U.S. patent application number 15/885588 was filed with the patent office on 2019-01-10 for method to determine a location for placing a well within a target reservoir.
The applicant listed for this patent is CHRISTOPHER LOUIS BEATO. Invention is credited to CHRISTOPHER LOUIS BEATO.
Application Number | 20190010789 15/885588 |
Document ID | / |
Family ID | 64902596 |
Filed Date | 2019-01-10 |
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United States Patent
Application |
20190010789 |
Kind Code |
A1 |
BEATO; CHRISTOPHER LOUIS |
January 10, 2019 |
METHOD TO DETERMINE A LOCATION FOR PLACING A WELL WITHIN A TARGET
RESERVOIR
Abstract
A method of drilling a wellbore. The method may include
collecting data for a plurality of well stages in at least one data
reservoir. The method may also include creating a priority group of
well stages from the plurality of well stages in the at least one
data reservoir. The method may also include validating the well
stages included within the priority group to create a validated
priority group. The method may also include analyzing the data for
the validated priority group to determine an effective conductivity
value for each well stage in the validated priority group. The
method may also include using the effective conductivity value to
determine a target location for drilling the wellbore in the target
reservoir. The method may also include drilling a well at the
target location in the target reservoir.
Inventors: |
BEATO; CHRISTOPHER LOUIS;
(Boulder, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BEATO; CHRISTOPHER LOUIS |
Boulder |
CO |
US |
|
|
Family ID: |
64902596 |
Appl. No.: |
15/885588 |
Filed: |
January 31, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62529914 |
Jul 7, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 49/008 20130101;
E21B 49/087 20130101; E21B 49/00 20130101; E21B 47/06 20130101;
E21B 43/26 20130101; E21B 47/07 20200501; E21B 41/0092
20130101 |
International
Class: |
E21B 41/00 20060101
E21B041/00; E21B 49/00 20060101 E21B049/00; E21B 49/08 20060101
E21B049/08; E21B 47/06 20060101 E21B047/06 |
Claims
1. A method of drilling a wellbore comprising: collecting data for
a plurality of well stages in at least one data reservoir; creating
a priority group of well stages from the plurality of well stages
in the at least one data reservoir; validating the well stages
included within the priority group to create a validated priority
group; analyzing the data for the validated priority group to
determine an effective conductivity value for each well stage in
the validated priority group; using the effective conductivity
value to determine a target location for drilling the wellbore in
the target reservoir; and drilling a well at the target location in
the target reservoir.
2. The method of claim 1, wherein validating the well stages
included within the priority group comprises: identifying each well
stage in the plurality of well stages which has experienced a
screen out or has experienced mechanical problems; or identifying a
stimulation design for each well stage in the plurality of well
stages.
3. The method of claim 1, wherein analyzing the data for the
validated priority group includes analyzing at least one of: a
breakdown pressure; a clean fluid volume (fluid without proppant);
a conductivity; a density; a downhole pressure; a downhole rate of
fluid; an effective conductivity; a fracture breakdown pressure; a
fracture closure pressure; a fracturing fluid volume; an initial
shut in pressure; pounds of proppant pumped; a proppant
concentration; a pump pressure; a pump rate; a reservoir fluid
composition; reservoir fluid properties; a reservoir pressure; a
specific gravity; a temperature of fluid; and a time.
4. The method of claim 1, further comprising collecting production
data for each well stage of the plurality of well stages in the at
least one data reservoir; comparing the production data to the
effective conductivity value for each well stage of the plurality
of well stages in the at least one data reservoir; and verifying
that the effective conductivity value correlates with the
production data.
5. The method of claim 1, wherein validating the well stages
included within the priority group comprises: identifying,
evaluating, and selectively eliminating outliers.
6. The method of claim 5, wherein validating the well stages
included within the priority group comprises: eliminating well
stages that experienced a screen out, well stages that experienced
mechanical problems, or the well stages with dissimilar stimulation
designs.
7. The method of claim 1, wherein the validated priority group of
the well stages comprises well stages with similar data.
8. The method of claim 1, wherein analyzing the data for the
validated priority group comprises: calculating a pressure leak-off
rate for each well stage in the validated priority group; and
determining the effective conductivity value for each well stage in
the validated priority group based upon the pressure leak-off rate
of each well stage in the priority group.
9. The method of claim 1, wherein analyzing the data for the
validated priority group comprises applying a dimensionless scaling
factor to the data to form dimensionless data.
10. The method of claim 9, wherein analyzing the data for the
validated priority group comprises determining an effective
dimensionless conductivity value for each well stage in the
validated priority group based upon the dimensionless data.
11. The method of claim 10, wherein analyzing the data comparing
the effective dimensionless conductivity value to the effective
conductivity value.
