishing of the fracture geometry design,
wellbores can be drilled and completed within those spacing unit
with the intent of creating fractures that connect reservoir across
the spacing units. However, geological discontinuity within and
across the spacing units can cause large variations in fracture
geometries and growth behaviors during well completion. In turn,
this can make it more difficult to control the large number of
variables of a hydraulic fracturing completion, e.g. in real time,
to cause actual fracture geometries and growth behaviors to
converge on planned fracture geometries and growth behaviors.
[0025] The disclosed technology addresses the foregoing by
selecting one or more subsurface objective functions for a
hydraulic fracture completion of a wellbore. In turn, variables of
the completion can be selected for controlling fracture formation
characteristics during the completion to converge on the one or
more subsurface objective functions.
[0026] In various embodiments, a method for conducting a hydraulic
fracturing job on a plurality of wellbores in a subterranean
formation can include receiving diagnostics data of a hydraulic
fracturing completion of a wellbore. The method can further include
accessing a fracture formation model that models formation
characteristics of fractures formed through the wellbore into a
formation surrounding the wellbore during the hydraulic fracturing
completion with respect to surface variables of the hydraulic
fracturing completion. The method can also include selecting one or
more subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore.
Furthermore, the method can include applying the fracture formation
model based on the diagnostics data to determine values of the
surface variables for controlling the formation characteristics of
the fractures to converge on the one or more subsurface objective
functions.
[0027] In various embodiments, a system for conducting a hydraulic
fracturing job on a plurality of wellbores in a subterranean
formation can include one or more processors; and at least one
computer-readable storage medium having stored therein instructions
which, when executed by the one or more processors, cause the
system to receive diagnostics data of a hydraulic fracturing
completion of a wellbore. The instructions can further cause the
system to access a fracture formation model that models formation
characteristics of fractures formed through the wellbore into a
formation surrounding the wellbore during the hydraulic fracturing
completion with respect to surface variables of the hydraulic
fracturing completion. Furthermore, the instructions can cause the
system to select one or more subsurface objective functions from a
plurality of subsurface objective functions for changing one or
more of the formation characteristics of the fractures, the one or
more subsurface objective functions including an objective function
for wellbore interference, the objective function for wellbore
interference including interference information from a neighboring
wellbore. Also, the instructions can cause the system to apply the
fracture formation model based on the diagnostics data to determine
values of the surface variables for controlling the formation
characteristics of the fractures to converge on the one or more
subsurface objective functions.
[0028] In various embodiments, a non-transitory computer-readable
storage medium comprising instructions stored on the non-transitory
computer-readable storage medium, the instructions, when executed
by one or more processors, cause the one or more processors to
receive diagnostics data of a hydraulic fracturing completion of a
wellbore. The instructions can further cause the one or more
processors to access a fracture formation model that models
formation characteristics of fractures formed through the wellbore
into a formation surrounding the wellbore during the hydraulic
fracturing completion with respect to surface variables of the
hydraulic fracturing completion. Furthermore, the instructions can
further cause the one or more processors to select one or more
subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore.
Also, the instructions can further cause the one or more processors
to apply the fracture formation model based on the diagnostics data
to determine values of the surface.
[0029] Turning now to FIG. 1, an example fracturing system 10 is
shown. The example fracturing system 10 shown in FIG. 1 can be
implemented using the systems, methods, and techniques described
herein. In particular, the disclosed system, methods, and
techniques may directly or indirectly affect one or more components
or pieces of equipment associated with the example fracturing
system 10, according to one or more embodiments. The fracturing
system 10 includes a fracturing fluid producing apparatus 20, a
fluid source 30, a solid source 40, and a pump and blender system
50. All or an applicable combination of these components of the
fracturing system 10 can reside at the surface at a well
site/fracturing pad where a well 60 is located.
[0030] During a fracturing job, the fracturing fluid producing
apparatus 20 can access the fluid source 30 for
introducing/controlling flow of a fluid, e.g. a fracturing fluid,
in the fracturing system 10. While only a single fluid source 30 is
shown, the fluid source 30 can include a plurality of separate
fluid sources. Further, the fracturing fluid producing apparatus 20
can be omitted from the fracturing system 10. In turn, the
fracturing fluid can be sourced directly from the fluid source 30
during a fracturing job instead of through the intermediary
fracturing fluid producing apparatus 20.
[0031] The fracturing fluid can be an applicable fluid for forming
fractures during a fracture stimulation treatment of the well 60.
For example, the fracturing fluid can include water, a hydrocarbon
fluid, a polymer gel, foam, air, wet gases, and/or other applicable
fluids. In various embodiments, the fracturing fluid can include a
concentrate to which additional fluid is added prior to use in a
fracture stimulation of the well 60. In certain embodiments, the
fracturing fluid can include a gel pre-cursor with fluid, e.g.
liquid or substantially liquid, from fluid source 30. Accordingly,
the gel pre-cursor with fluid can be mixed by the fracturing fluid
producing apparatus 20 to produce a hydrated fracturing fluid for
forming fractures.
[0032] The solid source 40 can include a volume of one or more
solids for mixture with a fluid, e.g. the fracturing fluid, to form
a solid-laden fluid. The solid-laden fluid can be pumped into the
well 60 as part of a solids-laden fluid stream that is used to form
and stabilize fractures in the well 60 during a fracturing job. The
one or more solids within the solid source 40 can include
applicable solids that can be added to the fracturing fluid of the
fluid source 30. Specifically, the solid source 40 can contain one
or more proppants for stabilizing fractures after they are formed
during a fracturing job, e.g. after the fracturing fluid flows out
of the formed fractures. For example, the solid source 40 can
contain sand.
[0033] The fracturing system 10 can also include additive source
70. The additive source 70 can contain/provide one or more
applicable additives that can be mixed into fluid, e.g. the
fracturing fluid, during a fracturing job. For example, the
additive source 70 can include solid-suspension-assistance agents,
gelling agents, weighting agents, and/or other optional additives
to alter the properties of the fracturing fluid. The additives can
be included in the fracturing fluid to reduce pumping friction, to
reduce or eliminate the fluid's reaction to the geological
formation in which the well is formed, to operate as surfactants,
and/or to serve other applicable functions during a fracturing job.
As will be discussed in greater detail later, the additives can
function to maintain solid particle suspension in a mixture of
solid particles and fracturing fluid as the mixture is pumped down
the well 60 to one or more perforations.
[0034] The pump and blender system 50 functions to pump fracture
fluid into the well 60. Specifically, the pump and blender system
50 can pump fracture fluid from the fluid source 30, e.g. fracture
fluid that is received through the fracturing fluid producing
apparatus 20, into the well 60 for forming and potentially
stabilizing fractures as part of a fracture job. The pump and
blender system 50 can include one or more pumps. Specifically, the
pump and blender system 50 can include a plurality of pumps that
operate together, e.g. concurrently, to form fractures in a
subterranean formation as part of a fracturing job. The one or more
pumps included in the pump and blender system 50 can be an
applicable type of fluid pump. For example, the pumps in the pump
and blender system 50 can include electric pumps and/or hydrocarbon
and hydrocarbon mixture powered pumps. Specifically, the pumps in
the pump and blender system 50 can include diesel powered pumps,
natural gas powered pumps, and diesel combined with natural gas
powered pumps.
[0035] The pump and blender system 50 can also function to receive
the fracturing fluid and combine it with other components and
solids. Specifically, the pump and blender system 50 can combine
the fracturing fluid with volumes of solid particles, e.g.
proppant, from the solid source 40 and/or additional fluid and
solids from the additive source 70. In turn, the pump and blender
system 50 can pump the resulting mixture down the well 60 at a
sufficient pumping rate to create or enhance one or more fractures
in a subterranean zone, for example, to stimulate production of
fluids from the zone. While the pump and blender system 50 is
described to perform both pumping and mixing of fluids and/or solid
particles, in various embodiments, the pump and blender system 50
can function to just pump a fluid stream, e.g. a fracture fluid
stream, down the well 60 to create or enhance one or more fractures
in a subterranean zone.
[0036] The fracturing fluid producing apparatus 20, fluid source
30, and/or solid source 40 may be equipped with one or more
monitoring devices (not shown). The monitoring devices can be used
to control the flow of fluids, solids, and/or other compositions to
the pumping and blender system 50. Such monitoring devices can
effectively allow the pumping and blender system 50 to source from
one, some or all of the different sources at a given time. In turn,
the pumping and blender system 50 can provide just fracturing fluid
into the well at some times, just solids or solid slurries at other
times, and combinations of those components at yet other times.
[0037] FIG. 2 shows the well 60 during a fracturing operation in a
portion of a subterranean formation of interest 102 surrounding a
wellbore 104. The fracturing operation can be performed using one
or an applicable combination of the components in the example
fracturing system 10 shown in FIG. 1. The wellbore 104 extends from
the surface 106, and the fracturing fluid 108 is applied to a
portion of the subterranean formation 102 surrounding the
horizontal portion of the wellbore. Although shown as vertical
deviating to horizontal, the wellbore 104 may include horizontal,
vertical, slant, curved, and other types of wellbore geometries and
orientations, and the fracturing treatment may be applied to a
subterranean zone surrounding any portion of the wellbore 104. The
wellbore 104 can include a casing 110 that is cemented or otherwise
secured to the wellbore wall. The wellbore 104 can be uncased or
otherwise include uncased sections. Perforations can be formed in
the casing 110 to allow fracturing fluids and/or other materials to
flow into the subterranean formation 102. As will be discussed in
greater detail below, perforations can be formed in the casing 110
using an applicable wireline-free actuation. In the example
fracture operation shown in FIG. 2, a perforation is created
between points 114.
[0038] The pump and blender system 50 is fluidly coupled to the
wellbore 104 to pump the fracturing fluid 108, and potentially
other applicable solids and solutions into the wellbore 104. When
the fracturing fluid 108 is introduced into wellbore 104 it can
flow through at least a portion of the wellbore 104 to the
perforation, defined by points 114. The fracturing fluid 108 can be
pumped at a sufficient pumping rate through at least a portion of
the wellbore 104 to create one or more fractures 116 through the
perforation and into the subterranean formation 102. Specifically,
the fracturing fluid 108 can be pumped at a sufficient pumping rate
to create a sufficient hydraulic pressure at the perforation to
form the one or more fractures 116. Further, solid particles, e.g.
proppant from the solid source 40, can be pumped into the wellbore
104, e.g. within the fracturing fluid 108 towards the perforation.
In turn, the solid particles can enter the fractures 116 where they
can remain after the fracturing fluid flows out of the wellbore.
These solid particles can stabilize or otherwise "prop" the
fractures 116 such that fluids can flow freely through the
fractures 116.
[0039] While only two perforations at opposing sides of the
wellbore 104 are shown in FIG. 2, as will be discussed in greater
detail below, greater than two perforations can be formed in the
wellbore 104, e.g. along the top side of the wellbore 104, as part
of a perforation cluster. Fractures can then be formed through the
plurality of perforations in the perforation cluster as part of a
fracturing stage for the perforation cluster. Specifically,
fracturing fluid and solid particles can be pumped into the
wellbore 104 and pass through the plurality of perforations during
the fracturing stage to form and stabilize the fractures through
the plurality of perforations.
[0040] FIG. 3 shows a portion of a wellbore 300 that is fractured
using multiple fracture stages. Specifically, the wellbore 300 is
fractured in multiple fracture stages using a plug-and-perf
technique.
[0041] The example wellbore 300 includes a first region 302 within
a portion of the wellbore 300. The first region 302 can be
positioned in proximity to a terminal end of the wellbore 300. The
first region 302 is formed within the wellbore 300, at least in
part, by a plug 304. Specifically, the plug 304 can function to
isolate the first region 302 of the wellbore 300 from another
region of the wellbore 300, e.g. by preventing the flow of fluid
from the first region 302 to another region of the wellbore 300.
The region isolated from the first region 302 by the plug 304 can
be the terminal region of the wellbore 300. Alternatively, the
region isolated from the first region 302 by the plug 304 can be a
region of the wellbore 300 that is closer to the terminal end of
the wellbore 300 than the first region 302. While the first region
302 is shown in FIG. 3 to be formed, at least in part, by the plug
304, in various embodiments, the first region 302 can be formed, at
least in part, by a terminal end of the wellbore 300 instead of the
plug 304. Specifically, the first region 302 can be a terminal
region within the wellbore 300.
[0042] The first region 302 includes a first perforation 306-1, a
second perforation 306-2, and a third perforation 306-3. The first
perforation 306-1, the second perforation 306-2, and the third
perforation 306-3 can form a perforation cluster 306 within the
first region 302 of the wellbore 300. While three perforations are
shown in the perforation cluster 306, in various embodiments, the
perforation cluster 306 can include fewer or more perforations. As
will be discussed in greater detail later, fractures can be formed
and stabilized within a subterranean formation through the
perforations 306-1, 306-2, and 306-3 of the perforation cluster 306
within the first region 302 of the wellbore 300. Specifically,
fractures can be formed and stabilized through the perforation
cluster 306 within the first region 302 by pumping fracturing fluid
and solid particles into the first region 302 and through the
perforations 306-1, 306-2, and 306-3 into the subterranean
formation.
[0043] The example wellbore 300 also includes a second region 310
positioned closer to the wellhead than the first region 302.
Conversely, the first region 302 is in closer proximity to a
terminal end of the wellbore 300 than the second region 310. For
example, the first region 302 can be a terminal region of the
wellbore 300 and therefore be positioned closer to the terminal end
of the wellbore 300 than the second region 310. The second region
310 is isolated from the first region 302 by a plug 308 that is
positioned between the first region 302 and the second region 310.
The plug 308 can fluidly isolate the second region 310 from the
first region 302. As the plug 308 is positioned between the first
and second regions 302 and 310, when fluid and solid particles are
pumped into the second region 310, e.g. during a fracture stage,
the plug 308 can prevent the fluid and solid particles from passing
from the second region 310 into the first region 302.
[0044] The second region 310 includes a first perforation 312-1, a
second perforation 312-2, and a third perforation 312-3. The first
perforation 312-1, the second perforation 312-2, and the third
perforation 312-3 can form a perforation cluster 312 within the
second region 310 of the wellbore 300. While three perforations are
shown in the perforation cluster 312, in various embodiments, the
perforation cluster 312 can include fewer or more perforations. As
will be discussed in greater detail later, fractures can be formed
and stabilized within a subterranean formation through the
perforations 312-1, 312-2, and 312-3 of the perforation cluster 312
within the second region 310 of the wellbore 300. Specifically,
fractures can be formed and stabilized through the perforation
cluster 312 within the second region 310 by pumping fracturing
fluid and solid particles into the second region 310 and through
the perforations 312-1, 312-2, and 312-3 into the subterranean
formation.
