U.S. patent application number 17/276985 was filed with the patent office on 2022-02-03 for using distributed sensor data to control cluster efficiency downhole.
The applicant listed for this patent is Landmark Graphics Corporation. Invention is credited to Ashwani Dev, Srinath Madasu, Satyam Priyadarshy, Keshava Prasad Rangarajan.
Application Number | 20220034220 17/276985 |
Document ID | / |
Family ID | 1000005954341 |
Filed Date | 2022-02-03 |
United States Patent
Application |
20220034220 |
Kind Code |
A1 |
Madasu; Srinath ; et
al. |
February 3, 2022 |
USING DISTRIBUTED SENSOR DATA TO CONTROL CLUSTER EFFICIENCY
DOWNHOLE
Abstract
A system for determining real time cluster efficiency for a
pumping operation in a wellbore includes a pump, a surface sensor,
a downhole sensor system, and a computing device. The pump can pump
slurry or diverter material in the wellbore. The surface sensor can
be positioned at a surface of the wellbore to detect surface data
about the pump. The downhole sensor system can be positioned in the
wellbore to detect downhole data about an environment of the
wellbore. The computing device can receive the surface data from
the surface sensor, receive the downhole data from the downhole
sensor system, apply the surface data and the downhole data to a
long short-term memory (LSTM) neural network to produce a predicted
cluster efficiency associated with operational settings of the
pump, and control the pump using the operational settings to
achieve the predicted cluster efficiency.
Inventors: |
Madasu; Srinath; (Houston,
TX) ; Dev; Ashwani; (Katy, TX) ; Rangarajan;
Keshava Prasad; (Sugar Land, TX) ; Priyadarshy;
Satyam; (Katy, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Landmark Graphics Corporation |
Houston |
TX |
US |
|
|
Family ID: |
1000005954341 |
Appl. No.: |
17/276985 |
Filed: |
November 30, 2018 |
PCT Filed: |
November 30, 2018 |
PCT NO: |
PCT/US2018/063251 |
371 Date: |
March 17, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 43/12 20130101;
E21B 2200/22 20200501; E21B 47/07 20200501; E21B 47/10 20130101;
E21B 47/12 20130101 |
International
Class: |
E21B 47/12 20060101
E21B047/12; E21B 47/07 20060101 E21B047/07; E21B 47/10 20060101
E21B047/10; E21B 43/12 20060101 E21B043/12 |
Claims
1. A system comprising: a pump in operable communication with a
wellbore having multiple stages, to pump slurry or diverter
material into the wellbore; a surface sensor positionable at a
surface of the wellbore to detect surface data about the pump; a
downhole sensor system positionable in the wellbore to detect
downhole data about an environment of the wellbore; and a computing
device to communicate with the pump, the surface sensor, and the
downhole sensor system, the computing device being operable to:
receive the surface data from the surface sensor; receive the
downhole data from the downhole sensor system; apply the surface
data and the downhole data to a long short-term memory (LSTM)
neural network to produce a predicted cluster efficiency associated
with operational settings of the pump; and control the pump using
the operational settings to achieve the predicted cluster
efficiency.
2. The system of claim 1, wherein the LSTM neural network is a deep
recurrent neural network (DRNN) that is trained using a subset of
the surface data and of the downhole data.
3. The system of claim 1, wherein the surface data includes a pump
pressure from the pump and flow rate of slurry or diverter
material.
4. The system of claim 1, wherein the downhole sensor system is a
distributed acoustic sensing system or a distributed temperature
sensing system implemented by a fiber optic cable.
5. The system of claim 1, wherein the downhole data includes flow
rate percentage at different depth ranges in the wellbore.
6. The system of claim 1, wherein the predicted cluster efficiency
represents a measurement of how uniformly that slurry or diverter
material is distributed among perforation clusters in the
wellbore.
7. The system of claim 1, wherein the computing device is operable
to control the pump using the operational settings to achieve the
predicted cluster efficiency substantially in real time with
respect to receiving the surface data and the downhole data.