12. A method of locating a wellbore comprising: collecting data for
a plurality of well stages in at least one data reservoir; creating
a priority group of well stages from the plurality of well stages
in the at least one data reservoir; validating the well stages
included within the priority group to create a validated priority
group; analyzing the data for the validated priority group to
determine an effective conductivity value for each well stage in
the validated priority group; and using the effective conductivity
value to determine a target location for drilling the wellbore in
the target reservoir.
13. The method of claim 12, wherein analyzing the data for the
validated priority group comprises determining the effective
conductivity value for each well stage in the priority group based
upon the data of each well stage in the priority group.
14. The method of claim 12, wherein analyzing the data for the
validated priority group comprises applying a dimensionless scaling
factor to the data to form dimensionless data.
15. The method of claim 14, wherein analyzing the data for the
validated priority group comprises determining an effective
dimensionless conductivity value for each well stage in the
validated priority group based upon the dimensionless data.
16. The method of claim 15, wherein analyzing the data for the
validated priority group comprises comparing the effective
dimensionless conductivity value to the effective conductivity
value.
17. A method of locating a wellbore in a target reservoir
comprising: establishing a model of a data reservoir in a validated
priority group by: collecting data for a plurality of well stages
in the data reservoir; creating a priority group of well stages
from the plurality of well stages in the data reservoir; validating
the well stages included within the priority group to create the
validated priority group; analyzing the data for the validated
priority group to determine the effective conductivity value for
each well stage in the validated priority group; developing the
model of the data reservoir using the effective conductivity value
for each well stage in the validated priority group; predicting an
effective conductivity value in the target reservoir using the
model of the data reservoir to; and locating the wellbore in the
target reservoir using the predicted effective conductivity in the
target reservoir.
18. The method of claim 17, wherein analyzing the data for the
validated priority group comprises applying a dimensionless scaling
factor to the data to form dimensionless data.
19. The method of claim 18, wherein analyzing the data for the
validated priority group comprises determining an effective
dimensionless conductivity value for each well stage in the
validated priority group based upon the dimensionless data.
20. The method of claim 17, wherein predicting the effective
conductivity value in the target reservoir comprises correlating at
least one attribute associated with the target reservoir with the
to the predicted effective conductivity value.
Description
RELATED APPLICATION
[0001] This application claims the benefit, under 35 USC .sctn.
119(e), of the filing of U.S. Provisional Patent Application Ser.
No. 62/529,914 filed on Jul. 7, 2017 and entitled "Method to
Determine and Predict an Effective Conductivity in a Target
Reservoir" to Christopher Louis Beato, which is incorporated herein
by reference in its entirety.
FIELD
[0002] The present embodiments generally relate to methods for
drilling a well within a target reservoir, particularly, to methods
for determining a location for placing a well within a target
reservoir, for example, by predicting the effective conductivity by
assessing quantity of natural fracture systems and paths of
weakness within the target reservoir.
BACKGROUND
[0003] Well testing has been used for decades to determine
essential reservoir properties and to assess wellbore conditions
prior to, during, and after a fracture stimulation of a well. There
are many different types of tests that can be utilized to collect
various pieces of information. The tests employed can vary based
upon various factors, such as well location, well type, formation
type, and the like.
[0004] One specific measure of a reservoir is a conductivity value
(kh). The conductivity value is a representation of the cumulative
effect of matrix permeability, natural fracture systems and paths
of weakness extant in a reservoir. In a shale or mudstone reservoir
the matrix permeability may be very low so the conductivity value
represents the natural fracture systems and paths of weakness
extant in the formation.
[0005] It may be desirable to drill into target reservoirs with
high conductivity values, as this means that the target reservoir
has a higher number of natural fracture systems and paths of
weakness, which will result in a more productive well and a greater
return on investment.
[0006] While a great deal of data is often collected in the oil and
gas industry, methods of predicting conductivity values based upon
the available data have heretofore not been practical or
accurate,
[0007] A need exists, therefore, to leverage existing, available
data to improve the placement of wells within a reservoir, for
example, by accurately qualitatively predicting the level or
quantity of natural fracture systems and paths of weakness prior to
drilling.
[0008] Thus, a need exists for determining optimum locations for
placing a well within a target reservoir, such as by determining
the effective conductivity by assessing quantity of natural
fracture systems and paths of weakness within the target reservoir.
Being able to assess a qualitatively "better" location to drill
into a target reservoir can result in a great deal of cost savings,
increased well productivity, higher return on investment, improved
safety of personnel, and lessened impact on the environment due to
fewer needed wells, and other benefits.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The detailed description will be better understood in
conjunction with the accompanying drawings as follows:
[0010] FIG. 1 depicts a flow diagram of the method according to one
or more embodiments.
[0011] FIG. 2 is a three dimensional representation of a data
reservoir.
[0012] FIG. 3 is a diagram illustrating a relationship between
pre-fracture and post-fracture data.