[0045] In fracturing the wellbore 300 in multiple fracturing stages
through a plug-and-perf technique, the perforation cluster 306 can
be formed in the first region 302 before the second region 310 is
formed using the plug 308. Specifically, the perforations 306-1,
306-2, and 306-3 can be formed before the perforations 312-1,
312-2, and 312-3 are formed in the second region 310. As will be
discussed in greater detail later, the perforations 306-1, 306-2,
and 306-3 can be formed using a wireline-free actuation. Once the
perforations 306-1, 306-2, and 306-3 are formed, fracturing fluid
and solid particles can be transferred through the wellbore 300
into the perforations 306-1, 306-2, and 306-3 to form and stabilize
fractures in the subterranean formation as part of a first
fracturing stage. The fracturing fluid and solid particles can be
transferred from a wellhead of the wellbore 300 to the first region
302 through the second region 310 of the wellbore 300.
Specifically, the fracturing fluid and solid particles can be
transferred through the second region 310 before the second region
310 is formed, e.g. using the plug 308, and the perforation cluster
312 is formed. This can ensure, at least in part, that the
fracturing fluid and solid particles flow through the second region
310 and into the subterranean formation through the perforations
306-1, 306-2, and 306-3 within the perforation cluster 306 in the
first region 302.
[0046] After the fractures are formed through the perforations
306-1, 306-2, and 306-3, the wellbore 300 can be filled with the
plug 308. Specifically, the wellbore 300 can be plugged with the
plug 308 to form the second region 310. Then, the perforations
312-1, 312-2, and 312-3 can be formed, e.g. using a wireline or
wireline-free actuation. Once the perforations 312-1, 312-2, and
312-3 are formed, fracturing fluid and solid particles can be
transferred through the wellbore 300 into the perforations 312-1,
312-2, and 312-3 to form and stabilize fractures in the
subterranean formation as part of a second fracturing stage. The
fracturing fluid and solid particles can be transferred from the
wellhead of the wellbore 300 to the second region 310 while the
plug 308 prevents transfer of the fluid and solid particles to the
first region 302. This can effectively isolate the first region 302
until the first region 302 is accessed for production of resources,
e.g. hydrocarbons. After the fractures are formed through the
perforation cluster 312 in the second region 310, a plug can be
positioned between the second region 310 and the wellhead, e.g. to
fluidly isolate the second region 310. This process of forming
perforations, forming fractures during a fracture stage, followed
by plugging on a region by region basis can be repeated.
Specifically, this process can be repeated up the wellbore towards
the wellhead until a completion plan for the wellbore 300 is
finished.
[0047] FIG. 4 shows an example flow 400 of a method for controlling
a fracturing completion job based on both surface observations and
subsurface diagnostics. The method shown in FIG. 4 can be
implemented with an applicable fracturing system for completing a
wellbore. For example, the method shown in FIG. 4 can be used to
control the fracturing system 10 shown in FIG. 1.
[0048] The method shown in FIG. 4 can be applied during a
fracturing completion job of one or more wellbores. Specifically,
and as will be discussed in greater detail later, the method can be
applied to perform the fracturing completion job according to one
or more fracturing completion plans. More specifically, the method
can be applied to identify one or more fracturing completion plans
for performing/continuing the fracturing completion job.
[0049] A fracturing completion plan, as used herein, can specify
how to perform hydraulic fracturing to achieve a target completion
in a wellbore. A target completion in a wellbore can specify
desired characteristics of a hydraulic fracture completion in a
wellbore. For example, a target completion can include fractures
that extend anywhere from between 80 and 100 feet into a reservoir
to several hundred feet into the reservoir. Further in the example,
the target completion can include that the fractures are formed at
locations through the wellbore that are spaced apart from each
other by 10 feet. A target completion can be specified by a
customer. As follows, the fracturing completion job can be
performed to achieve, or otherwise attempt to achieve, the target
completion for the customer.
[0050] In specifying how to perform hydraulic fracturing for a
wellbore completion, a fracturing completion plan can include
values of varying fracturing completion parameters and/or reservoir
parameters. More specifically, a fracturing completion plan can
include values of fracturing completion parameters and/or reservoir
parameters that vary across different fracturing completion plans,
thereby distinguishing the fracturing completion plans from each
other. In turn, different fracturing completion plans,
corresponding to different values of fracturing completion
parameters and/or reservoir parameters, can be applied in
performing the fracturing completion job, e.g. to achieve the
target completion. For example, in performing the fracturing
completion job, an operator can apply an initial fracturing
completion plan and modify a fracturing completion parameter of the
initial fracturing completion plan, effectively applying a new
fracturing completion plan.
[0051] Fracturing completion and reservoir parameters can include
applicable parameters related to performing hydraulic fracturing,
e.g. as part of a well completion, in a formation. Specifically,
fracturing completion parameters and reservoir parameters can
include applicable parameters that are variable as part of
performing hydraulic fracturing in a formation. For example,
reservoir parameters can include varying characteristics of a
formation, e.g. varying matrix permeability and porosity, in which
hydraulic fracturing is or will be performed. Fracturing completion
and reservoir parameters can include applicable parameters related
to perforation/opening formation in a wellbore as part of
performing hydraulic fracturing. For example, fracturing completion
parameters can include parameters related to control of a wireline
or a non-wireline technique for forming perforations in a wellbore
as part a hydraulic fracturing process.
[0052] Further, fracturing completion and reservoir parameters can
include applicable parameters related to fracture creation and
stabilization into a medium through perforations/opening in a
wellbore as part of performing hydraulic fracturing. Specifically,
fracturing completion and reservoir parameters can include fluid
control parameters related to hydraulic fracturing. For example,
fracture completion parameters can include a rate at which fluid is
pumped into a wellbore for forming fractures through the wellbore.
Further, fracturing completion and reservoir parameters can include
proppant control parameters related to hydraulic fracturing. For
example, fracture completion parameters can include a type of
proppant that is pumped into a wellbore, a rate at which the
proppant is pumped into the wellbore, and applicable proppant
concentration to ramp characteristics for stabilizing fractures
through the wellbore. Additionally, fracturing completion and
reservoir parameters can include additive control parameters. For
example, fracture completion parameters can include an amount of at
least one of a viscosifier, a friction reducer, a diverter agent, a
pH adjustment agent, a surfactant, a clay stabilizer, a formation
stabilizer, a viscosity breaker additive, and other applicable
additives to add to a proppant mixture pumped down a wellbore for
stabilizing fractures through the wellbore.
[0053] In the example flow 400 shown in FIG. 4, surface diagnostics
data at the fracturing completion job, otherwise referred to as
real time fracturing data, is gathered at step 402. Surface
diagnostics data can describe applicable surface observations at
the fracturing completion job. For example, surface diagnostics
data can describe surface pressures and offset or monitoring well
pressures at one or more wellbores of the fracturing completion
job, injection characteristics of either or both fluid and proppant
into the one or more wellbores, and injection characteristics of
one or more additives into the one or more wellbores. For example,
surface diagnostics data can describe rates at which a diverter is
introduced into a wellbore as part of a diverter stage of the
fracturing completion job. Surface diagnostics data can be gathered
by applicable sensors, equipment, and surface monitoring technique,
associated with hydraulic fracturing. For example, surface
diagnostics data can be gathered by flow sensors integrated at
wellheads of a pad.
[0054] Additionally, in the example flow 400 shown in FIG. 4,
subsurface diagnostics data at the fracturing completion job,
otherwise referred to as real time diagnostics data, is gathered at
step 404. Subsurface diagnostics data can describe applicable
subsurface diagnostics occurring at the fracture completion job.
Specifically, subsurface diagnostics data can describe flowrates
per perforation cluster in a wellbore of the fracturing completion
job, flowrates per perforation in the wellbore, temperature on
stages in the wellbore, microseismic activity in the wellbore,
acoustic measurements in the wellbore, strain measurements in the
wellbore, bottom hole pressure in the wellbore, and instantaneous
shut in pressures in the wellbore. Shut in pressure, as used
herein, includes a pressure in a wellbore once fluid, proppant, and
additives are no longer pumped into the wellbore at a completion of
a fracture creation and stabilization stage. Subsurface diagnostics
data can be gathered through applicable sensors, equipment, and
subsurface monitoring techniques associated with hydraulic
fracturing. For example, subsurface diagnostics data at the
fracturing completion job can be gathered using one or more fiber
optic cables, e.g. fiber optic cables integrated with one or more
wellbores of the fracturing completion job. Fiber optic cables can
include fiber optic lines, fiber optic tubes, waveguides, optical
waveguides, or any other fiber suitable for the intended purpose
and understood by a person of ordinary skill in the art. In another
example, subsurface diagnostics data at the fracturing completion
job can be gathered by one or more acoustic sensors, e.g. acoustic
sensors integrated with one or more wellbores of the fracturing
completion job. In yet another example, subsurface diagnostics data
at the fracturing completion job can be gathered by one or more
strain sensors, e.g. strain sensors integrated with one or more
wellbores of the fracturing completion job. In another example,
subsurface diagnostics data can be gathered by systems and
equipment that measure casing strain and/or well deformation in a
wellbore.
[0055] Both the surface diagnostics data and the subsurface
diagnostics data can be gathered by monitoring off-set wells.
Specifically, surface diagnostics data and subsurface diagnostics
data for a well can be gathered by monitoring an adjacent well,
similar to as is previously discussed with respect to off-set well
monitoring. For example, microseismic activity in a well can be
monitored through a fiber optic cable implemented in an adjacent
well. Further in the example, the monitored well can function only
as a monitoring well in which fracturing operations are not
actually performed.
[0056] Both the surface diagnostics data and the subsurface
diagnostics data gathered at steps 402 and 404 can be included as
part of completion characteristics data for the fracturing
completion job. Completion characteristics data, as used herein,
includes data describing applicable characteristics of a well
completion job. For example, completion characteristics data can
include estimated characteristics of fractures formed and
stabilized during the fracturing completion job. Completion
characteristics data can be gathered before the fracturing
completion job is performed as part of the well completion, while
the fracturing completion job is performed as part of the well
completion, and after the fracturing completion job is performed as
part of the well completion. Specifically, completion
characteristics data for the fracturing completion job can be
gathered as one or more fracturing completion plans are applied to
perform the fracturing completion job. More specifically, the
completion characteristics data for the fracturing completion job
can be gathered as the fracturing completion job is performed
according to changing fracturing completion plans, e.g. as an
operator modifies a fracture completion parameters for the
fracturing completion job. For example, the completion
characteristics data for the fracturing completion job can be
gathered as an operator changes a completion plan by introducing a
diverter material during a fracturing stage.
[0057] At step 406 in the example flow 400 shown in FIG. 4, all or
portions of the subsurface diagnostics data for the fracturing
completion job are presented to one or more operators associated
with the fracturing completion job. An operator associated with the
fracturing completion job can include an applicable operator tasked
with controlling the fracture completion job. An operator can
either be present on-site at the fracture completion job or remote
from the site of the fracture completion job. For example, an
operator associated with the fracture completion job can be part of
a pumping team at a pad of the fracture completion job.
[0058] Subsurface diagnostics data can be presented to the operator
in an applicable format, e.g. through an applicable graphical user
interface. In particular, subsurface diagnostics data can be
presented to the operator in a format that allows the operator to
quickly perceive the subsurface diagnostics data and react
appropriately. This is important, as subsurface diagnostics data
can include a large amount of complicated information that is not
easily perceivable by a human. By presenting the subsurface
diagnostics data in a format that is easily perceivable, the
operator can quickly adjust the fracture completion job in response
to the subsurface diagnostics data.
[0059] In various embodiments, including the method shown in FIG.
4, when the subsurface diagnostics are for multiple wellbores on a
fracturing site, e.g. multiple wellbores pumped simultaneously,
then the subsurface diagnostics data can be presented in a format
that allows an operator to perceive the data for each individual
wellbore. Specifically,
[0060] At step 408, the operator can make a recommended action for
controlling the fracture completion job. A recommended action can
include not changing a current fracturing completion plan used in
performing the fracture completion job. A recommended action can
also include changing a current fracturing completion plan used in
performing the fracture completion job. Specifically, a recommended
action can include modifying one or more fracturing completion and
reservoir parameters of a fracturing completion plan used in
performing the fracture completion job, effectively performing the
fracture completion job using a new fracturing completion plan. For
example, a recommended action can include adding a diverter or
diverting material during a fracture stage. In another example, a
recommended action can include increasing a proppant concentration
and/or flow rate in a wellbore. The fracture completion job can
subsequently be performed according to the recommended action of
the operator, as determined at step 408. Specifically, the operator
can implement the recommended action in order to complete the one
or more wellbores as part of the fracture completion job.
[0061] The operator can identify the recommended action at step
408, based on either or both the surface diagnostics data and the
subsurface diagnostics data gathered at steps 402 and 404.
Specifically, the operator can identify the recommended action
based on the surface diagnostics data and the subsurface
diagnostics data and the knowledge and experience of the operator.
For example, the Examiner can adjust a flow pressure at which fluid
is pumped into a wellbore based on an observed flow rate at a
perforation cluster, as indicated by the subsurface diagnostics
data, and the operator's own knowledge of an ideal flow rate for
the type of formation being fractured.
[0062] FIG. 5 shows an example flow 500 of a method for performing
a fracturing completion job with a fracture completion model. In
particular, the method shown in FIG. 5 can be utilized to perform a
fracture completion job in real time by facilitating control of
hydraulic fracturing with a fracture completion model. Further and
as will be discussed in greater detail later, the method shown in
FIG. 5 can account for a large number of fracturing completion and
reservoir parameters that a human would otherwise be incapable of
accounting for in performing a fracture completion job. In turn,
this can lead to a more accurate and efficient completion of one or
more wellbores as part of a fracture completion job.