8. A method comprising: receiving surface data from a surface
sensor positioned at a surface of a wellbore to detect surface data
about a pump; receiving downhole data from a downhole sensor system
disposed in the wellbore to detect the downhole data about an
environment of the wellbore; applying the surface data and the
downhole data to a long short-term memory (LSTM) neural network to
produce a predicted cluster efficiency associated with operational
settings of the pump; and controlling the pump using the
operational settings to achieve the predicted cluster
efficiency.
9. The method of claim 8, wherein the LSTM neural network is a deep
neural network (DRNN) that is trained using a subset of the surface
data and of the downhole data.
10. The method of claim 8, wherein the surface data includes a pump
pressure from the pump and flow rate of slurry or diverter
material.
11. The method of claim 8, wherein the downhole sensor system is a
distributed acoustic sensing system or a distributed temperature
sensing system implemented by a fiber optic cable.
12. The method of claim 8, wherein the downhole data includes flow
rate percentage at different depth ranges in the wellbore.
13. The method of claim 8, wherein the predicted cluster efficiency
represents a measurement of how uniformly that slurry or diverter
material is distributed among perforation clusters in the
wellbore.
14. The method of claim 8, wherein controlling the pump using the
operational settings to achieve the predicted cluster efficiency
comprises controlling the pump substantially in real time with
respect to receiving the surface data and the downhole data.
15. A non-transitory computer-readable medium that includes
instructions that are executable by a processing device for causing
the processing device to perform operations comprising: receiving
surface data from a surface sensor positioned at a surface of a
wellbore to detect surface data about a pump; receiving downhole
data from a downhole sensor system disposed in the wellbore to
detect the downhole data about an environment of the wellbore;
applying the surface data and the downhole data to a long
short-term memory (LSTM) neural network to produce a predicted
cluster efficiency associated with operational settings of the
pump; and controlling the pump using the operational settings to
achieve the predicted cluster efficiency.
16. The non-transitory computer-readable medium of claim 15,
wherein the LSTM neural network is a deep recurrent neural network
(DRNN) that is trained using a subset of the surface data and of
the downhole data.
17. The non-transitory computer-readable medium of claim 15,
wherein the surface data includes a pump pressure from the pump and
flow rate of slurry or diverter material, wherein the downhole data
includes flow rate percentage at different depth ranges in the
wellbore.
18. The non-transitory computer-readable medium of claim 15,
wherein the downhole sensor system is a distributed acoustic
sensing system or a distributed temperature sensing system
implemented by a fiber optic cable.
19. The non-transitory computer-readable medium of claim 15,
wherein the predicted cluster efficiency represents a measurement
of how uniformly that slurry or diverter material is distributed
among perforation clusters in the wellbore.
20. The non-transitory computer-readable medium of claim 15,
wherein the operation of controlling the pump using the operational
settings to achieve the predicted cluster efficiency comprises
controlling the pump substantially in real time with respect to
receiving the surface data and the downhole data.
Description
TECHNICAL FIELD
[0001] The present disclosure relates generally to devices for use
in wellbores. More specifically, but not by way of limitation, this
disclosure relates to using distributed sensor data to determine
and control cluster efficiency downhole.
BACKGROUND
[0002] A well such as an oil or gas well can include a wellbore
drilled through a subterranean formation. The wellbore can include
perforations. Fluid can be injected through the perforations to
create fractures in the subterranean formation in a process
referred to as hydraulic fracturing. The fractures can enable
hydrocarbons to flow from the subterranean formation into the
wellbore, from which the hydrocarbons can be extracted. Cluster
efficiency can refer to how uniformly slurry fluid is distributed
among perforation clusters. An inefficient cluster efficiency may
involve one perforation cluster receiving most of the slurry fluid
while another perforation cluster receives very little. Measuring
or predicting cluster efficiency can be challenging as direct real
time measurement may not be possible and predictive models may
involve many variables with several assumptions that may not apply
to a particular wellbore.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a cross-sectional view of an example of well that
includes a system for pumping slurry or diverter material in a
wellbore according to one example of the present disclosure.
[0004] FIG. 2 is a schematic diagram of a system for pumping slurry
or diverter material in a wellbore according to one example of the
present disclosure.