[0013] FIG. 4 is a three dimensional representation of a data
reservoir.
[0014] FIG. 5 is a three dimensional representation of a data
reservoir.
[0015] FIG. 6 is a three dimensional representation of a data
reservoir.
[0016] FIG. 7 is a three dimensional representation of a data
reservoir.
[0017] FIG. 8 is a three dimensional representation of a data
reservoir.
[0018] The present embodiments are detailed below with reference to
the listed Figures.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0019] Before explaining the present method in detail, it is to be
understood that the method is not limited to the particular
embodiments and that it can be practiced or carried out in various
ways.
[0020] Specific structural and/or functional details disclosed
herein are not to be interpreted as limiting, but merely as a basis
of the claims and as a representative basis for teaching persons of
ordinary skill in the art, upon viewing this disclosure, to
variously employ the present embodiments.
[0021] Disclosed herein are methods generally related to drilling a
well within a target reservoir, more particularly, by determining a
location for placing a well within a target reservoir. In some
embodiments, the methods may qualitatively predict an effective
conductivity of the target reservoir. Additionally or
alternatively, in some embodiments the methods may quantify
relative predictions to further distinguish predicted effective
conductivity values. As will be disclosed herein, the predicted
effective conductivity values may be used to determine one or more
optimum locations within a target reservoir.
[0022] In some embodiments, the methods may be suitable for
application to reservoirs termed by persons of ordinary skill in
the art as "unconventional reservoirs." An "unconventional
reservoir," as used herein, may refer to a reservoir containing oil
and gas that requires special recovery operations. Examples if
unconventional reservoirs can include, but are not limited to, gas
and oil shales, coalbed methane, tight-gas sands, gas-hydrate
deposits, fractured reservoirs, reservoirs with low matrix
permeability, heavy oil and tar sands, and similar reservoirs known
in the industry.
[0023] In some embodiments, the methods may predict the character
and/or quantity of natural fracture systems and other paths of
weakness within a target reservoir, on the basis of data from a
data reservoir. "Natural fracture systems or paths of weakness," as
used herein, may refer to fluid flow pathways within an
unconventional reservoir which can allow oil and gas to move
through the formation, for example, so as to be produced, "Target
reservoir," as used herein, may refer to a three dimensional volume
of rock containing oil or gas that is to be developed. "Data
reservoir," as used herein, may refer to a three dimensional volume
of rock containing oil or gas for which there is data, such as data
resulting from a fracturing or other stimulation operation or data
from a diagnostic test or operation. In embodiments, the data
reservoir and the target reservoir may be the same reservoir.
[0024] Referring to FIG. 1, an embodiment of a method 100 for
determining a location for placing a well within a target reservoir
is illustrated as a flow diagram.
Organizing Data from the Data Reservoir
[0025] In some embodiments, the methods may generally include
organizing data from the data reservoir. The data reservoir can be
any reservoir for which the desired fracture stimulation data
and/or diagnostic data is present. In embodiments, the data
reservoir can also be the target reservoir. For example, it may be
desirable to select an optimum drilling spot within a reservoir
which has been produced, previously, for a period of time. In
alternate embodiments, the target reservoir can be a completely
separate reservoir which is believed to have similar
characteristics, in one or more aspects.
[0026] In some embodiments, the data may include fracture
stimulation data. In various embodiments, fracture stimulation data
can refer to a wide variety of characteristics, attributes, or
parameters associated with the performance of a fracture
stimulation. "Fracture stimulation," as used herein, may refer to
the process of pumping a fluid into a well at high pressure in
order to propagate a hydraulic fracture away from a wellbore and
into a target reservoir allowing a reservoir to be stimulated,
thereby creating and enhancing fluid flow pathways and allowing oil
and gas to be produced. Examples of fracture stimulation data may
include, but are not limited to, reservoir pressure, breakdown
pressure, initial shut in pressure, fracture closure pressure,
fracture breakdown pressure, conductivity, pump pressure, pump
rate, clean fluid volume (fluid without proppant), fracturing fluid
volume, pounds of proppant pumped, proppant concentration,
temperature of the fluid being pumped, the duration of the
fracturing operation and/or the time, the specific gravity of the
fluid being pumped, the density of the fluid being pumped, downhole
pressure, downhole rate of fluid, and the like.
[0027] For example, in the embodiment of FIG. 1, the method 100
includes collecting fracture stimulation data for a plurality of
well stages in at least one data reservoir, as illustrated in step
102, "Well stage," as used herein, may refer to a specific
hydraulic fracture operation at any location in a wellbore. For
example, FIG. 2 is a three dimensional representation of a data
reservoir 200 having a plurality of well stages 210 disposed with
the reservoir 200.