[0063] The method shown in FIG. 5 can be implemented with an
applicable fracturing system for completing a wellbore. For
example, the method shown in FIG. 5 can be used to control the
fracturing system 10 shown in FIG. 1. Specifically, the method
shown in FIG. 5 can account for a large number of fracturing
completion and reservoir parameters that are controlled in
completing multiple wellbores simultaneously. This is advantageous
as a human would otherwise be incapable of accounting for such a
large number of fracturing parameters to control a multi wellbore
completion, thereby potentially leading to problems during the
completion, e.g. screen outs.
[0064] In the method shown in FIG. 5, public data, client data,
internal data, and well survey data can be gathered at
corresponding steps 502, 504, 505, and 508. Public data can include
applicable public data related to subterranean formations and/or
hydrocarbon extractions. For example, public data can include
wellbore scenario information that operators provide to the public,
e.g. have to file with government agencies and public interest
groups. Public data can be retrieved from an applicable public
wellbore data storage system, such as RS Energy Group.RTM..
[0065] Client data can include applicable client data related to
subterranean formations, fracture completions, and/or hydrocarbon
extractions. For example, client data can include geologic data of
a fracture job site of an oil and gas production company. In
another example, client data can include all or a portion of a
target completion of the fracture completion job. For example,
client data can specify that an oil and gas production company
wants fractures that extend into a specific hydrocarbon reservoir
at horizontal intervals of every 20 feet.
[0066] Internal data can include applicable data related to
subterranean formations, fracture completions, and/or hydrocarbon
extractions that is maintained by an entity responsible for
performing the fracture completion job. For example, internal data
can include geological data at well sites of past fracture
completion jobs. Internal data can be specific to a client of an
entity responsible for performing the fracture completion job.
Further, internal data can be maintained across a plurality of
different clients by an entity responsible for performing the
fracture completion job.
[0067] Well survey data can include applicable data, e.g.
geographical data, related to wells at a site of the current
fracture completion jobs. For example, well survey data can include
a physical profile of a wellbore at the current fracture completion
job. Well survey data can be maintained by a customer, e.g. an oil
and gas production company, or an entity responsible for performing
the fracture completion job.
[0068] At step 510 in the flow 500 shown in FIG. 5, a fracturing
completion model is applied to identify a plurality of possible
fracturing completion plans for performing the fracture completion
job. Specifically, the fracturing completion model can be applied
to identify a plurality of possible fracturing completion plans for
performing the fracture completion job at a target completion, e.g.
a target completion of a client/customer. The possible fracturing
completion plans identified through the fracturing completion model
can have varying values of fracturing completion and reservoir
parameters, e.g. uncertainty parameters for the fracturing
completion model. Specifically, the fracturing completion model can
vary different fracturing completion and reservoir parameters in
order to identify a plurality of possible fracturing completion
plans for achieving the target completion. For example, the
possible fracturing completion plans can have varying proppant t-e
ramp characteristics to achieve the target completion. In another
example, the possible fracturing completion plans can have varying
fluid pumping times during fracturing stages to achieve the target
completion.
[0069] Further, in identifying the plurality of possible fracturing
completion plans, the fracturing completion model can also identify
subsurface fracture simulations corresponding to each of the
plurality of possible fracturing completion plans. A subsurface
fracture simulation can include a simulated representation of
fracture creation and stabilization/possible outcomes in one or
more wellbores when a specific fracturing completion plan is
performed. Specifically, the fracturing completion model can use a
possible fracturing completion plan, as structured input, to
generate a corresponding subsurface fracture simulation, as
structured output. The fracturing completion model can generate
subsurface fracture simulations for each of the identified possible
fracturing completion and reservoir parameters. Subsequently and as
will be discussed in greater detail later, the subsurface fracture
simulations can be used to select a fracturing completion plan from
the plurality of possible fracturing completion plans.
[0070] The fracturing completion model can be an applicable
geomechanical fracture simulator for identifying possible
fracturing completion plans by varying values of fracturing
completion and reservoir parameters. More specifically, the
fracture completion model can be an applicable geomechanical
fracture simulator for identifying subsurface fracture simulations
for a possible fracturing completion plans. For example,
GOHFER.RTM. can be used to identify a plurality of possible
fracturing completion plans and generate corresponding subsurface
fracture simulations for the different possible fracturing
completion plans. Further, the fracturing completion model can
identify possible fracturing completion plans and corresponding
subsurface fracture simulations using one or a combination of the
public data, the client data, the internal data, and the well
survey data gathered for the fracture completion job at
corresponding steps 502, 504, 505, and 508.
[0071] The fracturing completion model can simulate fracturing
completion plans to identify subsurface fracture simulations using
a subsurface fracture grid. Specifically, a subsurface fracture
grid can be a simulated physical grid within a subterranean
formation of the fracturing completion job. More specifically, a
subsurface fracture grid can be a simulated physical grid of the
subterranean formation that physically quantifies fractures that
are formed according to different fracturing completion plans. In
turn, the fracturing completion model can use the subsurface
fracture grid to simulate the possible fracturing completion plans
and generate subsurface fracture simulations.
[0072] At step 512 in the flow 500 of the example method shown in
FIG. 5, the possible fracturing completion plans and the
corresponding subsurface fracture simulations are provided to a
fracturing decision engine 617. The fracturing decision engine 617
can use the possible fracturing completion plans and the
corresponding subsurface fracture simulations to identify a
fracturing completion plan to apply in performing the fracture
completion job. More specifically, the fracturing decision engine
617 can select a fracturing completion plan to apply in achieving
the target completion based on the possible fracturing completion
plans and the corresponding subsurface fracture simulations.
[0073] The fracturing decision engine 617 can select the fracturing
completion plan from the plurality of possible fracturing
completion plans through machine learning and/or artificial
intelligence. The fracturing decision engine 617 can use an
applicable machine learning and/or artificial intelligence
technique, e.g. one or more completion plan selection model(s), to
select the fracturing completion plan from the plurality of
possible fracturing completion plans. Specifically, the fracturing
decision engine 617 can use machine learning and/or artificial
intelligence, e.g. a completion plan selection model, to select the
fracturing completion plan for achieving the target completion in
the one or more wellbores during the fracture completion job. For
example, the fracturing decision engine 617 can use machine
learning and/or artificial intelligence to select the fracturing
completion plan from the possible fracturing completion plans based
on a predicted accuracy of the fracturing completion plan in
achieving the target completion. Further in the example, the
fracturing decision engine 617 can use artificial intelligence and
machine learning to predict accuracies of each of the possible
fracturing completion plans in achieving the target completion,
e.g. based on similarities between the target completion and
subsurface fracture simulations of the fracturing completion plans.
As follows, the fracturing decision engine 617 can select a
possible fracturing completion plan that is predicted to most
accurately achieve the target completion.
[0074] The selected fracturing completion plan can be an initial
fracturing completion plan. Specifically, the selected fracturing
completion plan can be the first fracturing completion plan that is
implemented to start the fracture completion job. The initial
fracturing completion plan can be selected based on a predicted
accuracy of the fracturing completion plan in achieving the target
completion, e.g. based on similarities between the subsurface
fracture simulation of the fracturing completion plan and the
target completion. The initial fracturing completion plan can be
selected without completion characteristic data of the fracture
completion job, e.g. before fracture completion operations are
actually carried out to generate the completion characteristic
data.
[0075] Further, the fracturing completion plan can be a
new/replacement fracturing completion plan that can be implemented
to replace a current fracturing completion plan in performing the
fracture completion job. Specifically, at step 518, the fracturing
decision engine 617 can select the new fracturing completion plan
while the current fracturing completion plan is performed. As
follows, the fracturing completion job can be performed according
to the new fracturing completion plan, effectively switching
fracturing completion plans. The new fracturing completion plan can
be selected and implemented in an attempt to more accurately
achieve the target completion. For example, the current fracturing
completion plan can be causing screen outs during the fracture
completion job leading to failed realization of the target
completion. As follows, the new fracturing completion plan can be
selected and implemented to reduce screen out occurrences and more
closely realize the target completion.
[0076] The fracturing decision engine 617 can determine whether to
select the new fracturing completion plan from the plurality of
possible fracturing completion plans through application of machine
learning and/or artificial intelligence, e.g. through application
of a completion plan selection model. The fracturing decision
engine 617 can determine whether to select a new completion plan
and subsequently select the new fracturing completion plan based on
performance characteristics of the currently implemented fracturing
completion plan. Specifically, the fracturing decision engine 617
can determine deficiencies of the current fracturing completion
plan from completion characteristics data, including surface
diagnostics data gathered at step 514 and subsurface diagnostics
data gathered at step 515, for the current fracturing completion
plan. As follows, the fracturing decision engine 617 can determine
to switch to a new fracturing completion plan and subsequently
select the new fracturing completion plan based on the completion
characteristics data for the current fracturing completion plan.
For example, if subsurface diagnostics data indicates that a screen
out is occurring, then the fracturing decision engine 617 can
select a new fracturing completion plan, e.g. a plan that adds a
viscosifier, to reduce the chances of screen out occurrence.
[0077] The fracturing decision engine 617 can apply machine
learning and/or artificial intelligence to the completion
characteristics data to select a fracturing completion plan, e.g.
an initial fracturing completion plan or a replacement fracturing
completion plan, from the plurality of possible fracturing
completion plans. Specifically, the fracturing decision engine 617
can apply a completion plan selection model that is trained through
artificial intelligence and machine learning to the surface
diagnostics data and the subsurface diagnostics data to select a
fracturing completion plan from the plurality of possible
fracturing completion plans. For example, the fracturing decision
engine 617 can apply a completion plan selection model to the
completion characteristics data to recognize deficiencies in the
current fracturing completion plan. As follows, the fracturing
decision engine 617 can use the completion plan selection model to
select the new fracturing completion plan while accounting for the
large number of fracturing completion and reservoir parameters that
form the possible fracturing completion plans.
[0078] A completion plan selection model can map events, both
unfavorable events and favorable events, occurring in a fracture
completion to values of fracturing completion and reservoir
parameters, e.g. values of parameters that form a fracture
completion plan. Further, a completion plan selection model can map
events, both unfavorable events and favorable events, occurring in
a fracture completion to completion characteristics data, e.g.
either or both subsurface and surface diagnostics data. For
example, the fracturing decision engine 617 can recognize an
occurrence of a runaway fracture by applying machine learning
and/or artificial intelligence to subsurface pressures included in
subsurface diagnostics data. Further in the example, the fracturing
decision engine 617 can apply the completion plan selection model
to diagnose that a diverter material should to be added during a
fracturing stage to account for runaway fractures. As follows, the
fracturing decision engine 617 can select a new fracturing
completion plan that adds the diverter material during the
fracturing stage based on application of the completion plan
selection model.
[0079] Using machine learning and/or artificial intelligence to
select a fracturing completion plane, e.g. a new fracturing
completion plan, is advantageous as a human is typically unable to
timely analyze the wealth of completion characteristic data.
Specifically, a human is typically unable to timely analyze the
wealth of completion characteristics data to determine whether to
apply a new fracturing completion plan. Further, using machine
learning and/or artificial intelligence to select a new fracturing
completion plan is advantageous as a human is typically unable to
analyze the large number of fracturing completion parameters and/or
reservoir parameters for selecting the new fracturing completion
plan. These advantages are further realized when fracturing is
performed on multiple wellbores and potentially simultaneously on
the multiple wellbores. Specifically, fracturing on multiple
wellbores simultaneously can increase the number of fracturing
completion parameters and/or reservoir parameters that need to be
accounted for and the complexity of the fracturing completion
parameters and the reservoir parameters that should be accounted
for in selecting a fracturing completion plan, e.g. a new
fracturing completion plan. Applying machine learning and/or
artificial intelligence can insure that the numerous and complex
fracturing completion and reservoir parameters present in a
multi-wellbore fracturing job are properly accounted for in
selecting a new fracturing completion plan.
[0080] Once a fracturing completion plan is selected from the
plurality of possible fracturing completion plans, then the method
can include facilitating performance of the fracture completion job
according to the selected fracturing completion plan. In
facilitating implementation of the selected fracturing completion
plan, one or more alerts, actionable alerts, can be presented to an
operator for implementing the fracture completion job. For example,
an alert can be presented that instructs an operator to increase a
concentration of proppant. Further, in facilitating implementation
of the selected fracturing completion plan, a fracturing system
used in performing the fracturing job can be controlled to
implement the fracturing completion plan. Specifically,
instructions for implementing the selected fracturing completion
plan to the fracturing system and the fracturing system can
autonomously control itself according to the instructions to
implement the selected fracturing completion plan.
[0081] In facilitating performance of the fracture completion job
according to the selected fracturing completion plan, the method
can include facilitating switching to the new fracturing completion
plan for completing the one or more wellbores. Specifically, alerts
for implementing, or otherwise switching to the new fracturing
completion plan, can be presented to an operator. Subsequently, the
operator can use the alerts to control a fracturing system
according to the new fracturing completion plan. Further,
instructions for implementing the new fracturing completion plan
can be provided to the fracturing system. The fracturing system can
then autonomously control itself to operate according to the
instructions and implement the new fracturing completion plan.
[0082] Either or both surface diagnostics data and subsurface
diagnostics data, e.g. gathered at steps 514 and 515 can be used to
train/retrain the fracturing completion model applied at step 510.
In turn, the trained/retrained fracturing completion model can be
used to generate a plurality of possible fracturing completion
plans. Specifically, the trained/retrained fracturing completion
model can be used to generate corresponding subsurface fracturing
simulations for each of the possible fracturing completion plans.
As follows, the plurality of possible fracturing completion plans
and corresponding subsurface fracturing simulations can be
analyzed, e.g. based on machine learning, to select a fracture
completion plan, e.g. an initial fracturing completion plan or a
changed fracturing completion plan, to implement in performing a
fracture completion job. The trained/retrained fracturing
completion model can be applied to one or a plurality of different
fracturing completion jobs from the fracturing completion job that
is the subject of the flow 500 shown in FIG. 5.
[0083] Further, either or both surface diagnostics data and
subsurface diagnostics data, e.g. gathered at steps 514 and 515 can
be used to train/retrain the completion plan selection model
applied at step 518. In turn, the trained/retrained completion plan
selection model can be used to select a new fracture completion
plan from the plurality of fracture completion plans for the
fracturing completion job. The trained/retrained completion plan
selection model can be applied to one or a plurality of different
fracturing completion jobs from the fracturing completion job that
is the subject of the flow 500 shown in FIG. 5.