[0005] FIG. 3 is an example of a flowchart of a process for pumping
slurry or diverter material in a wellbore according to one example
of the present disclosure.
[0006] FIG. 5 is a chart of slurry flow rate with respect to
different depth ranges detected by a distributed acoustic sensing
system according to one example of the present disclosure.
[0007] FIG. 5 is a graphical illustration of a model that can be
used in a system for pumping material in a wellbore according to
one example of the present disclosure.
[0008] FIG. 6 is a graphical illustration of another model that can
be used in a system for pumping material in a wellbore according to
one example of the present disclosure.
[0009] FIG. 7 is a further graphical illustration of the model of
FIG. 6 being used in the system for pumping material in a wellbore
according to one example of the present disclosure.
DETAILED DESCRIPTION
[0010] Certain aspects and features of the present disclosure
relate to determining a cluster efficiency of a hydraulic
fracturing operation in a wellbore using distributed acoustic
sensing (DAS) data and/or distributed temperature sensing (DTS)
data, surface variable sensors to detect surface data, and a
machine-learning subsystem to use the data to predict the cluster
efficiency. Based on the predicted cluster efficiency, the
hydraulic fracturing operation can be modified or otherwise
controlled or planned.
[0011] A DAS or DTS can include distributed sensors in a wellbore
or a fiber optic cable that is capable of sensing conditions
downhole at different points along the fiber optic cable. The
sensed data can be transmitted to a subsystem at the surface of the
wellbore for analysis and interpretation. Examples of sensed data
include temperature, pressure, fluid flow rate, and other
characteristics of the environment in the wellbore. The data can be
sensed and communicated to the subsystem substantially in real time
such that the present conditions in a wellbore can be determined.
Surface variable sensors can include one or more sensors positioned
at or proximate to the surface of the wellbore to detect data at
the surface, such as surface pressure, surface fluid flow rate,
proppant rate, cluster spacing, and stage location.
[0012] An example of the machine-learning subsystem is a deep
recurrent neural network (DRNN) or other long short-term memory
(LSTM) machine-learning algorithm that can use the data from the
distributed sensors and the surface variable sensors to predict
cluster efficiency for the wellbore. In one example, training data
is created from a subset of the received data and a temporal model
is generated for the DRNN. Temporal values of surface data and
distributed sensed data is inputted into the model and a cluster
efficiency is outputted. The cluster efficiency can be used to
control a slurry pumping subsystem that has flow control and
directional control by determining how much slurry to pump and how
fast to pump the slurry into the wellbore for the fracturing
operation during stimulation. By combining surface data
observations and downhole data, cluster efficiency can be better
predicted and stimulation operations can be successfully controlled
using real time decisions to change the completion design or to
pump diverter material and the amount of diverter material to pump
downhole.
[0013] Illustrative examples are given to introduce the reader to
the general subject matter discussed here and are not intended to
limit the scope of the disclosed concepts. The following sections
describe various additional features and examples with reference to
the drawings in which like numerals indicate like elements, and
directional descriptions are used to describe the illustrative
aspects but, like the illustrative aspects, should not be used to
limit the present disclosure.
[0014] FIG. 1 is a partial cross-sectional diagram of a well 100
having layers 104a, 104b, 104c, and 104d in a production zone. The
layers 104a-d can be adjacent portions of a subterranean formation
through which a wellbore 102 is formed. The layers 104a-d can each
have a different composition. Each layer may be treated differently
with respect to material placement, and to that extent, a material
placement process for well 100 may be said to be a "multi-stage"
process in that each time a change in fracturing characteristics
occurs during material placement due to the multiple layers,
another stage in the material placement process can be reached. The
well 100 can also include a computing device 110, a pump 120, and
downhole sensors 130. The downhole sensors 130 can be part of a DAS
or DTS that can collect, in real time, data about the environment
within the well 100. Also included is a surface sensor 140, which
can detect pressure, flow rates, and other data at the surface of
the well 100. Although one surface sensor 140 is shown, more than
one surface sensor 140 can be used.