[0028] Additionally or alternatively, in some embodiments, the data
may include data associated with or resulting from one or more
diagnostic tests. Diagnostic tests associated with wells and/or
well stages are generally known to persons of ordinary skill in the
art. An example of such diagnostic tests includes a Diagnostic
Formation Injection Test (DFIT), which is a relatively
long-duration, relatively low-volume fracturing operation in which
a volume of fracturing fluid (such as potassium chloride or a
similar fluid) is pumped until fracture-initiation and, thereafter,
the pressure within the well may be monitored for a period of time,
for example, until fracture closure (e.g., for a period of several
hours or even a few days). Another example of such diagnostic tests
includes a Fluid Efficiency Test (FET), which is a relatively
short-duration, relatively low-volume fracturing operation in which
a volume of fluid is injected into a well stage to observe
break-down followed by a shut-in period (e.g., for a period of
several minutes or hours), for example, so as to observe
instantaneous shut-in pressure (ISIP) subsequent to leak-off of the
pressure. "Instantaneous shut-in pressure (ISIP)," as used herein,
may refer to the recorded pump pressure immediately after pumps are
shut down, for example, during a fracturing operation or diagnostic
test. "Pressure leak-off," as used herein, can refer to the changes
in pressure as recorded at the well from the time that pumps are
shut off, for example, over the course of a predetermined time
interval.
[0029] In the embodiment of FIG. 1, the method 100 includes
collecting diagnostic test data for the plurality of well stages in
the at least one data reservoir, as illustrated in step 104, While
in the embodiment of FIG. 1 the method 100 includes both collecting
fracture stimulation data for a plurality of well stages in at
least one data reservoir, as illustrated in step 102, and
collecting diagnostic test data for the plurality of well stages in
the at least one data reservoir, as illustrated in step 104, in
some other embodiments, a method may utilize only fracture
stimulation data or only data from a diagnostic test. For example,
in some embodiments the data may be collected from any data source
or combination of data sources from which a pressure leak-off rate
can be established over a predetermined duration with respect to
one or more well stages. Additionally or alternatively, in some
embodiments the data collected may be from any data source or
combination of data sources from which a conductivity can be
established with respect to one or more well stages. For example,
invarious embodiments, pressure leak-off data may be determined
based upon measurements via a wellhead sensor or a downhole sensor.
As additional examples, conductivity may be measured, determined,
estimated, or extrapolated based upon injection testing, side-wall
cores, mud gas logs, and the like. For example, in some
embodiments, the data may be associated with pressure pulses
resulting from an occurrence of a water hammer effect within a
wellbore, "Water hammer effect," as used herein, may describe a
sudden shut down of pumps during a fracture stimulation which
results in the formation of a series of pressure pulses, known as a
water hammer effect, due to the confined space of the wellbore, the
reservoir conductivity, and volume. For example, the water hammer
effect within a wellbore may be used to approximate reservoir
conductivity resulting from natural fractures and/or other paths of
weakness within the surrounding formation.
[0030] In some embodiments, organizing the data from the data
reservoir may further include creating a priority group of well
stages from the plurality of well stages in the at least one data
reservoir. "Priority group of well stages," as used herein, may
refer to wells or well stages generally believed by a person having
ordinary skill in the art as being similar to each other and to a
target reservoir in at least one aspect. For example, in the
embodiment of FIG. 1, the method 100 includes creating a priority
group of well stages from the plurality of well stages in the at
least one data reservoir, as illustrated in step 106.
[0031] Persons of ordinary skill in the art, upon viewing this
disclosure, will appreciate those aspects in which well stages may
be similar to each other and/or to the target reservoir. For
example, in some embodiments a well stage may be selected for
inclusion within the priority group on the basis of having a
similar formation pressure, on the basis of formation similarities,
on the basis of similar gamma ray measurements, on the basis of
similar resistivity measurements, on the basis of similar fracture
stimulation fluids used, on the basis of similar amounts of
proppant used, on the basis of similar diagnostic tests used, and
combinations thereof. In some, more particular embodiments, a
person having ordinary skill in the art may select a data reservoir
on the basis of having similarities to the target such as rock
matrix characteristics (e.g., the same or substantially similar
depositional environments) and/or the same or substantially similar
basis reservoir pressure. For example, a skilled artisan may want
to include well stages having the same or substantially similar
reservoir pressures in a priority group in that similarities in
reservoir pressure may serve as an indication of reservoir
depletion (e.g., a well stage within a pressure-depleted reservoir
may exhibit a completely different leakoff rate due to the lower
pressure in comparison to a relatively undepleted reservoir).
[0032] Also, persons of ordinary skill in the art, upon viewing
this disclosure, will appreciate to the degree to which a given
aspect may be similar between two well stages and/or between a well
stage and to the target reservoir. For example, a well stage that
varies from other well stages included within the priority group
and/or from the target reservoir by more than about one standard
deviation in a given aspect, such as instantaneous shut in
pressure, may be determined as dissimilar, and therefore, not
included within the priority group. However, the amount of
acceptable variation can be selected based upon the application and
the particular aspect.