[0084] The fracturing completion model and/or the completion plan
selection model can be trained/retrained using parameters of the
fracturing completion plans, e.g. values of varying fracturing
completion parameters and/or reservoir parameters, which are
applied to formulate the different fracturing completion plans.
Specifically, values of parameters of the applied fracturing
completion plans can be correlated with the completion
characteristics data based on times that the applied fracturing
completion plans are implemented and times that the completion
characteristics data is generated. This can ensure that the
completion characteristics data is accurately associated with
values of fracturing completion and reservoir parameters of
completion plans that were used to generate the completion
characteristics data. As follows, the fracturing completion model
and/or the completion plan selection model can be trained/retrained
with the completion characteristics data and corresponding values
of the fracturing completion and reservoir parameters used in
generating the completion characteristics data.
[0085] Further, the fracturing completion model and/or the
completion plan selection model can be trained/retrained based on
specific events occurring during the fracturing completion job.
Specifically, an occurrence of an event can be correlated with
values of fracturing completion and reservoir parameters at the
time the event occurred. Subsequently, the fracturing completion
model and/or the completion plan selection model can be
trained/retrained based on the values of the fracturing completion
and reservoir parameters correlated with the specific event. For
example, a runaway fracture can be detected during the fracturing
completion job. Further in the example, the values of fracturing
completion and reservoir parameters that caused the runway fracture
can be correlated with the runaway fracture. Specifically, a flow
rate of proppant slurry and fluid that led to the runaway fracture
can be correlated with an occurrence of the runaway fracture. In
turn, the fracturing completion model and/or the completion plan
selection model can be trained/retrained based on the values of the
fracturing completion and reservoir parameters that led to the
runaway fracture.
[0086] FIGS. 6A-6C show example diagrams of a fracturing system 600
for controlling a fracturing completion job based on wellbore
interference. Fracturing system 600 as shown in FIG. 6A can be
implemented using the systems, methods, and techniques described
herein. In particular, the disclosed system, methods, and
techniques may directly or indirectly affect one or more components
or pieces of equipment associated with the example fracturing
system 600, according to one or more embodiments. In some
implementations, fracturing system 600 can include an artificial
intelligence agent 610, an advisor 620/controller 622, environment
parameters 630, sensors 640, and models 650. All or an applicable
combination of these components of the fracturing system 600 can
reside at the surface at a well site/fracturing pad where a well 60
is located.
[0087] During a fracturing job, fracturing system 600 can include
performing a fracture completion job in real time by facilitating
control of hydraulic fracturing based on set point data 611 as
described herein along with a fracture completion model 650. As
further described herein, fracturing system 600 can utilize
fracture completion model 650 to facilitate control of hydraulic
fracturing as described herein and interpret data received from
public data, set point inputs 611, internal fracturing data 612,
customer fracturing data 613, fracturing shape 614, pressure data
615, fracturing growth 616, and sensors 640. Furthermore, as will
be discussed in greater detail below, fracturing system 600 can
account for a large number of fracturing completion and reservoir
parameters, which a human would otherwise be incapable of
accounting for, in performing a fracture completion job. In turn,
this can lead to a more accurate and efficient completion of one or
more wellbores as part of a fracture completion job.
[0088] In some implementations, fracturing system 600 as shown in
FIG. 6A can account for a large number of fracturing completion,
set point data 611, and reservoir parameters (e.g., fracturing
shape 614, fracturing growth 616, and data from sensors 640) that
can be controlled in completing multiple wellbores simultaneously
or individually. This is advantageous as a human would otherwise be
incapable of accounting for such a large number of fracturing
parameters (e.g., fracturing shape 614, fracturing growth 616, and
data from sensors 640) to control a multi-wellbore completion,
thereby potentially leading to problems during the completion
(e.g., screen outs).
[0089] In some instances, public data, internal data 612, customer
fracturing data 613, and well survey data (e.g., set point data
611, fracturing shape 614, pressure data 615, and fracturing growth
616) can be gathered and received by artificial intelligence agent
610 to implement fracturing operations. Public data can include
applicable public data related to subterranean formations and/or
hydrocarbon extractions. For example, public data can include
wellbore scenario information that operators provide to the public
(e.g., having to file with government agencies and public interest
groups). Public data can be retrieved from an applicable public
wellbore data storage system, such as RS Energy Group.RTM.. In some
instances, public data can be received via communication interfaces
(e.g., wired and/or wireless) for communicating public data to
other devices such as artificial intelligence agent 610, advisor
620, controller 622, and/or any other device.
[0090] Set point data 611 can include applicable data related to
cluster efficiency, wellbore interference 619, and complexity of
the overall distribution of fracturing activity. Wellbore
interference 619 can include data related to interference that may
be encountered by neighboring wellbores. For example, if one
wellbore pumps an excess amount of proppant into a subterranean
cavity, the excess proppant may flow into a designated area of a
neighboring wellbore, thereby affecting the efficiency of the
neighboring wellbore.
[0091] Wellbore interference 619 can occur when fractures between
two wells interfere in the same volume of reservoir or rock
formation. When the fractures interfere with one another, there may
be an increased probability that the two wells may drain the same
unit volume of reservoir. Some wellbore interference 619 can be
beneficial to ensure that no resources are left behind. However,
too much wellbore interference 619 can lead to over expenditure of
capital to recover resources in the rock formation.
[0092] In some implementations, wellbore interference 619 can be
measured with fiber optic cables as described herein in, on, or
around an offset well. Fracturing system 600 can utilize a fiber
optic cable (e.g., a distributed acoustic sensing fiber optic
cable) to measure and receive data relating to the wellbore,
fluids, subterranean formation, etc. For example, data that can be
received by the fiber optic cable can include distributed strain
(e.g., ultra-low frequency acoustic data that can include data
relating to fractures near the offset well that may not be seen
from the reservoir), distributed temperature (e.g., temperature
data of fractures that may be nearby), and acoustic data that can
be utilized to measure micro-seismicity in a far field between two
wells.
[0093] In some instances, fracturing system 600 can further include
utilizing pressure gauges that are installed along the offset well.
For example, the pressure gauges can be installed on the surface or
along the subsurface of the offset well. In some instances,
pressure gauges installed on the surface can be configured to read
the pressure of fluid inside the wellbore. Pressure gauges can also
be located in the subsurface and be configured to read pressures on
the outside or inside of the pipe/casing. Readings of pressure
changes inside the pipe can also determine changes that result in
strain being placed on the pipe due to approaching and intersecting
fractures. For pressure gauges measuring pressure inside the pipe,
the location of the strain may not be known. On the outside of the
pipe, there may be pressures associated with local strain, but also
pressures associated with direct fluid communication between the
fracture and the pressure gauge. Fracturing system 600 can include
observing and modeling the response from multiple pressure gauges
in a well can provide information relating to fracture growth. The
pressure gauges can be configured to measure connectivity of fluid
throughout the subterranean reservoir or fractures. For example,
fluids may be connected through a fracture system in the
subterranean reservoir, and may not be formed by fracturing system
600, but may be present from an existing fracturing system.
[0094] Artificial intelligence agent 610 of fracturing system 600
can control wellbore interference 619 by adjusting fluid flow rate,
proppant concentration (e.g., density of the fluid), total volume
of fluid (e.g., slurry) that can be injected into a designated
fracture or subterranean fracture systems, diverters that can
redirect fluid travel path within wells and fractures, rheology
(e.g., to adjust the shape of the fractures, thereby affecting
total length of the fracture), and cluster efficiency (e.g., the
number of clusters of perforations per treatment can reduce the
length of the fractures).
[0095] In some implementations, artificial intelligence 610 of
fracturing system 600 can adjust objective functions including
wellbore interference parameters 619 to control against unwanted
variations such as detrimental wellbore interference 619. For
example, wellbore interference 619 can be adjusted by artificial
intelligence agent 610 of fracturing system 600 to reduce strain
observed at an offset well. In some instances, artificial
intelligence agent 610 can receive data relating to wellbore
interference 619 to determine strain data. The strain data can then
be utilized to determine or estimate how far away a fracture may be
(e.g., proximity of fracture) and control (e.g., select an
appropriate completion plan) the amount of strain induced at the
offset well. The strain data can also be utilized to determine the
amount of volume associated with an approaching fracture to control
and minimize the amount of strain induced at the offset well. In
other instances, artificial intelligence agent 610 can receive data
relating to wellbore interference 619 to determine and
reduce/affect pressure at the offset well. For example, pressure
due to wellbore interference can be estimated to determine how much
fluid has entered a drainage boundary of an offset well. In another
instance, artificial intelligence agent 610 can receive data
relating to wellbore interference 619 to determine and adjust
objective functions to control against fracture velocity (e.g.,
growth rate of a fracture) utilizing micro seismic processes and
sensors.
[0096] Data related to wellbore interference 619 can further
include information relating to neighboring wells that can affect
the target well of fracturing system 600. For example, a treatment
well can pump fluids down a casing of a wellbore. The casing can
include different clusters or different fractures propagating away
from different clusters. The subterranean rock formation
surrounding the casing or wellbore can begin to break (e.g.,
fracture) in a primary direction along an axis. Thereafter, the
fractures can also break in a secondary direction, which can be
measured by a stress sensor as described herein in different
directions. In some instances, fracture surface area may be
desired, which can include extending beyond the fractures and
providing better access to different parts of the subterranean
reservoir. However, such an extension can cause wellbore
interference 619, which can include an offset well interfering over
a period of time or from a relative distance. Subsequently, the
offset well can encroach on a treatment well and negatively affect
its performance. For example, a well may be previously drilled and
completed.
[0097] Thereafter, a treatment well can be drilled next to the
offset well to drain the subterranean rock formation volume. As the
offset well and the treatment well interact with one another, each
well begins to interfere with the other well's performance metrics,
thereby causing wellbore interference 619. In some instances,
artificial intelligence agent 610 of fracturing system 600 can
optimize completion plans to combat wellbore interference 619. For
example, a fracture from the offset well can "directly hit" the
treatment well, which can cause fluctuations in fluid consumption
by both wells.
[0098] Data relating to wellbore interference 619 of fracturing
system 600 can not only occur from offset wells, but also from
natural fractures in the subterranean rock formation. For example,
subterranean rock formations include a complex formation for
various rock beds, and such rock beds can include weaknesses around
the bedding planes (e.g., natural fractures). Fractures are
constantly changing and tend to follow fault planes and planes of
weakness of the subterranean rock formation. Moreover, as fault
planes are not always parallel (e.g., fault planes can be
perpendicular or angular to one another), branching can occur,
thereby causing wellbore interference 619.
[0099] In other implementations, fracturing system 600 can
determine a direction of a fracture of a subterranean formation
with sensors as described herein as micro-seismic measurements.
Thereafter, fracturing system 600 can determine a height, width,
and length of the fracture by utilizing sensors of fracturing
system 600 to determine a projected geometry of the fracture. Based
on the characteristics of the fractures, artificial intelligence
610 of fracturing system 600 can determine which completion plan to
select to adjust parameters and objective functions to minimize
wellbore interference 619. Determining the quantity, size,
direction, speed, etc. of fractures in a particular subterranean
rock formation can provide fracturing system 600 with necessary
data to adjust controls and objective functions, selecting
appropriate completion plans, and/or updating completion plans. For
example, as one well is being completed, fracturing system 600 can
determine or receive instructions to stop the fracturing process at
a certain point. The surrounding area of the reservoir where
fractures are to be generated can extend into a fracturing area of
another offset well. By doing so, wellbore interference 619 can
include the anticipated region of interference that will be
fractured through the offset well area or drained by another
neighboring well.
[0100] If the fractures of the treatment well grow into the
neighboring subterranean area of the offset well, the treatment
well can have its own fractures and drainage area as well as
wellbore inference 619 from neighboring wells. For example,
wellbore interference 619 can include data relating to pumping and
draining interference caused by neighboring offset wells that are
anticipated due to proximity or draining parameters of the offset
well. In some instances, treatment well and offset well may be
extracting resources from the same drainage area. In such
instances, the overall productivity of both wells may diminish as
both wells are extracting resources from the same drainage area. In
response, artificial intelligence agent 610 of fracturing system
600 may select a completion plan that adjusts objective functions
to counter conflicting drainage from surrounding offset wells
(e.g., wellbore interference 619).
[0101] In some instances, wellbore interference 619 can include
data relating to fluctuations in pressure at the treatment well,
clusters of perforations, corresponding fractures, etc. to
determine whether there is interference from a neighboring offset
well. For example, during the fracturing or completion process,
fracturing system 600 can generate a pressure curve that
illustrates a rate of depletion or change in production. If the
pressure curve decreases at a relative rate, then the drainage
process is proceeding accordingly. However, if there is a sudden
increase rate of depletion or change in slope in production,
artificial intelligence agent 610 can determine that sufficient
drainage is being inhibited by interference from a neighboring
offset well. While some wellbore interference 619 may be beneficial
as it may ensure that all of the resources in the subterranean rock
formation is completely removed, sudden spikes or shifts in
drainage may cause the fracturing system 600 to drain the
subterranean rock formation too quickly or inefficiently. Pressure
measurements can also include magnitude and fluid connectivity
(e.g., pre-post) of the pressure measured at the wellbore. The
magnitude and fluid connectivity of pressure may fluctuate based on
wellbore interference 619 from neighboring wellbores, which
artificial intelligence agent 610 may utilize to determine which
completion plan to select.
[0102] Data relating to wellbore interference 619 from offset wells
can be measured with sensors, distributed acoustic sensing fiber
optic cables, or any other device suitable for the intended purpose
and understood by a person of ordinary skill in the art. For
example, distributed acoustic sensing fiber optic cables can
measure distributed strain, distributed temperature, and/or
microseismic data throughout the treatment well. The distributed
acoustic sensing fiber optic cables can be positioned within, on,
or around the treatment well temporality or permanently. In some
instances, strain can be measured as a distributed strain with
ultra-low frequency acoustic and microseismic data received from
the distributed acoustic sensing fiber optic cable. Based on
measured distributed strain and distributed temperature data,
artificial intelligence agent 610 can determine wellbore
interference 619 and adjust completion plans or parameters of
fracturing system 600 accordingly. In other instances, distributed
acoustic sensing fiber optic cables can be utilized to determine
flow distribution, minimum stress (e.g., depletion of resources),
pressure, flow rate, concentration, etc.