[0015] The computing device 110 can dynamically control a pumping
schedule for slurry to be pumped in well 100 by the pump 120, such
as for stimulating the well 100. The computing device 110 can
determine the specific pressure and the specific pump rate of
slurry to pump into the wellbore 102. The computing device 110 can
receive data from the downhole sensors 130 and the surface sensor
140 and use the data to determine a cluster efficiency for the well
100 that is used to control the pump 120 in how much slurry is
pumped and how fast the slurry is pumped.
[0016] In some examples, the computing device 110 can be used to
control the placement of a diverter. A diverter can be a fluid
(e.g., polylactic acid) for temporarily reducing permeability in a
layer. The diverter material injected into the subsurface formation
may be, for example, a degradable polymer. Examples of different
degradable polymer materials that may be used include, but are not
limited to, polysaccharides; lignosulfonates; chitins; chitosans;
proteins; proteinous materials; fatty alcohols; fatty esters; fatty
acid salts; aliphatic polyesters; poly(lactides); poly(glycolides);
poly( -caprolactones); polyoxymethylene; polyurethanes;
poly(hydroxybutyrates); poly(anhydrides); aliphatic polycarbonates;
polyvinyl polymers; acrylic-based polymers; poly(amino acids);
poly(aspartic acid); poly(alkylene oxides); poly(ethylene oxides);
polyphosphazenes; poly(orthoesters); poly(hydroxy ester ethers);
polyether esters; polyester amides; polyamides;
polyhydroxyalkanoates; polyethyleneterephthalates;
polybutyleneterephthalates; polyethylenenaphthalenates, and
copolymers, blends, derivatives, or combinations thereof. But
various examples of the present disclosure are not intended to be
limited thereto and that other types of diverter materials may also
be used. At a certain stage, the amount of diverter placed in the
wellbore may be greater than or less than at other stages. In some
examples, the computing device 110 can be used to similarly control
the placement of hydraulic fracturing fluid. In additional or
alternative examples, the computing device 110 can optimize the
pumping schedule so that less time is needed or less material is
needed to achieve a desired result.
[0017] In some aspects, the pump 120 can be positioned at the
surface of the well 100 for pumping a fluid into the wellbore 102.
The pump 120 can be communicatively coupled to the computing device
110 for receiving instructions from the computing device 110. In
additional or alternative aspects, the well 100 can include one or
more pumps.
[0018] The downhole sensors 130 can be positioned in the wellbore
102 for measuring average pressures and flow rates at each stage,
and communicating this data to the surface. The well 100 can
include a multilateral wellbore having any number of lateral bores,
each passing through any number of layers. In some examples, the
wellbore can include a cement casing. The wellbore can be in any
phase, including installation, completion, stimulation, and
production. In some aspects, a wellbore can have a single downhole
sensor.
[0019] FIG. 2 is a block diagram of an example of a system 200 for
controlling a pump over time according to some aspects. In some
examples, the components shown in FIG. 2 (e.g., the computing
device 110 and power source 220 can be integrated into a single
structure. For example, the components can be within a single
housing. In other examples, the components shown in FIG. 2 can be
distributed (e.g., in separate housings) and in electrical
communication with each other.
[0020] The system 200 includes a computing device 110. The
computing device 110 can include a processor 204, a memory 207, and
a bus 206. The processor 204 can execute one or more operations for
obtaining data associated with the wellbore and controlling a pump
120 to place material, such as slurry, into the wellbore. The
processor 204 can execute instructions stored in the memory 207 to
perform the operations. The processor 204 can include one
processing device or multiple processing devices. Non-limiting
examples of the processor 204 include a Field-Programmable Gate
Array ("FPGA"), an application-specific integrated circuit
("ASIC"), a microprocessor, etc.
[0021] The processor 204 can be communicatively coupled to the
memory 207 via the bus 206. The non-volatile memory 207 may include
any type of memory device that retains stored information when
powered off. Non-limiting examples of the memory 207 include
electrically erasable and programmable read-only memory ("EEPROM"),
flash memory, or any other type of non-volatile memory. In some
examples, at least some of the memory 207 can include a
non-transitory medium from which the processor 204 can read
instructions. A non-transitory computer-readable medium can include
electronic, optical, magnetic, or other storage devices capable of
providing the processor 204 with computer-readable instructions or
other program code. Non-limiting examples of a computer-readable
medium include (but are not limited to) magnetic disk(s), memory
chip(s), ROM, random-access memory ("RAM"), an ASIC, a configured
processor, optical storage, or any other medium from which a
computer processor can read instructions. The instructions can
include processor-specific instructions generated by a compiler or
an interpreter from code written in any suitable
computer-programming language, including, for example, C, C++, C#,
etc.