[0033] The priority group may represent an initial set of well
stages. The priority group can be selected by persons of ordinary
skill in the art, upon viewing this disclosure, having knowledge of
a plurality of well stages. Additionally or alternatively, the
priority group can be selected by mining existing data, for
example, from a database including a plurality of well stages, for
specific parameters or aspects. In some embodiments, the well
stages within the priority group may be wells within the target
reservoir that have not, or likely have not, experienced pressure
interference from a neighboring well.
[0034] In some embodiments, organizing the data from the data
reservoir may further include validating the well stages included
within the priority group to create a validated priority group.
Generally, during validation, outlier well stages may be eliminated
from the priority group, for example, due to dissimilarities
between a well stage and the other priority group members,
differences in fracturing techniques between a well stage and the
other priority group members (e.g. differences in fracturing fluid
composition), abnormalities associated with a particular well
stage, such mechanical failures, screen outs, and the like.
"Mechanical problems," as used herein, may refer to any of a
variety of mechanical equipment malfunctions, such as gauges that
may not be reading correctly, or any other equipment failure
relative to a well at any time during drilling, completion, or
other operations. "Screen out," as used herein, may refer to an
unplanned event that occurs when a hydraulic fracture stimulation
has terminated because an injection pressure has reached its upper
limit. For example, when the well injection pressure nears the
maximum allowable working pressure of the fracturing equipment in
use or of the well itself.
[0035] For example, in the embodiment of FIG. 1, the method 100
includes validating the well stages included within the priority
group to create a validated priority group, as illustrated in step
108. For example, in various embodiments, validating the well
stages may include identifying each well stage in the plurality of
well stages in the at least one data reservoir which has
experienced a screen out or has experienced mechanical problems,
identifying a stimulation design for each well stage in the
plurality of well stages in the at least one data reservoir, or the
like. Persons of ordinary skill in the art, upon viewing this
disclosure, will be able to selectively eliminate outlying well
stages so as to validate the well stages selected for the priority
group, for example, by reviewing information associated with a
given well stage. Additionally or alternatively, the priority group
can be validated by mining the data associated with the well stages
within the priority group, for example, from a database including a
plurality of well stage for outlying parameters or aspects or
substantial deviations in parameters or aspects.
[0036] In some embodiments, outlier well stages can be grouped and
analyzed separately to provide valuable information. In
embodiments, persons of ordinary skill in the art, upon viewing
this disclosure, can create a priority group of outliers. For
example, in some embodiments one or more priority groups of
outlying well stages (e.g., well stages disqualified from the
priority group on the basis of having screened out, having exhibitd
too high of a fracture propagation pressure, or the like) may form
a priority group with may be analyzed to provide data in addition
to the data from the validated priority group. For example, one or
more priority groups of outlying well stages may be effective to
provide negative data, such as excluding reservoirs and/or portions
of a reservoir that are not desirable. In some embodiments,
multiple priority groups can be created and analyzed for a gicen
target reservoir.
Analyzing the Data from the Well Stages of the Validated Priority
Group
[0037] In some embodiments, the method may generally include
analyzing the data for each of the well stages of the validated
priority group. Generally, in some embodiments, the data associated
with each of the well stages can be analyzed to determine a
pressure leak-off rate for each of the well stage, for example, in
order to enable a determination of an effective post-fracture
conductivity value for each well stage in the validated priority
group. "Effective conductivity value," as used herein, can refer to
a measure of the conductivity of an unconventional reservoir due to
the number of natural fractures or planes of weakness present.
[0038] In some embodiments, analyzing the data for each of the well
stages of the validated priority group includes determining a
pressure leak-off rate for each of the well stages of the validated
priority group. The pressure leak-off rate may be calculated in any
suitable manner as determined by persons of ordinary skill in the
art, upon viewing this disclosure.
[0039] In some embodiments, the pressure leak-off rate for a given
well stage may be determined using a suitable mathematical
function. For example, in the embodiment of FIG. 1, the method 100
includes determining the pressure leak-off rate for a given well
stage, as illustrated as step 110. An example of a mathematical
function by which the pressure leak-off rate may be determined
includes the G-function. Generally, the G-function is a
dimensionless time function suitable for developing a linear
relationship with respect to pressure behavior, such as during
pressure leak-off. More particularly, the G function is an example
of a dimensionless scaling factor. An example of the use of the
G-function in determining pressure leak-off rate is provided in the
examples, below. Persons of ordinary skill in the art, upon viewing
this disclosure, will appreciate that other methods of mathematical
manipulation can readily be applied in order to determine the
pressure leak-off rate for each of the well stages.