[0103] In some implementations, fracturing system 600 can include
and utilize pressure gauges distributed throughout a well and/or
surrounding subterranean rock formation, along with a fiber optic
cable, at a surface and a subsurface to measure maximum fluid
throughput in the reservoir. The information received from the
pressure gauges and fiber optic cable can be wellbore interference
619 that can be utilized by artificial intelligence agent 610 to
adjust objective functions and/or completion plans. As such, data
relating to temperature, pressure, and strain can be utilized by
artificial intelligence agent 610 to determine and counter wellbore
interference 619 by adjusting objective functions and/or completion
plans accordingly.
[0104] An example includes an old wellbore with perforations that
has been in production for a while, fracturing system 600 can
utilize or add pressure gauges at the well to determine drainage
parameters associated with the old wellbore. When a fracture hits
the fracture system of the old wellbore, pressure changes will be
experienced by the pressure gauges in the old wellbore. Thereafter,
a pressure gauge can be provided downhole at the surface and
fracturing system 600 can receive corresponding responses from the
pressure gauge. With the information received from the downhole
pressure gauge, artificial intelligence agent 610 can determine
whether there is wellbore interference 619 and adjust objective
functions and/or completion plans accordingly. In some instances, a
new well can include fiber optic cables and pressure gauges
distributed accordingly along the length of the wellbore. As such,
if a fracture grows over here and hits the new wellbore, fracturing
system 600 can measure the strain. If a cool fluid passes by the
new wellbore, fracturing system 600 can measure the temperature of
the cool fluid. Micro-seismicity can further be measured and may be
when there are fractures occurring or breaking the slippage in the
subterranean rock formation as the fracture is propagating through
the reservoir. Micro-seismicity can be measured with acoustic fiber
optic cables and corresponding models can be generated for the
micro-seismicity events in a 3D space, while strain and temperature
can be measured with corresponding strain and temperature gauges
and/or fiber optic cables as described herein. In some examples,
strain can be continuously measured at a well when it is expected
to be affected by fractures of a neighboring well. Strain can be
utilized to determine how far away the fracture is (e.g.,
proximity) to adjust objective functions accordingly. From the data
related to strain, fracturing system 600 can determine the volume
of an approaching fracture to minimize its effect on the well.
Artificial intelligence agent 610 can then receive this information
to determine wellbore interference 619 to adjust objective
functions and/or completion plans accordingly.
[0105] Additionally, subsurface diagnostics data relating to
wellbore interference 619 can be gathered at the fracturing
completion job. Subsurface diagnostics data can describe applicable
subsurface events or changes occurring during the fracture
completion job. For example, subsurface diagnostics data including
data relating to wellbore interference 619 can describe temperature
of stages in the wellbore, microseismic activity in the wellbore,
acoustic measurements in the wellbore, strain measurements in the
wellbore, bottom hole pressure in the wellbore, and instantaneous
shut in pressures in the wellbore. Subsurface diagnostics data
relating to wellbore interference 619 can further be gathered
through applicable sensors, equipment, and subsurface monitoring
techniques associated with hydraulic fracturing. For example,
subsurface diagnostics data relating to wellbore interference 619
at the fracturing completion job can be gathered using one or more
fiber optic cables, e.g. fiber optic cables integrated with one or
more wellbores of the fracturing completion job. In another
example, subsurface diagnostics data relating to wellbore
interference 619 at the fracturing completion job can be gathered
by one or more acoustic sensors, e.g. acoustic sensors integrated
with one or more wellbores of the fracturing completion job. In yet
another example, subsurface diagnostics data relating to wellbore
interference 619 at the fracturing completion job can be gathered
by one or more strain sensors, e.g. strain sensors integrated with
one or more wellbores of the fracturing completion job. In another
example, subsurface diagnostics data relating to wellbore
interference 619 can be gathered by systems and equipment that
measure casing strain and/or well deformation in a wellbore.
[0106] The fracturing system 600 or an operator can identify an
action based on either or both the surface diagnostics data and the
subsurface diagnostics data relating to wellbore interference 619.
For example, the fracturing system 600 or the operator can identify
the action based on the surface diagnostics data and the subsurface
diagnostics data relating to wellbore interference 619 and the
knowledge and experience of the operator. For example, the Examiner
can adjust a flow pressure at which fluid is pumped into a wellbore
based on an observed distributed pressure or temperature, as
indicated by the subsurface diagnostics data relating to wellbore
interference 619, and the operator's own knowledge of an ideal
pressure and temperature for the type of formation being
fractured.
[0107] In some implementations, fracturing system 600 can include
the ability to weight across different surface variables. For
example, the surface variables can control various parameters such
as rate, proppant concentration, rheology, and diverters. In other
instances, fracturing system 600 can include the ability to weight
against various objective functions. For example, the objective
functions can include cluster efficiency, wellbore interference
619, complexity, and fiscals.
[0108] In some instances, artificial intelligence agent 610 of
fracturing system 600 can include determining which surface
variables to control based on input data 611 including data
relating to wellbore interference 619 that can provide effective
concentrations and rheology of the fluid. Artificial intelligence
agent 610 can train and retrain models that select completion plans
based on various objective functions including providing efficient
cluster perforation flow. Artificial intelligence agent 610 of
fracturing system 600 can also weigh different objective functions
including increasing wellbore interference 619 when determining
which completion plans to utilize in the fracturing system 600.
[0109] Referring to FIG. 6B, an example graph 670 of wellbore
interference 619 for a corresponding well is shown. Graph 670
includes a vertical axis 672 that can represent distributed
acoustic sensing fluid distribution in oil barrel per foot (Bbl/ft)
and a horizontal axis 674 that can represent stage lengths and/or
positions, which can utilized by artificial intelligence agent 610
to balance objective functions such as cluster efficiency, wellbore
interference 619, and cost effectiveness. Graph 670 can further
represent stage volume (Bbl/ft) that may vary depending on boundary
effects (e.g., wellbore interference 619). Stage volume can include
out-target (Bbl) stage volume 676 and in-target (Bbl) stage volume
678. As shown in FIG. 6B, out-target stage volume 676 and in-target
stage volume 678 fluctuate and vary with each stage. For example,
stage 40 of FIG. 6B can include an in-target stage volume value of
approximately 62 Bbl/ft and an out-target stage volume value of
approximately 10 Bbl/ft. At another stage of FIG. 6B, stage 253 can
include an in-target stage volume value of approximately 10 Bbl/ft
and an out-target stage volume value of approximately 0 Bbl/ft.
FIG. 6B also includes cluster efficiency 680 in view of stages 674.
Cluster efficiency 680 may be an average value of cluster
efficiency at each stage 674 or may be the cluster efficiency of
the clusters of each stage 674. FIG. 6B illustrates a cluster
efficiency 680 value of 63% for the corresponding well.
Furthermore, FIG. 6B illustrates a wellbore interference 682
percentage of 6% for the corresponding well. In some instances,
customers and/or users may select a particular rate of oil barrel
per foot (e.g., 33 Bbl/ft), for a corresponding number of clusters
(e.g., 400 clusters), and spacing between each cluster of the
plurality of clusters (e.g., 25 feet spacing between each
cluster/stage).
[0110] FIG. 6C illustrates a map view of a fracture 684 in view of
an offset well 686 and a treatment well 688, in accordance with
various aspects of the subject technology. Fracture 684 can be an
interpolated map of micro-seismic density that can be updated
throughout the completion job. Fracture 684 can further include an
outer ring 690 that can be a projected fracture length. The
projected fracture length can be calculated with the following
equation:
Fracture length (ft)=Fracture growth rate (ft/bbl).times.Remaining
volume (bbl).
[0111] Outer ring 690 of fracture 684 may be asymmetric for a
respective growth rate on each tip. This may move with time based
on the corresponding projection. At offset well 686, a bubble 692
can emerge and can be represented in a color scale (e.g., red to
yellow to green) to represent location and proximity of wellbore
interference 619, which may be calculated from measured distributed
acoustic sensing strain values. Prior fracturing treatments may
also be represented by "dull shading 694. Lines 696 may also be
included to represent perforation center and fracture azimuth
values. As such, FIG. 6C can represent the effect of wellbore
interference 619 from neighboring wells that can provide valuable
information to artificial intelligence agent 610 to adjust
objective functions and/or completion plans accordingly.
[0112] Cluster efficiency can include data related to individual
perforations, clusters of perforations, overall performance of
wellbores, and any other data related to cluster efficiency that is
suitable for the intended purpose and understood by a person of
ordinary skill in the art. Complexity can include data related to
the effects of cluster efficiency, wellbore interference 619, and
any other data suitable for the intended purpose and understood by
a person of ordinary skill in the art. In some instances, set point
data 611 can be received via communication interfaces (e.g., wired
and/or wireless) for communicating set point data 611 to other
devices such as artificial intelligence agent 610, advisor 620,
controller 622, and/or any other device.
[0113] Internal data 612 can include applicable data related to
subterranean formations, fracture completions, and/or hydrocarbon
extractions that is measured and/or maintained by an entity
responsible for performing the fracture completion job. For
example, internal data 612 can include geological data at well
sites of past fracture completion jobs. In some instances, internal
data 612 can be specific to a client of an entity responsible for
performing the fracture completion job. Furthermore, internal data
612 can be maintained across a plurality of different clients by an
entity responsible for performing the fracture completion job. In
some instances, internal data 612 can be received via communication
interfaces (e.g., wired and/or wireless) for communicating internal
data 612 to other devices such as artificial intelligence agent
610, advisor 620, controller 622, and/or any other device.
[0114] Client/customer fracturing data 613 can include applicable
client data related to subterranean formations, fracture
completions, and/or hydrocarbon extractions. For example, customer
fracturing data 613 can include geologic data of a fracture job
site of an oil and gas production company. In another example,
customer fracturing data 613 can include all or a portion of a
target completion of the fracture completion job. Customer
fracturing data 613 can also indicate that an oil and gas
production company desires fractures that can extend into a
specific hydrocarbon reservoir at horizontal intervals of every 20
feet. In some instances, customer fracturing data 613 can further
include geological and geophysical (G&G) data that can be
utilized by artificial intelligence agent 610 in fracturing
operations. In some instances, customer fracturing data 613 can be
received via communication interfaces (e.g., wired and/or wireless)
for communicating customer fracturing data 613 to other devices
such as artificial intelligence agent 610, advisor 620, controller
622, and/or any other device.
[0115] Well survey data can include applicable data (e.g.,
geographical data including fracturing shape 614, pressure data
615, and fracturing growth 616), related to wells at a site of the
current fracture completion jobs. For example, well survey data can
include a physical profile of a wellbore at the current fracture
completion job. Well survey data can further be maintained by a
customer (e.g., an oil and gas production company) or an entity
responsible for performing the fracture completion job. Examples of
fracturing shape 614 include length, height, width, and azimuth.
Pressure data 615 can include data driven proxy models that can
provide fracture information. Fracturing growth 616 can include
data relating to perforation flow, fracturing velocity, asymmetry
of the fracture, complexity of the fracturing job, and any other
fracturing growth data suitable for the intended purpose and
understood by a person of ordinary skill in the art. In some
instances, well survey data can be received via communication
interfaces (e.g., wired and/or wireless) for communicating well
survey data to other devices such as artificial intelligence agent
610, advisor 620, controller 622, and/or any other device.
[0116] Pressure data 615 can include proxy-type models that can be
generated to evaluate different options and actions, which is
faster and more efficient than having to generate and re-run full
models. The proxy-type models can also be generated based on data
received from a treatment well (e.g., treatment well 712 of FIG. 7)
and pressure data received from offset monitor wells (e.g., offset
wells 710 of FIG. 7). The proxy-type models can also be utilized to
determine a more probable fracture response based on data collected
by fracturing system 600.
[0117] In some implementations, fracturing completion models 650
can be applied to interpret gathered diagnostic data by the sensors
640. For example, the fracturing completion models 650 can be
applied to transform the diagnostics data into a form that is more
readily interpretable by the AI agent 610. Further, the fracturing
completion models 650 can be applied to identify a plurality of
possible fracturing completion plans for performing the fracture
completion job. For example, fracturing completion model 650 can be
applied to identify a plurality of possible fracturing completion
plans for performing the fracture completion job at a target
completion (e.g., a target completion of a client/customer). The
possible fracturing completion plans identified through fracturing
completion model 650 can have varying values of fracturing
completion and reservoir parameters (e.g., uncertainty parameters
for the fracturing completion model). For example, fracturing
completion model 650 can vary different fracturing completion and
reservoir parameters in order to identify a plurality of possible
fracturing completion plans for achieving the target completion. In
some instances, the possible fracturing completion plans utilized
by fracturing completion model 650 can have varying proppant ramp
characteristics to achieve the target completion. In another
instance, the possible fracturing completion plans can have varying
fluid pumping times during fracturing stages to achieve the target
completion.
[0118] Fracturing system 600 can further include identifying the
plurality of possible fracturing completion plans based on data
related to the wellbore and the surrounding subterranean area. In
some instances, fracturing completion model 650 can also identify
subsurface fracture simulations corresponding to each of the
plurality of possible fracturing completion plans. A subsurface
fracture simulation (e.g., by artificial intelligence agent 610)
can include a simulated representation of fracture creation and
stabilization/possible outcomes in one or more wellbores when a
specific fracturing completion plan is performed. For example,
fracturing completion model 650 can utilize a possible fracturing
completion plan, as structured input, to generate a corresponding
subsurface fracture simulation, as structured output. Fracturing
completion model 650 can also generate subsurface fracture
simulations for each of the identified possible fracturing
completion and reservoir parameters. Subsequently, and as discussed
herein, the subsurface fracture simulations generated by artificial
intelligence agent 610 can be utilized to select fracturing
completion models 650 from the plurality of possible fracturing
completion plans.
[0119] Fracturing completion model 650 can also be an applicable
geomechanical fracture simulator for identifying possible
fracturing completion plans by varying values of fracturing
completion and reservoir parameters. For example, fracturing
completion model 650 can be an applicable geomechanical fracture
simulator for identifying subsurface fracture simulations for a
possible fracturing completion plans. In some instances,
subterranean simulation tools such as GOHFER.RTM. can be utilized
to identify a plurality of possible fracturing completion plans and
generate corresponding subsurface fracture simulations for the
different possible fracturing completion plans. Furthermore,
fracturing completion model 650 can identify possible fracturing
completion plans and corresponding subsurface fracture simulations
using one or a combination of the public data, set point data 611,
customer fracturing data 613, internal fracturing data 612, and the
well survey data (e.g., fracturing shape 614, pressure data 615,
and fracturing growth data 616) gathered for the fracture
completion job.