[0022] In some examples, the memory 207 can include computer
program instructions 210 for executing a DRNN long-short term
memory (LSTM) machine-learning module or other type of deep
recurrent neural network. The instructions 210 can be usable for
applying the DRNN to wellbore data and surface data associated with
the wellbore and controlling the pump in response to a predicted
value of a response variable. In some examples, the memory 207 can
include stored variable values 212 and stored data 213, such as
surface data and wellbore data from sensors 140, 130,
respectively.
[0023] The system 200 can include a power source 220. The power
source 220 can be in electrical communication with the computing
device 110. In some examples, the power source 220 can include a
battery or an electrical cable (e.g., a wireline). In some
examples, the power source 220 can include an AC signal generator.
System 200 receives input from downhole sensors 130 and surface
sensor 140. System 200 in this example also includes input/output
interface 232. Input/output interface 232 can connect to a
keyboard, pointing device, display, and other computer input/output
devices. An operator may provide input using the input/output
interface 232. All or portions of input/output interface 232 may be
located either locally or remotely relative to the rest of system
200.
[0024] During a stage of the stimulation process for a stimulated
well, slurry can be pumped by the pump 120 at the top of the
wellhead. The physics and engineering aspects that are involved can
be complex and data can sometimes be uncertain and noisy. The use
of a DRNN can resolve time and spatial non-linear variations. The
DRNN can predict a cluster efficiency in a slurry pumping operation
based on surface data from a surface sensor and wellbore data from
one or more downhole sensors, such as a DAS or DTS. The DRNN can be
trained using training data of a subset of data received from the
sensors (e.g., time scaled or based on another variable) with known
cluster efficiencies, or from data received from another well than
the one to which the trained DRNN will be applied.
[0025] FIG. 3 is an example of a flowchart of a process 300 for
predicting a cluster efficiency and controlling a pump for pumping
slurry in a wellbore. Some examples can include more, fewer, or
different blocks than those shown in FIG. 3. The blocks shown in
FIG. 3 can be implemented using, for example, the computing device
illustrated in FIG. 1 and FIG. 2.
[0026] In block 302, a computing device with a trained DRNN model
receives surface data detected by a surface sensor at a surface of
a wellbore. The surface data can include a pressure of slurry being
pumped in to a wellbore and a flow rate of the slurry being pumped
into the wellbore. The surface sensor can be a flow sensor and may
include different components, such as a component to measure flow
rate and another component, such as a piezoelectric component, to
measure pressure. The computing device can receive the surface data
by being communicatively coupled to the surface sensor. The surface
data can be associated with a pumping stage at which the pump is
operating.
[0027] In block 304, the computing device receives downhole data
from one or more downhole sensors, such as sensors that are part of
a DAS or DTS. For example, the downhole sensors may be part of or
implemented by a fiber optic cable that is positioned in the
wellbore and capable of detecting data about the environment
downhole at different positions in the wellbore. The downhole data
can include flow rates and pressures of flow of slurry at different
positions in the wellbore, along with the locations of fractures in
the wellbore. The downhole data can include other types of data,
such as acoustically sensed data representing a physical structure
of the wellbore environment at different positions in the wellbore.
The computing device can be communicatively coupled to the downhole
sensors, such as via a fiber optic coupling to the DAS or DTS fiber
optic cable.
[0028] In block 306, the computing device uses the surface data and
the downhole data as inputs to the trained DRNN and determines a
cluster efficiency for the wellbore that is associated with certain
pump operational settings, such as pressure and flow rate of slurry
pumping. The cluster efficiency can be how uniformly the slurry
flow will be distributed among perforation clusters in the wellbore
and the outputted cluster efficiency can represent an achievable
uniform and distributed slurry flow among the factures in the
wellbore to allow for more efficiency production if slurry or
diverter material is pumped according to the operational
settings.