[0040] In some embodiments, analyzing the data for each of the well
stages of the validated priority group includes determining an
effective conductivity value for each of the well stages of the
validated priority group based upon the pressure leak-off rate of
each of the respective well stages. For example, in the embodiment
of FIG. 1, the method 100 includes determining an effective
conductivity value for each of the well stages of the validated
priority group, as illustrated at step 112.
[0041] In some embodiments, the effective conductivity value for
one or more of the well stages may be expressed as a qualitative
designation of the effective conductivity. In some embodiments, the
pressure leak-off rate of a given well stage can be compared to
pressure leak-off rates of the other well stages within the
validated priority group, for example, to give qualitative values
of conductivity. For example, well stages having a relatively high,
rapid pressure leak-off rate may be characterized as having a high
conductivity. Well stages having an intermediate, more gradual
pressure leak-off rate may be characterized as having a medium or
intermediate conductivity. Well stages having a relatively low,
slow pressure leak-off rate may be characterized as having a low
conductivity. In various embodiments, the well stages may be
delineated, for example, between high-conductivity well stages,
intermediate conductivity well stages, and low-conductivity well
stages, based upon any suitable standard. For example, in some
embodiments, well stages having a pressure leak-off rate that is
within about one (1) standard deviation may be characterized as
being an intermediate-conductivity well stage; well stages having a
pressure leak-off rate of less than about one (1) standard
deviation may be characterized as being a low-conductivity well
stage; well stages having a pressure leak-off rate that is more
than about one (1) standard deviation may be characterized as being
a high-conductivity well stage.
[0042] Additionally or alternatively, in some embodiments the
effective conductivity value for one or more of the well stages may
comprise a quantitative value. In some embodiments a correlation
(e.g., a mathematical relationship) may be developed between the
pressure leak-off rate of a given well stage and the effective
conductivity of that well stage. In various embodiments, the
correlation between pressure leak-off rate and effective
conductivity may be developed by any suitable methodology. For
example, in some embodiments, the pressure leak-off rate may be
related to (e.g., plotted with respect to) the pre-facture
effective conductivity for those well stages within the validated
priority group having both pressure leak-off data and effective
conductivity data. In some embodiments, a best-fit equation may be
developed based upon the data points relating (e.g., as a plot)
pressure leak-off rate and effective conductivity. For example, in
some embodiments, the best-fit equation correlating pressure
leak-off rate and effective conductivity may be a polynomial
equation. The relationship between pressure leak-off rate and
effective conductivity can be used to determine, quantitatively,
the effective post-fracture conductivity for each of the well
stages of the validated priority group.
[0043] In some embodiments, determining the effective conductivity
quantitatively, as opposed to qualitatively, may allow for greater
resolution of the data associated with the well stages. For
example, rather than designating the well stages as having high,
intermediate, or conductivity, a quantitative determination of
effective conductivity based upon leak-off rate can be made for the
well stages within a validated priority group.
[0044] In some embodiments, analyzing the data for each of the well
stage of the validated priority group includes verifying the
effective conductivity that has been determined for each well stage
of the validated priority group. In various embodiments, the
effective conductivity that has been determined for each well stage
can be compared to other data for each of the respective well
stages and/or the associated well. For example, the effective
conductivity that has been determined for each well stage can be
compared to production logs, tracer surveys, seismic data, electric
line logs, drill mud log data, or combinations thereof. The
comparison between effective conductivity and this data (e.g., well
data) may help to verify that the effective conductivity that was
determined for each well stage.
[0045] In some embodiments, analyzing the data for each of the well
stage of the validated priority group includes analyzing both
pre-fracture and post-fracture data. For example, the ISIP for the
pre-fracture diagnostic tests may be were plotted and compared to
the post-fracture ISIP data for each well stage of the validataed
priority group. A comparison plotted as a straight line may be
effective to confirm that the same polynomial equation describing
the best fit curve solving for effective conductivity (e.g., which
may be developed with pre-fracture data) can be used with the rate
of change calculated from the dimensionless data analysis of the
post-fracture data. An example of such a correlation is illustrated
in FIG. 3.
[0046] In some embodiments, analyzing the data for each of the well
stages of the validated priority group includes creating a model of
the data reservoir, for example, a three-dimensional (3D) model of
the data reservoir. The effective conductivity data for each of the
well stages, as determined herein, may be located within the 3D
model. For example, FIGS. 4 and 5 are a two-dimensional (in the
horizontal plane) and a three-dimensional representation of the
data reservoir illustrated in FIG. 2. The examples of FIGS. 4 and 5
illustrate the variation in natural fracture density within the
data reservoir.