[0120] Fracturing completion model 650 of fracturing system 600 can
further include utilizing subterranean data such as that received
from a distributed acoustic sensing (DAS) fiber optic cable 652,
and interpreting low frequency pressure data 654 and high frequency
pressure data 656. Examples of data received from a DAS fiber optic
cable 652 can include flow rate, microseismic monitoring (MSM), and
strain of the fracturing process. In some implementations,
interpretations from low frequency pressure data 654 can include
determining and/or utilizing a minimal amount of stress experienced
by fracturing system 600, flow area, and pore volume (e.g., of a
reservoir characteristic). In other instances, interpretations from
high frequency pressure data 656 can include determining and/or
utilizing RLC (e.g., high frequency analysis), a wavelet analysis
technique, Fourier transforms, moving references, and
compressional/primary wave (p-wave) velocity.
[0121] Fracturing completion model 650 of fracturing system 600 can
further simulate fracturing completion plans to identify subsurface
fracture simulations using a subsurface fracture grid. For example,
a subsurface fracture grid can be a simulated physical grid within
a subterranean formation of the fracturing completion job. In some
instances, a subsurface fracture grid can be a simulated physical
grid of the subterranean formation that physically quantifies
fractures that are formed according to different fracturing
completion plans. In turn, fracturing completion model 650 can
utilize the subsurface fracture grid to simulate the possible
fracturing completion plans and generate subsurface fracture
simulations.
[0122] Fracturing system 600 can further include utilizing advisor
620 and controller 622 upon selection of a fracturing completion
plan by artificial intelligence agent 610. In some instances,
advisor 620 of fracturing system 600 can be an open loop where the
fracturing completion plan selected by artificial intelligence
agent 610 is outputted 660 to a fracturing site or operator. For
example, once a fracturing completion plan is selected by
artificial intelligence agent 610, the selected fracturing
completion plan can be directly outputted 660 to the fracturing
site or operator along with environment data 630 and unknown
disturbances 632. Fracturing data 624 (e.g., flow rate,
concentration, rheology, and diverter information) can further be
outputted 660 along with the fracturing completion plan selected by
artificial intelligence agent 610.
[0123] In other instances, controller 622 of fracturing system 600
can be a closed loop where the fracturing system 600 further
receives fracturing data 624 (e.g., flow rate, concentration,
rheology, and diverter information) that can then be received by
artificial intelligence agent 610 to determine whether to maintain
or change the selected fracturing completion plan. For example,
once a fracturing completion plan is selected by artificial
intelligence agent 610, artificial intelligence agent 610 can
subsequently receive fracturing data 624 in real time and update or
maintain the fracturing completion plan outputted 660 to fracturing
system 600. Fracturing data 624 can further be outputted 660 along
with the fracturing completion plan selected by artificial
intelligence agent 610. In some implementations, artificial
intelligence agent 610 can continuously receive and consider
fracturing data 624 received by controller 622 to determine how to
proceed with fracturing completion plans utilized by fracturing
system 600.
[0124] In some implementations, the possible fracturing completion
plans and the corresponding subsurface fracture simulations of
fracturing system 600 can be provided to a fracturing decision
engine 617. In some instances, fracturing decision engine 617 can
include artificial intelligence agent 610. The fracturing decision
engine 617 can use the possible fracturing completion plans and the
corresponding subsurface fracture simulations to identify a
fracturing completion plan to apply in performing the fracture
completion job by fracturing system 600. For example, the
fracturing decision engine 617 can select a fracturing completion
plan to apply in achieving the target completion based on the
possible fracturing completion plans and the corresponding
subsurface fracture simulations.
[0125] The fracturing decision engine 617 can select the fracturing
completion plan from the plurality of possible fracturing
completion plans through machine learning and/or artificial
intelligence (e.g., artificial intelligence agent 610). The
fracturing decision engine 617 can use an applicable machine
learning and/or artificial intelligence technique, e.g., one or
more completion plan selection model(s) based on the
above-mentioned fracturing data and parameters, to select the
fracturing completion plan from the plurality of possible
fracturing completion plans. For example, artificial intelligence
agent 610 can use machine learning and/or artificial intelligence,
e.g., a completion plan selection model, to select the fracturing
completion plan for achieving the target completion in the one or
more wellbores during the fracture completion job. In some
instances, artificial intelligence agent 610 can use machine
learning and/or artificial intelligence to select the fracturing
completion plan from the possible fracturing completion plans based
on a predicted accuracy of the fracturing completion plan in
achieving the target completion and measured data and parameters of
the fracturing activity. Furthermore, in the example, artificial
intelligence agent 610 can use artificial intelligence and machine
learning to predict accuracies of each of the possible fracturing
completion plans in achieving the target completion, e.g., based on
similarities between the target completion and subsurface fracture
simulations of the fracturing completion plans. As described
herein, artificial intelligence agent 610 can select a possible
fracturing completion plan that can be predicted to most accurately
achieve the target completion.
[0126] In some implementations, the selected fracturing completion
plan can be an initial fracturing completion plan. For example, the
selected fracturing completion plan can be the first fracturing
completion plan that is implemented to start the fracture
completion job, which may be selected by artificial intelligence
agent 610. The initial fracturing completion plan can be selected
based on a predicted accuracy of the fracturing completion plan in
achieving the target completion, e.g., based on similarities
between the subsurface fracture simulation of the fracturing
completion plan and the target completion. The initial fracturing
completion plan can also be selected by artificial intelligence
agent 610 without completion characteristic data of the fracture
completion job, e.g., before fracture completion operations are
actually carried out to generate the completion characteristic
data.
[0127] Furthermore, the fracturing completion plan of fracturing
system 600 can be a new/replacement fracturing completion plan that
can be implemented to replace a current fracturing completion plan
in performing the fracture completion job. For example, artificial
intelligence agent 610 can select the new fracturing completion
plan while the current fracturing completion plan is performed. As
described herein, the fracturing completion job can be performed by
fracturing system 600 according to the new fracturing completion
plan, effectively switching fracturing completion plans based on
past and/or current data measured from sensors 640 or provided by
set point data 611, internal fracturing data 612, customer
fracturing data 613, fracturing shape data 614, fracturing pressure
data 615, and fracturing growth data 616. The new fracturing
completion plan can be selected and implemented by artificial
intelligence agent 610 to accurately achieve the target completion.
For example, the current fracturing completion plan can be causing
screen outs during the fracture completion job leading to failed
realization of the target completion. As described herein, the new
fracturing completion plan can be selected and implemented by
artificial intelligence agent 610 to reduce screen out occurrences
and more closely realize the target completion.
[0128] In other instances, artificial intelligence agent 610 of
fracturing system 600 can determine whether to select the new
fracturing completion plan from the plurality of possible
fracturing completion plans through application of machine learning
and/or artificial intelligence, e.g., through application of a
completion plan selection model. For example, artificial
intelligence agent 610 can determine whether to select a new
completion plan and subsequently select the new fracturing
completion plan based on performance characteristics of the
currently implemented fracturing completion plan. In some
instances, artificial intelligence agent 610 can determine
deficiencies of the current fracturing completion plan from
completion characteristics data, including surface and subsurface
diagnostics data (e.g., including past, current, and/or updated set
point data 611, internal fracturing data 612, customer fracturing
data 613, fracturing shape data 614, fracturing pressure data 615,
and fracturing growth data 616, environment data 630, and sensor
data 640), for the current fracturing completion plan. Environment
data 630 can include unknown disturbances 632 detected at
environment 630 such as at the wellbore or surrounding subterranean
region. As described herein, artificial intelligence agent 610 can
determine to switch to a new fracturing completion plan and
subsequently select the new fracturing completion plan based on the
completion characteristics data for the current fracturing
completion plan. For example, if subsurface diagnostics data
indicates that a screen out is occurring, then artificial
intelligence agent 610 of fracturing system 600 can select a new
fracturing completion plan, e.g., a plan that adds a viscosifying
chemical to reduce the chances of screen out occurrence.
[0129] In other instances, artificial intelligence agent 610 can
apply machine learning and/or artificial intelligence to the
completion characteristics data to select a fracturing completion
plan, e.g., an initial fracturing completion plan or a replacement
fracturing completion plan, from the plurality of possible
fracturing completion plans. For example, artificial intelligence
agent 610 can apply a completion plan selection model that is
trained and/or retrained through artificial intelligence and/or
machine learning based on the surface diagnostics data and the
subsurface diagnostics data (e.g., including past, current, and/or
updated set point data 611, internal fracturing data 612, customer
fracturing data 613, fracturing shape data 614, fracturing pressure
data 615, and fracturing growth data 616, environment data 630, and
sensor data 640) to select a fracturing completion plan from the
plurality of possible fracturing completion plans. For example,
artificial intelligence agent 610 can apply a completion plan
selection model to the completion characteristics data to recognize
deficiencies in the current fracturing completion plan. As
described herein, artificial intelligence agent 610 can also use
the completion plan selection model to select the new fracturing
completion plan while accounting for the large number of fracturing
completion and reservoir parameters that form the possible
fracturing completion plans.
[0130] A completion plan selection model can map events, both
unfavorable events and favorable events (e.g., including
environment data 630 and unknown disturbances 632), occurring in a
fracture completion to values of fracturing completion and
reservoir parameters, e.g., values of parameters that form a
fracture completion plan. Furthermore, a completion plan selection
model can map events, both unfavorable events and favorable events,
occurring in a fracture completion to completion characteristics
data, e.g., either or both subsurface and surface diagnostics data.
For example, artificial intelligence agent 610 can recognize an
occurrence of a runaway fracture by applying machine learning
and/or artificial intelligence to subsurface pressures included in
subsurface diagnostics data. Furthermore, in the example,
artificial intelligence agent 610 can apply the completion plan
selection model to diagnose that a diverter material should to be
added during a fracturing stage to account for runaway fractures.
As described herein, artificial intelligence agent 610 can select a
new fracturing completion plan that adds diverter material during
the fracturing stage based on application of the completion plan
selection model.
[0131] Using machine learning and/or artificial intelligence to
select a fracturing completion plane, e.g., a new fracturing
completion plan, is advantageous as a human is unable to timely
analyze the wealth of completion characteristic data (e.g.,
including past, current, and/or updated set point data 611,
internal fracturing data 612, customer fracturing data 613,
fracturing shape data 614, fracturing pressure data 615, and
fracturing growth data 616, environment data 630, and sensor data
640). For example, a human is unable to timely analyze the wealth
of completion characteristics data to determine whether to apply a
new fracturing completion plan. Furthermore, using machine learning
and/or artificial intelligence to select a new fracturing
completion plan is advantageous as a human is unable to analyze the
large number of fracturing completion parameters and/or reservoir
parameters for selecting the new fracturing completion plan.
[0132] These advantages are further realized when fracturing is
performed on multiple wellbores and potentially simultaneously on
the multiple wellbores. For example, fracturing on multiple
wellbores simultaneously can increase the number of fracturing
completion parameters and/or reservoir parameters that need to be
accounted for and the complexity of the fracturing completion
parameters and the reservoir parameters that should be accounted
for in selecting a fracturing completion plan, e.g., a new
fracturing completion plan. Applying machine learning and/or
artificial intelligence by artificial intelligence agent 610 can
ensure that the numerous and complex fracturing completion and
reservoir parameters present in a multi-wellbore fracturing job are
properly accounted for in selecting a new fracturing completion
plan by fracturing system 600.
[0133] In some implementations, once a fracturing completion plan
is selected by fracturing system 600 from the plurality of possible
fracturing completion plans, fracturing system 600 can include
facilitating performance of a fracture completion job according to
a selected fracturing completion plan. In facilitating
implementation of the selected fracturing completion plan, one or
more alerts, e.g., actionable alerts, can be presented to an
operator for implementing the fracture completion job. For example,
an alert can be presented that instructs an operator to increase a
concentration of proppant. Furthermore, in facilitating
implementation of the selected fracturing completion plan,
fracturing system 600 can perform the fracturing job to implement
the fracturing completion plan. In some instances, fracturing
system 600 can provide instructions for implementing the selected
fracturing completion plan and autonomously controlling the
instructions to implement the selected fracturing completion
plan.
[0134] In facilitating performance of the fracture completion job
according to the selected fracturing completion plan, fracturing
system 600 can include facilitating switching to the new fracturing
completion plan for completing the one or more wellbores. For
example, alerts for implementing, or otherwise switching to the new
fracturing completion plan, can be presented to an operator.
Subsequently, the operator can use the alerts to control fracturing
system 600 according to the new fracturing completion plan.
Furthermore, fracturing system 600 can include instructions for
implementing the new fracturing completion plan can be provided to
the fracturing system 600. Fracturing system 600 can also then
autonomously control itself to operate according to the
instructions and implement the new fracturing completion plan.
[0135] Either or both surface diagnostics data and subsurface
diagnostics data (e.g., including past, current, and/or updated set
point data 611, internal fracturing data 612, customer fracturing
data 613, fracturing shape data 614, fracturing pressure data 615,
and fracturing growth data 616, environment data 630, and sensor
data 640) can be utilized to train/retrain the fracturing
completion model by fracturing system 600. In turn, the
trained/retrained fracturing completion model can be used to
generate a plurality of possible fracturing completion plans. For
example, the trained/retrained fracturing completion model can be
used to generate corresponding subsurface fracturing simulations
for each of the possible fracturing completion plans. As described
herein, the plurality of possible fracturing completion plans and
corresponding subsurface fracturing simulations can be analyzed
(e.g., based on artificial intelligence agent 610) to select a
fracture completion plan (e.g., an initial fracturing completion
plan or a changed fracturing completion plan), to implement in
performing a fracture completion job. The trained/retrained
fracturing completion model can be applied to one or a plurality of
different fracturing completion jobs from the fracturing completion
job by fracturing system 600.
[0136] Furthermore, either or both surface diagnostics data and
subsurface diagnostics data (e.g., including past, current, and/or
updated set point data 611, internal fracturing data 612, customer
fracturing data 613, fracturing shape data 614, fracturing pressure
data 615, and fracturing growth data 616, environment data 630, and
sensor data 640) can be used to train/retrain the completion plan
selection model by fracturing system 600. In turn, the
trained/retrained completion plan selection model can be used to
select a new fracture completion plan from the plurality of
fracture completion plans for the fracturing completion job. The
trained/retrained completion plan selection model can be applied to
one or a plurality of different fracturing completion jobs from the
fracturing completion job by fracturing system 600.