[0029] In block 308, the computing device outputs a command to the
pump to operate according to the operational settings that the
computing device predicts will result in a high cluster efficiency.
The pump can be communicatively coupled to the computing device to
receive the command and can change operational settings in
accordance with the command. In response to the pump changing
operational settings, the pump can be considered to be operating in
a subsequent pumping stage and the process can return to block 302
to repeat the process for the subsequent pumping stage.
[0030] FIG. 4 is a chart 400 of slurry rate flow percentages per
depth range as detected by downhole sensors that are part of a DAS
system. The chart 400 indicates that slurry flow rate percentage is
highest at depth range 1 and depth range 3 and lower at other depth
ranges. For cluster efficiency, the percentage of slurry flow
detected at different depth ranges would ideally be the same or
similar. The detected slurry flow rate can be used with surface
data by a computing device with a DRNN to determine operational
settings of the pump to achieve a better cluster efficiency than is
reflected in FIG. 4.
[0031] FIG. 5 is a graphical illustration of an example of a DRNN
that is an LSTM 500 that can be used to control pumping operations
for slurry or diverter material in a wellbore in a wellbore. LSTM
500 does not make use of convolutional layers. The model can
provide for a multi-step prediction method for spatiotemporal data
provided by the surface sensor and downhole sensors. LSTM 500 can
provide a network structure for spatiotemporal data that accounts
for the spatiotemporal characteristics of the data by providing
strong correlation between local neighbors. The structure of the
LSTM is configured by the surface data and the downhole data to
form a structure based on state-to-state transitions. The network
can be regularized by specifying a number of hidden units in the
LSTM to avoid over-fitting or under-fitting the surface data.
Over-fitting of the data occurs when a model fits the data almost
perfectly and the model is complicated. This typically means the
model is fitting the noise of the data. The model has low bias and
high variance. On the contrary, under-fitting occurs when the model
is too simple to fit the data hence it has high bias and low
variance. The predictive model should balance between over-fitting
and under-fitting the data. This can be determined by the
performance of the model on the training and test data.
[0032] The network at each of points 502, 504, and 506 can be a
learned representation that accounts for the characteristics of the
wellbore as related to slurry placement. The network at future
points in time 508, 510, and 512 is a predicted representation that
takes into account these characteristics.
[0033] FIG. 6 is a graphical illustration of a DRNN that is an LSTM
network 600 with convolutional layers. Convolutions are used for
both input-to-state and state-to-state connections. Using
convolutional layers, the final state, represented by layer 602,
can have a large receptive field. FIG. 7 shows how convolutional
layers 700 are used to predict wellbore properties. An input 702 is
used in an encoding network including layers 704 and 706. A
prediction network configured with the real-time surface data and
including convolutional layers 708 and 710 produces a prediction,
712.
[0034] In some aspects, systems, devices, and methods for
multi-stage placement of material in a wellbore are provided
according to one or more of the following examples:
[0035] Example 1 is a system comprising: a pump in operable
communication with a wellbore having multiple stages, to pump
slurry or diverter material into the wellbore; a surface sensor
positionable at a surface of the wellbore to detect surface data
about the pump; a downhole sensor system positionable in the
wellbore to detect downhole data about an environment of the
wellbore; and a computing device to communicate with the pump, the
surface sensor, and the downhole sensor system, the computing
device being operable to: receive the surface data from the surface
sensor; receive the downhole data from the downhole sensor system;
apply the surface data and the downhole data to a long short-term
memory (LSTM) neural network to produce a predicted cluster
efficiency associated with operational settings of the pump; and
control the pump using the operational settings to achieve the
predicted cluster efficiency.
[0036] Example 2 is the system of example 1, wherein the LSTM
neural network is a deep recurrent neural network (DRNN) that is
trained using a subset of the surface data and of the downhole
data.
[0037] Example 3 is the system of example 1, wherein the surface
data includes a pump pressure from the pump and flow rate of slurry
or diverter material.