[0047] Additionally, in some embodiments, the 3D model may include
a variety of data associated with each of the well stages of the
validated priority group. For example, in various embodiments, the
3D model may include production logs, tracer surveys, seismic data,
electric line logs, drill mud log data, total organic content
measurements from drill cuttings analyses, and/or whole core
analyses for one or more of the well stages of the validated
priority group. Seismic data may have many attributes that can be
correlated to effective conductivity.
[0048] In some embodiments, the 3D model may be used to conduct
multi-attribute and/or multivariate analysis of the data reservoir
(as represented by the 3D model). Generally, the multi-attribute
and/or multivariate analysis may apply machine learning processes
to various data with regard to the data reservoir (e.g., as
represented by the 3D model) to analyze multiple attributes
simultaneously and, thereby, extract additional information from
the 3D model. For example, FIGS. 6 and 7 are a two-dimensional (in
the horizontal plane) and a three-dimensional representation of the
data reservoir illustrated in FIGS. 2, 4, and 5. The examples of
FIGS. 6 and 7 illustrate the results of a multi-attribute and/or
multivariate analysis and shows the correlation between natural
fracture density within the data reservoir and additional features
of the reservoir, such as resistivity and porosity.
[0049] Examples of multi-attribute and/or multivariate analysis
methods may make use of machine-learning techniques such as neural
networks, a linear regression, a non-linear regression, or any
other suitable means known to persons of ordinary skill in the art,
upon viewing this disclosure. In some embodiments,
commercially-available software may be employed to conduct at least
a portion of the multi-attribute and/or multivariate analysis.
Examples of suitable, commercially-available software packages
include GGX; Paradise, commercially-available from Geophysical
Insights; RSA; Landmark; and Transform, commercially-available from
Drillers Info.
[0050] In some embodiments, the multi-attribute and/or multivariate
analysis may include a principal component analysis. The principal
component analysis may be effective to identify those attributes of
the data reservoir that are most significant with respect to a
given condition. Additionally or alternatively, in some embodiment,
the multi-attribute and/or multivariate analysis may include the
generation of one or more self-organizing maps (SOMs). The
self-organizing map is an artificial neural network based
machine-learning process that can be applied to 3D volumes, such as
the data reservoir, to further classify data within that
volume.
Applying the Data Analysis to the Target Reservoir
[0051] In some embodiments, the method may generally include
applying the analysis of the well stages of the validated priority
group to the target reservoir. For example, in the embodiment of
FIG. 1, the method 100 includes applying the analysis of the well
stages of the validated priority group to the target reservoir, as
illustrated in step 114.
[0052] In some embodiments, applying the analysis of the well
stages of the validated priority group to the target reservoir may
include generating a 3D model of the target reservoir. For example,
the analysis of the data reservoir, such as the relationships,
trends, correlations and the like developed from a principal
component analysis, self-organizing maps, or other
multi-attribute/multivariate analysis developed based upon the data
reservoir may be applied to the target reservoir. The 3D model of
the target reservoir may incorporate available data associated with
the target reservoir, such as three dimensional seismic data,
ambient seismic data, passive seismic data, or other suitable data
(such as from the water-hammer effect). Based upon this 3D model of
the target reservoir, the method may yield a prediction of an
effective conductivity of a target reservoir due to natural
fracture systems or paths of weakness.
[0053] For example, in some embodiments, the methods may yield a
qualitative prediction of the effective conductivity of various
portions of the target reservoir. For example, various portions of
the target reservoir may be characterized as having a "high"
conductivity value, an "intermediate" conductivity value, or a
"low" conductivity value. Additionally or alternatively, the
methods may yield a quantitative prediction of the effective
conductivity of the target reservoir. For example, in some
embodiments, the predicted conductive value of two or more portions
of the target reservoir may quantified, for example, for
comparison. Additionally or alternatively, the methods can further
quantify relative predictions between multiple target reservoirs to
further distinguish predicted valuations.
[0054] In some embodiments, the methods may use the effective
pre-fracture conductivity value to determine optimum locations for
drilling in the target reservoir. Once again, this effective
pre-fracture conductivity can be combined with seismic, or other
data for a prospective site. In the event that diagnostic testing
has occurred at the prospective site, this data can be incorporated
to accurately predict conductivity of the prospective
reservoir.
[0055] In some embodiments, the method may use the effective
post-fracture conductivity value to determine optimum locations for
drilling in a target reservoir. The method can make use of seismic,
or other data for a prospective site in conjunction with effective
conductivity values from similar well stages to predict an optimum
drilling site for a future well.
[0056] In some embodiments, the method may include comparing the
predicted results using both the pre-fracture and the post-fracture
data. This allows for very specific validation of data utilized to
predict conductivity and can result in a high degree of accuracy.
Other data sources used for comparison and validation purposes
including, but not limited to are: microseismic data, fiber optic
data, distributed acoustic monitoring, distributed strain
monitoring, distributed temperature monitoring, production rates,
pressure data, and the like.