[0137] In some implementations, the fracturing completion model
and/or the completion plan selection model can be trained/retrained
using parameters of the fracturing completion plans (e.g., values
of varying fracturing completion parameters and/or reservoir
parameters), which can be applied to formulate the different
fracturing completion plans. For example, values of parameters of
the applied fracturing completion plans can be correlated with the
completion characteristics data based on times that the applied
fracturing completion plans are implemented and times that the
completion characteristics data are generated. This can ensure that
the completion characteristics data is accurately associated with
values of fracturing completion and reservoir parameters of
completion plans that were used to generate the completion
characteristics data. As described herein, the fracturing
completion model and/or the completion plan selection model of
fracturing system 600 can be trained/retrained with the completion
characteristics data and corresponding values of the fracturing
completion and reservoir parameters used in generating the
completion characteristics data.
[0138] Furthermore, the fracturing completion model and/or the
completion plan selection model of fracturing system 600 can be
trained/retrained based on specific events occurring during the
fracturing completion job. For example, an occurrence of an event
can be correlated with values of fracturing completion and
reservoir parameters at the time the event occurred. Subsequently,
the fracturing completion model and/or the completion plan
selection model of fracturing system 600 can be trained/retrained
based on the values of the fracturing completion and reservoir
parameters correlated with the specific event. In some instances, a
runaway fracture can be detected during the fracturing completion
job. Furthermore, in the example, the values of fracturing
completion and reservoir parameters that caused the runway fracture
can be correlated with the runaway fracture. For example, a flow
rate of proppant slurry and fluid that led to the runaway fracture
can be correlated with an occurrence of the runaway fracture. In
turn, the fracturing completion model and/or the completion plan
selection model of fracturing system 600 can be trained/retrained
based on the values of the fracturing completion and reservoir
parameters that led to the runaway fracture.
[0139] While the description has made reference to performing
fracturing jobs as part of well completion activities by fracturing
system 600, the techniques and systems described herein can be
applied to any applicable situation where a fracturing job is
performed. Specifically, the techniques and systems for performing
a fracturing job, as described herein, can be applied to perform
well workover activities. For example, the techniques and systems
described herein can be applied in well workover activities to
change a completion based on changing hydrocarbon reservoir
conditions. In another example, the techniques and systems
described herein can be applied in well workover activities to pull
and replace a defective completion.
[0140] FIG. 7 shows an example fracturing operation 700 utilizing
set point data such as data related to cluster efficiency 720,
wellbore interference 722, and complexity 724 of a wellbore site
(e.g., the surrounding subterranean region between neighboring
wellbores 710, 712). Fracturing operation 700 can utilize
fracturing system 600 and its corresponding components, elements,
and structure, as described herein. For example, cluster efficiency
720 of fracturing operation 700 is similar to cluster efficiency of
set points 611 of fracturing system 600. Wellbore interference 722
of fracturing operation 700 is similar to wellbore interference 619
of set points 611 of fracturing system 600. Complexity 724 of
fracturing operation 700 is similar to complexity of set points 611
of fracturing system 600.
[0141] Fracturing operation 700 can include sensors 730 that are
distributed throughout subterranean formation 734 surrounding the
offset well 710 and the treatment well 712. Sensors 730 can further
be distributed within an interior region or adjacent to the offset
well 710 and/or the treatment well 712. Sensors 730 can further
gather data from subterranean formation 734 that can then be
utilized by fracturing operation 700 and fracturing system 600 to
determine which fracturing completion model to select and
utilize.
[0142] In some implementations, cluster efficiency 720 may be
illustrated by arrows 732 as shown in FIG. 7. The length of arrows
732 may represent the efficiency of perforation clusters of
fracturing operation 700. For example, longer arrows 732 can
represent higher cluster efficiency 720, while shorter arrows 732
can represent lower cluster efficiency 720. As described herein, as
wellbores 710, 712 undergo fracturing procedures, circumstances may
arise where perforation clusters become clogged or inoperable. In
such cases, where actionable, inefficient/inoperable perforation
clusters are discontinued or operations are conducted to liberate
the clogged perforations/perforation clusters.
[0143] Fracturing operation 700 further illustrates interactions
between offset well 710 and treatment well 712. For example, as
treatment well 712 injects proppant into subterranean formation
734, the injected proppant can interfere with proppant injected by
offset well 710. The interference potentially created between
offset well 710 and treatment well 712 can deteriorate efficiency
and performance of one or both wellbores 710, 712.
[0144] In some instances, the combination of cluster efficiency 720
and wellbore interference 722 along with the subterranean formation
734 attributes to the complexity 724 of fracturing operation 700.
The totality of the circumstances within wellbores 710, 712 (e.g.,
cluster efficiency 720) and surrounding wellbores 710, 712 (e.g.,
wellbore interference 722) establishes variations in complexity 724
of fracturing operation 700 that attribute to selection of
fracturing completion models by artificial intelligence agent 610
of fracturing system 600 as shown in FIG. 6A.
[0145] FIG. 8 shows an example fracturing operation 800 with
multiple wellbores 810, 812 and respective fracturing interactions
between the multiple wellbores 810, 812. Multiple wellbores 810,
812 of fracturing operation 800 are similar to wellbores 710, 712
of fracturing operation 700 as described herein. In some instances,
well survey data from wellbores 810, 812 can be received via
communication interfaces (e.g., wired and/or wireless 820, 822) for
communicating data from wellbores 810, 812 to other devices such as
artificial intelligence agent 610, advisor 620, controller 622,
fracturing site 802, and/or any other device as described in
fracturing system 600 and as shown in FIG. 6A. In other instances,
communication interface 822 can provide information received from
pressure gauges distributed at or throughout wellbore 812, while
communication interface 820 can provide information received from a
fiber optic cable (e.g., distributed acoustic sensing fiber optic
cable) installed within, around, or on wellbore 810.
[0146] Fracturing operation 800 of FIG. 8 further illustrates zones
of perforation clusters 814, 816 of wellbores 810, 812,
respectively. The zones of perforation clusters 814, 816 illustrate
a general region of the subterranean formation that proppant is
expected to flow from wellbores 810, 812. As described herein, as
zones of perforation clusters 814 of wellbore 810 draw closer to
zones of perforation 816 of wellbore 812, interference can be
experienced by wellbores 810, 812, thereby deteriorating the
quality of fracturing operation of wellbores 810, 812 of fracturing
operation 800. FIG. 8 further illustrates fracturing lines 818 of
wellbores 810 throughout the subterranean formation.
[0147] Having disclosed some example system components and
concepts, the disclosure now turns to FIG. 9, which illustrate
example method 900 for controlling a fracturing completion job. The
steps outlined herein are exemplary and can be implemented in any
combination thereof, including combinations that exclude, add, or
modify certain steps.
[0148] At step 902, the method 900 can include receiving
diagnostics data of a hydraulic fracturing completion of a
wellbore. The diagnostics data received at step 902 can include
applicable diagnostics data gathered during the wellbore
completion. Specifically, the diagnostics data received at step 902
can include applicable subsurface data gathered during the wellbore
completion.
[0149] At step 904, the method 900 can include accessing a fracture
formation model that models formation characteristics of fractures
formed through the wellbore into a formation surrounding the
wellbore during the hydraulic fracturing completion with respect to
surface variables of the hydraulic fracturing completion. The
formation characteristics can include either or both subsurface
fracture geometry of the fractures during formation and growth
behavior of the fractures during formation.
[0150] Surface variables of the hydraulic fracturing completion
that are modeled by the fracture formation model can include
applicable variables of the hydraulic fracturing completion that
can be modified at the surface of the wellbore. For example,
surface variables can include a pressure of a fluid or slurry
pumped into the wellbore during the fracturing completion. Further,
surface variables of the hydraulic fracturing completion can
include variables that are modified at the surface and effectively
implemented at the subsurface, e.g. at or through the wellbore.
Specifically, surface variables of the hydraulic fracturing
completion can include characteristics of a perforation plan that
can be modified or otherwise selected at the surface. In turn, the
perforation plan can be implemented at the subsurface based on the
characteristics of the perforation plan selected at the surface.
For example, the fracture formation model can model a number of
perforations clusters selected as part of a perforation plan at the
surface, which can then be implemented to form a selected number of
perforation clusters subsurface.
[0151] At step 906, the method 900 can include selecting one or
more subsurface objective functions from a plurality of subsurface
objective functions for changing one or more of the formation
characteristics of the fractures, the one or more subsurface
objective functions including an objective function for wellbore
interference, the objective function for wellbore interference
including interference information from a neighboring wellbore. A
subsurface objective function is a real-valued function with a
value that is associated with one or more applicable subsurface
characteristics of the hydraulic fracturing completion.
Specifically, a subsurface object function can include a
real-valued function of one or more characteristics of either or
both fracture growth behaviors and fracture geometries during the
hydraulic fracturing completion. The value of the real-valued
function can either be maximized or minimized over a set of values
of one or more applicable surface variables associated with the
hydraulic fracturing completion.
[0152] A value of a subsurface objective function can include
actual subsurface attributes associated with subsurface
characteristics. For example, a subsurface objective function can
include an objective function for fracture complexity, an objective
function for cluster efficiency, or an objective function for well
interference. Further, a value of a subsurface objective function
can include actual surface attributes associated with subsurface
characteristics. For example, a subsurface objective function can
include an objective function for one or more surface costs
associated with changing one or more of the formation
characteristics of the fractures during the hydraulic fracturing
completion.
[0153] In some instances, the selecting of the one or more
subsurface objective functions can include assigning varying
weights to each of the one or more subsurface objective functions.
Specifically, the selecting of the one or more subsurface objective
functions can include assigning a weight to at least one of the one
or more subsurface objective functions that is greater than a
corresponding weight assigned to each other subsurface objective
function of the plurality of subsurface objective functions. For
example, when minimizing well interference during the hydraulic
fracturing completion, an objective function for well interference
can be weighted greater than objective functions for cluster
efficiency and fracture complexity.
[0154] At step 908, the method 900 can include applying the
fracture formation model based on the diagnostics data to determine
values of the surface variables for controlling the formation
characteristics of the fractures to converge on the one or more
subsurface objective functions.
[0155] In some instances, the method 900 can further include
forming a completion plan for performing the hydraulic fracturing
completion of the wellbore based on the values of the surface
variables. The method 900 can also include facilitating
implementation of the completion plan in performing the hydraulic
fracturing completion of the wellbore. The completion plan can be a
modified completion plan formed by modifying a previous completion
plan for the hydraulic fracturing completion based on the values of
one or more of the surface variables.
[0156] In other instances, the method 900 can further include
measuring the interference information from the neighboring
wellbore with a distributed acoustic sensing fiber optic cable at
the wellbore. The interference information from the neighboring
wellbore can include data relating to at least one of pressure,
temperature, and strain measured at the wellbore.
[0157] In another instance, the method 900 can further include
receiving pressure data from pressure gauges at the neighboring
wellbore to determine pressure changes at the neighboring wellbore.
The method 900 can further include measuring micro-seismic data at
the wellbore to determine projected geometry of the fractures. The
projected geometry of the fractures can include at least one of
length, width, height, and velocity of the fractures.
[0158] In some implementations, the interference information from
the neighboring wellbore can include drainage data measured at the
wellbore, the drainage data including a change in drainage rate
based on interference from the neighboring wellbore. The objective
function for wellbore interference can be determined for each stage
of a plurality of stages of the wellbore. The method 900 can
further include determining which stages of the plurality of stages
of the wellbore are out-target and in-target to determine if the
neighboring wellbore is interfering with the wellbore.
[0159] In other instances, the method 900 can further include
modifying the surface variables based on the fracture formation
model applied according to the diagnostics data to converge on the
one or more subsurface objective functions including the objective
function for wellbore interference. For example, each of the
plurality of subsurface objective functions can include a
corresponding weight assigned to the plurality of subsurface
objective functions including a weight assigned to the objective
function for wellbore interference, the modifying of the surface
variables being further based on the corresponding weight assigned
to the plurality of subsurface objective functions.
[0160] In other implementations, the method 900 can further include
forming a completion plan for performing the hydraulic fracturing
completion of the wellbore based on the values of the surface
variables, the completion plan being a modified completion plan
formed by modifying a previous completion plan for the hydraulic
fracturing completion based on the values of one or more of the
surface variables.
[0161] FIG. 10 illustrates an example computing device architecture
1000 which can be employed to perform various steps, methods, and
techniques disclosed herein. The various implementations will be
apparent to those of ordinary skill in the art when practicing the
present technology. Persons of ordinary skill in the art will also
readily appreciate that other system implementations or examples
are possible.
[0162] As noted above, FIG. 10 illustrates an example computing
device architecture 1000 of a computing device which can implement
the various technologies and techniques described herein. The
components of the computing device architecture 1000 are shown in
electrical communication with each other using a connection 1005,
such as a bus. The example computing device architecture 1000
includes a processing unit (CPU or processor) 1010 and a computing
device connection 1005 that couples various computing device
components including the computing device memory 1015, such as read
only memory (ROM) 1020 and random access memory (RAM) 1025, to the
processor 1010.
[0163] The computing device architecture 1000 can include a cache
of high-speed memory connected directly with, in close proximity
to, or integrated as part of the processor 1010. The computing
device architecture 1000 can copy data from the memory 1015 and/or
the storage device 1030 to the cache 1012 for quick access by the
processor 1010. In this way, the cache can provide a performance
boost that avoids processor 1010 delays while waiting for data.
These and other modules can control or be configured to control the
processor 1010 to perform various actions. Other computing device
memory 1015 may be available for use as well. The memory 1015 can
include multiple different types of memory with different
performance characteristics. The processor 1010 can include any
general purpose processor and a hardware or software service, such
as service 1 1032, service 2 1034, and service 3 1036 stored in
storage device 1030, configured to control the processor 1010 as
well as a special-purpose processor where software instructions are
incorporated into the processor design. The processor 1010 may be a
self-contained system, containing multiple cores or processors, a
bus, memory controller, cache, etc. A multi-core processor may be
symmetric or asymmetric.