[0038] Example 4 is the system of example 1, wherein the downhole
sensor system is a distributed acoustic sensing system or a
distributed temperature sensing system implemented by a fiber optic
cable.
[0039] Example 5 is the system of example 1, wherein the downhole
data includes flow rate percentage at different depth ranges in the
wellbore.
[0040] Example 6 is the system of example 1, wherein the cluster
efficiency represents a measurement of how uniformly that slurry or
diverter material is distributed among perforation clusters in the
wellbore.
[0041] Example 7 is the system of example 1, wherein the computing
device is operable to control the pump using the operational
settings to achieve the predicted cluster efficiency substantially
in real time with respect to receiving the surface data and the
downhole data.
[0042] Example 8 is a method comprising: receiving surface data
from a surface sensor positioned at a surface of a wellbore to
detect surface data about a pump; receiving downhole data from a
downhole sensor system disposed in the wellbore to detect the
downhole data about an environment of the wellbore; applying the
surface data and the downhole data to a long short-term memory
(LSTM) neural network to produce a predicted cluster efficiency
associated with operational settings of the pump; and controlling
the pump using the operational settings to achieve the predicted
cluster efficiency.
[0043] Example 9 is the method of example 8, wherein the LSTM
neural network is a deep recurrent neural network (DRNN) that is
trained using a subset of the surface data and of the downhole
data.
[0044] Example 10 is the method of example 8, wherein the surface
data includes a pump pressure from the pump and flow rate of slurry
or diverter material.
[0045] Example 11 is the method of example 8, wherein the downhole
sensor system is a distributed acoustic sensing system or a
distributed temperature sensing system implemented by a fiber optic
cable.
[0046] Example 12 is the method of example 8, wherein the downhole
data includes flow rate percentage at different depth ranges in the
wellbore.
[0047] Example 13 is the method of example 8, wherein the cluster
efficiency represents a measurement of how uniformly that slurry or
diverter material is distributed among perforation clusters in the
wellbore.
[0048] Example 14 is the method of example 8, wherein controlling
the pump using the operational settings to achieve the predicted
cluster efficiency comprises controlling the pump substantially in
real time with respect to receiving the surface data and the
downhole data.
[0049] Example 15 is a non-transitory computer-readable medium that
includes instructions that are executable by a processing device
for causing the processing device to perform operations comprising:
receiving surface data from a surface sensor positioned at a
surface of a wellbore to detect surface data about a pump;
receiving downhole data from a downhole sensor system disposed in
the wellbore to detect the downhole data about an environment of
the wellbore; applying the surface data and the downhole data to a
long short-term memory (LSTM) neural network to produce a predicted
cluster efficiency associated with operational settings of the
pump; and controlling the pump using the operational settings to
achieve the predicted cluster efficiency.
[0050] Example 16 is the non-transitory computer-readable medium of
example 15, wherein the LSTM neural network is a deep recurrent
neural network (DRNN) that is trained using a subset of the surface
data and of the downhole data.
[0051] Example 17 is the non-transitory computer-readable medium of
example 15, wherein the surface data includes a pump pressure from
the pump and flow rate of slurry or diverter material, wherein the
downhole data includes flow rate percentage at different depth
ranges in the wellbore.
[0052] Example 18 is the non-transitory computer-readable medium of
example 15, wherein the downhole sensor system is a distributed
acoustic sensing system or a distributed temperature sensing system
implemented by a fiber optic cable.
[0053] Example 19 is the non-transitory computer-readable medium of
example 15, wherein the cluster efficiency represents a measurement
of how uniformly that slurry or diverter material is distributed
among perforation clusters in the wellbore.
[0054] Example 20 is the non-transitory computer-readable medium of
example 15, wherein the operation of controlling the pump using the
operational settings to achieve the predicted cluster efficiency
comprises controlling the pump substantially in real time with
respect to receiving the surface data and the downhole data.
[0055] The foregoing description of certain examples, including
illustrated examples, has been presented only for the purpose of
illustration and description and is not intended to be exhaustive
or to limit the disclosure to the precise forms disclosed. Numerous
modifications, adaptations, and uses thereof will be apparent to
those skilled in the art without departing from the scope of the
disclosure.
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