[0164] To enable user interaction with the computing device
architecture 1000, an input device 1045 can represent any number of
input mechanisms, such as a microphone for speech, a
touch-sensitive screen for gesture or graal input, keyboard, mouse,
motion input, speech and so forth. An output device 1035 can also
be one or more of a number of output mechanisms known to those of
skill in the art, such as a display, projector, television, speaker
device, etc. In some instances, multimodal computing devices can
enable a user to provide multiple types of input to communicate
with the computing device architecture 1000. The communications
interface 1040 can generally govern and manage the user input and
computing device output. There is no restriction on operating on
any particular hardware arrangement and therefore the basic
features here may easily be substituted for improved hardware or
firmware arrangements as they are developed.
[0165] Storage device 1030 is a non-volatile memory and can be a
hard disk or other types of computer readable media which can store
data that are accessible by a computer, such as magnetic cassettes,
flash memory cards, solid state memory devices, digital versatile
disks, cartridges, random access memories (RAMs) 1025, read only
memory (ROM) 1020, and hybrids thereof. The storage device 1030 can
include services 1032, 1034, 1036 for controlling the processor
1010. Other hardware or software modules are contemplated. The
storage device 1030 can be connected to the computing device
connection 1005. In one aspect, a hardware module that performs a
particular function can include the software component stored in a
computer-readable medium in connection with the necessary hardware
components, such as the processor 1010, connection 1005, output
device 1035, and so forth, to carry out the function.
[0166] For clarity of explanation, in some instances the present
technology may be presented as including individual functional
blocks including functional blocks comprising devices, device
components, steps or routines in a method embodied in software, or
combinations of hardware and software.
[0167] In some embodiments the computer-readable storage devices,
mediums, and memories can include a cable or wireless signal
containing a bit stream and the like. However, when mentioned,
non-transitory computer-readable storage media expressly exclude
media such as energy, carrier signals, electromagnetic waves, and
signals per se.
[0168] Methods according to the above-described examples can be
implemented using computer-executable instructions that are stored
or otherwise available from computer readable media. Such
instructions can include, for example, instructions and data which
cause or otherwise configure a general purpose computer, special
purpose computer, or a processing device to perform a certain
function or group of functions. Portions of computer resources used
can be accessible over a network. The computer executable
instructions may be, for example, binaries, intermediate format
instructions such as assembly language, firmware, source code, etc.
Examples of computer-readable media that may be used to store
instructions, information used, and/or information created during
methods according to described examples include magnetic or optical
disks, flash memory, USB devices provided with non-volatile memory,
networked storage devices, and so on.
[0169] Devices implementing methods according to these disclosures
can include hardware, firmware and/or software, and can take any of
a variety of form factors. Typical examples of such form factors
include laptops, smart phones, small form factor personal
computers, personal digital assistants, rackmount devices,
standalone devices, and so on. Functionality described herein also
can be embodied in peripherals or add-in cards. Such functionality
can also be implemented on a circuit board among different chips or
different processes executing in a single device, by way of further
example.
[0170] The instructions, media for conveying such instructions,
computing resources for executing them, and other structures for
supporting such computing resources are example means for providing
the functions described in the disclosure.
[0171] In the foregoing description, aspects of the application are
described with reference to specific embodiments thereof, but those
skilled in the art will recognize that the application is not
limited thereto. Thus, while illustrative embodiments of the
application have been described in detail herein, it is to be
understood that the disclosed concepts may be otherwise variously
embodied and employed, and that the appended claims are intended to
be construed to include such variations, except as limited by the
prior art. Various features and aspects of the above-described
subject matter may be used individually or jointly. Further,
embodiments can be utilized in any number of environments and
applications beyond those described herein without departing from
the broader spirit and scope of the specification. The
specification and drawings are, accordingly, to be regarded as
illustrative rather than restrictive. For the purposes of
illustration, methods were described in a particular order. It
should be appreciated that in alternate embodiments, the methods
may be performed in a different order than that described.
[0172] Where components are described as being "configured to"
perform certain operations, such configuration can be accomplished,
for example, by designing electronic circuits or other hardware to
perform the operation, by programming programmable electronic
circuits (e.g., microprocessors, or other suitable electronic
circuits) to perform the operation, or any combination thereof.
[0173] The various illustrative logical blocks, modules, circuits,
and algorithm steps described in connection with the examples
disclosed herein may be implemented as electronic hardware,
computer software, firmware, or combinations thereof. To clearly
illustrate this interchangeability of hardware and software,
various illustrative components, blocks, modules, circuits, and
steps have been described above generally in terms of their
functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present application.
[0174] The techniques described herein may also be implemented in
electronic hardware, computer software, firmware, or any
combination thereof. Such techniques may be implemented in any of a
variety of devices such as general purposes computers, wireless
communication device handsets, or integrated circuit devices having
multiple uses including application in wireless communication
device handsets and other devices. Any features described as
modules or components may be implemented together in an integrated
logic device or separately as discrete but interoperable logic
devices. If implemented in software, the techniques may be realized
at least in part by a computer-readable data storage medium
comprising program code including instructions that, when executed,
performs one or more of the method, algorithms, and/or operations
described above. The computer-readable data storage medium may form
part of a computer program product, which may include packaging
materials.
[0175] The computer-readable medium may include memory or data
storage media, such as random access memory (RAM) such as
synchronous dynamic random access memory (SDRAM), read-only memory
(ROM), non-volatile random access memory (NVRAM), electrically
erasable programmable read-only memory (EEPROM), FLASH memory,
magnetic or optical data storage media, and the like. The
techniques additionally, or alternatively, may be realized at least
in part by a computer-readable communication medium that carries or
communicates program code in the form of instructions or data
structures and that can be accessed, read, and/or executed by a
computer, such as propagated signals or waves.
[0176] Other embodiments of the disclosure may be practiced in
network computing environments with many types of computer system
configurations, including personal computers, hand-held devices,
multi-processor systems, microprocessor-based or programmable
consumer electronics, network PCs, minicomputers, mainframe
computers, and the like. Embodiments may also be practiced in
distributed computing environments where tasks are performed by
local and remote processing devices that are linked (either by
hardwired links, wireless links, or by a combination thereof)
through a communications network. In a distributed computing
environment, program modules may be located in both local and
remote memory storage devices.
[0177] In the above description, terms such as "upper," "upward,"
"lower," "downward," "above," "below," "downhole," "uphole,"
"longitudinal," "lateral," and the like, as used herein, shall mean
in relation to the bottom or furthest extent of the surrounding
wellbore even though the wellbore or portions of it may be deviated
or horizontal. Correspondingly, the transverse, axial, lateral,
longitudinal, radial, etc., orientations shall mean orientations
relative to the orientation of the wellbore or tool. Additionally,
the illustrate embodiments are illustrated such that the
orientation is such that the right-hand side is downhole compared
to the left-hand side.
[0178] The term "coupled" is defined as connected, whether directly
or indirectly through intervening components, and is not
necessarily limited to physical connections. The connection can be
such that the objects are permanently connected or releasably
connected. The term "outside" refers to a region that is beyond the
outermost confines of a physical object. The term "inside"
indicates that at least a portion of a region is partially
contained within a boundary formed by the object. The term
"substantially" is defined to be essentially conforming to the
particular dimension, shape or another word that substantially
modifies, such that the component need not be exact. For example,
substantially cylindrical means that the object resembles a
cylinder, but can have one or more deviations from a true
cylinder.
[0179] The term "radially" means substantially in a direction along
a radius of the object, or having a directional component in a
direction along a radius of the object, even if the object is not
exactly circular or cylindrical. The term "axially" means
substantially along a direction of the axis of the object. If not
specified, the term axially is such that it refers to the longer
axis of the object.
[0180] Although a variety of information was used to explain
aspects within the scope of the appended claims, no limitation of
the claims should be implied based on particular features or
arrangements, as one of ordinary skill would be able to derive a
wide variety of implementations. Further and although some subject
matter may have been described in language specific to structural
features and/or method steps, it is to be understood that the
subject matter defined in the appended claims is not necessarily
limited to these described features or acts. Such functionality can
be distributed differently or performed in components other than
those identified herein. The described features and steps are
disclosed as possible components of systems and methods within the
scope of the appended claims.
[0181] Moreover, claim language reciting "at least one of" a set
indicates that one member of the set or multiple members of the set
satisfy the claim. For example, claim language reciting "at least
one of A and B" means A, B, or A and B.
[0182] Statements of the disclosure include:
[0183] Statement 1: A method comprising: receiving diagnostics data
of a hydraulic fracturing completion of a wellbore; accessing a
fracture formation model that models formation characteristics of
fractures formed through the wellbore into a formation surrounding
the wellbore during the hydraulic fracturing completion with
respect to surface variables of the hydraulic fracturing
completion; selecting one or more subsurface objective functions
from a plurality of subsurface objective functions for changing one
or more of the formation characteristics of the fractures, the one
or more subsurface objective functions including an objective
function for wellbore interference, the objective function for
wellbore interference including interference information from a
neighboring wellbore; and applying the fracture formation model
based on the diagnostics data to determine values of the surface
variables for controlling the formation characteristics of the
fractures to converge on the one or more subsurface objective
functions.
[0184] Statement 2: A method according to Statement 1, further
comprising measuring the interference information from the
neighboring wellbore with a distributed acoustic sensing fiber
optic cable at the well.
[0185] Statement 3: A method according to any of Statements 1 and
2, wherein the interference information from the neighboring
wellbore includes data relating to at least one of pressure,
temperature, and strain measured at the wellbore.
[0186] Statement 4: A method according to any of Statements 1
through 3, further comprising receiving pressure data from pressure
gauges at the neighboring wellbore to determine pressure changes at
the neighboring wellbore.
[0187] Statement 5: A method according to any of Statements 1
through 4, wherein the interference information from the
neighboring wellbore includes drainage data measured at the
wellbore, the drainage data including a change in drainage rate
based on interference from the neighboring wellbore.
[0188] Statement 6: A method according to any of Statements 1
through 5, further comprising measuring micro-seismic data at the
wellbore to determine projected geometry of the fractures.
[0189] Statement 7: A method according to any of Statements 1
through 6, wherein the projected geometry of the fractures includes
at least one of length, width, height, and velocity of the
fractures.
[0190] Statement 8: A method according to any of Statements 1
through 7, wherein the objective function for wellbore interference
is determined for each stage of a plurality of stages of the
wellbore.
[0191] Statement 9: A method according to any of Statements 1
through 8, further comprising determining which stages of the
plurality of stages of the wellbore are out-target and in-target to
determine if the neighboring wellbore is interfering with the
wellbore.
[0192] Statement 10: A method according to any of Statements 1
through 9, further comprising modifying the surface variables based
on the fracture formation model applied according to the
diagnostics data to converge on the one or more subsurface
objective functions including the objective function for wellbore
interference.
[0193] Statement 11: A method according to any of Statements 1
through 10, further comprising forming a completion plan for
performing the hydraulic fracturing completion of the wellbore
based on the values of the surface variables.
[0194] Statement 12: A method according to any of Statements 1
through 11, wherein the completion plan is a modified completion
plan formed by modifying a previous completion plan for the
hydraulic fracturing completion based on the values of one or more
of the surface variables.
[0195] Statement 13: A method according to any of Statements 1
through 12, wherein the formation characteristics include either or
both subsurface fracture geometry of the fractures during formation
and growth behavior of the fractures during formation.
[0196] Statement 14: A method according to any of Statements 1
through 13, wherein the plurality of subsurface objective functions
include one or more of an objective function for fracture
complexity, an objective function for cluster efficiency, and an
objective function for one or more surface costs associated with
changing one or more of the formation characteristics of the
fractures during the hydraulic fracturing completion.
[0197] Statement 15: A method according to any of Statements 1
through 14, wherein the selecting of the one or more subsurface
objective functions includes assigning a weight to each of the one
or more subsurface objective functions that is greater than a
corresponding weight assigned to each other subsurface objective
functions of the plurality of subsurface objective functions.
[0198] Statement 16: A system comprising: one or more processors;
and at least one computer-readable storage medium having stored
therein instructions which, when executed by the one or more
processors, cause the system to: receive diagnostics data of a
hydraulic fracturing completion of a wellbore; access a fracture
formation model that models formation characteristics of fractures
formed through the wellbore into a formation surrounding the
wellbore during the hydraulic fracturing completion with respect to
surface variables of the hydraulic fracturing completion; select
one or more subsurface objective functions from a plurality of
subsurface objective functions for changing one or more of the
formation characteristics of the fractures, the one or more
subsurface objective functions including an objective function for
wellbore interference, the objective function for wellbore
interference including interference information from a neighboring
wellbore; and apply the fracture formation model based on the
diagnostics data to determine values of the surface variables for
controlling the formation characteristics of the fractures to
converge on the one or more subsurface objective functions.
[0199] Statement 17: A system according to Statement 16, wherein
the instructions which, when executed by the one or more
processors, cause the system to measure the interference
information from the neighboring wellbore with a distributed
acoustic sensing fiber optic cable at the wellbore.
[0200] Statement 18: A system according to any of Statements 16 and
17, wherein the interference information from the neighboring
wellbore includes data relating to at least one of pressure,
temperature, and strain measured at the wellbore.
[0201] Statement 19: A non-transitory computer-readable storage
medium comprising: instructions stored on the non-transitory
computer-readable storage medium, the instructions, when executed
by one or more processors, cause the one or more processors to:
receive diagnostics data of a hydraulic fracturing completion of a
wellbore; access a fracture formation model that models formation
characteristics of fractures formed through the wellbore into a
formation surrounding the wellbore during the hydraulic fracturing
completion with respect to surface variables of the hydraulic
fracturing completion; select one or more subsurface objective
functions from a plurality of subsurface objective functions for
changing one or more of the formation characteristics of the
fractures, the one or more subsurface objective functions including
an objective function for wellbore interference, the objective
function for wellbore interference including interference
information from a neighboring wellbore; and apply the fracture
formation model based on the diagnostics data to determine values
of the surface variables for controlling the formation
characteristics of the fractures to converge on the one or more
subsurface objective functions.
[0202] Statement 20: A non-transitory computer-readable storage
medium according to Statement 19, wherein the instructions, when
executed by the one or more processors, cause the one or more
processors to measure the interference information from the
neighboring wellbore with a distributed acoustic sensing fiber
optic cable at the wellbore.
* * * * *