[0057] In some embodiments, the 3D model of the target reservoir
may be used to predict an optimum location for a production or
other well. For example, the predicted effective conductivity of
various portions of the target reservoir can be used to locate
those portions of the target reservoir that are most likely to be
productive. Not intending to be bound by theory, portions of the
reservoir qualitatively characterized as having high conductivity
and/or quantitatively predicted to have relatively high
conductivity may be advantageously selected for production. For
example, relatively higher conductivity resulting from relatively
higher occurrence of natural fracture systems or paths of weakness
may allow improved fluid communication to a wellbore from the
reservoir and, thus, improved productivity and/or decreased inputs.
For example, and not intending to be bound by theory, by exploiting
reservoirs and/or portions of a reservoir having a relatively high
occurence of natural fractures, the opportunity for fluid
connection between the wellbore and surface area within the
reservoir may be improved. Therefore, by placing a well within a
reservoir and/or a portion of a reservoir having a relatively high
occurence of natural fractures and planes of weakness, upon
hydraulically fracture stimulating the reservoir, access may be to
the surface associated with the natural fractures. For example,
FIG. 8 is a three-dimensional representation of the data reservoir
illustrated in FIGS. 2, 4, 5, 6, and 7, where the data reservoir is
also the target reservoir. The example of FIG. 8 illustrates the
placement of potential wells 810 within the target reservoir so as
to penetrate portions of the target reservoir having a relatively
high occurence of natural fractures and planes of weakness.
[0058] In various embodiments, the predicted effective conductivity
of various portions of the target reservoir can be used to select
optimum well locations, select optimum well parameters, such as
well coordinates, well orientation, well depth, number of well
stages or zones, stimulation design and placement, and the like.
For example, in a reservoir found to have a layer of rock that has
very low degree of natural fracturing, the lack of natural
fracturing may act as a barrier to hydraulic fracture stimulation,
which in turn may limit the amount of reservoir surface area to
which fluid connectivity can be provided. As a result, a horizontal
well may be placed so as to avoid the layer of rock, for example,
either well above or below this barrier. In another example, in a
reservoir found to have a relatively high degree of natural
fracturing, the spacing between wells (e.g., between horizontal
portions of a well) may be increased such that it is necessary to
drill fewer wells to develop the same amount of oil and gas.
Conversely, in a reservoir having relatively little natural
fracturing, the spacing between wells may be decreased, for
example, such that more wells are placed within reservoir to
develop the reservoir. As such, the number and placement of wells
used can be made more efficient.
[0059] Additionally or alternatively, in some embodiments the
predicted effective conductivity of various portions of the target
reservoir can be used to further inform determinations as to
existing wells, for example, such as whether to workover a well,
whether to provide stimulation treatments, whether to relocate
production zones, or the like. For example, a target reservoir may
sits within the data reservoir and/or for existing horizontal wells
within the data reservoir. As an example, an existing horizontal
well that has, historically, not produced much oil and/or gas but
is found to have been placed within a reservoir that having
relatively high effective conduciting may justify stimulation
operations to significantly increase the value of the well.
[0060] In some embodiments, the methods disclose herein may further
comprise placing a well according to the predicted, optimum
location. In such embodiments, a well may be drilled using a
drilling rig positioned on the earth's surface and extending over
and around a wellbore that penetrates reservoir for the purpose of
recovering hydrocarbons. The well may be drilled into the reservoir
using any suitable drilling technique. In some embodiments, the
drilling rig comprises a derrick with a rig floor through which a
work string extends downward from the drilling rig into the
well.
[0061] The well may have any suitable characteristics, according
the designs developed according to the disclosed methods. For
example, the well may be horizontal or vertical, for example,
extending substantially vertically away from the earth's surface
over a vertical wellbore portion, or may deviate at any angle from
the earth's surface over a deviated or horizontal wellbore portion.
In various embodiments, portions or substantially all of the well
may be vertical, deviated, horizontal, and/or curved. In some
instances, at least a portion of the well may be lined with a
casing that is secured into position against the reservoir in a
conventional manner using cement, or may include portions that are
uncased.
[0062] In some embodiments, the methods disclosed herein may be
particularly advantageous in that these methods are able to
accurately predict effective conductivity values with very little
data. For example, in many instances less than five minutes of
monitoring data can be utilized to predict an effective
conductivity. While many tests or data collection methods may take
days or weeks to complete in order to calculate actual reservoir
parameters, the present embodiments may utilize a very small slice
of the data to predict the effective conductivity of a
reservoir.
[0063] While the above method has been described primarily as
applicable to predicting an effective conductivity, persons of
ordinary skill in the art, upon viewing this disclosure, will
recognize that other factors can be incorporated to heighten the
predictive value of the method. For example, characteristics
affecting the extractable amount of oil and gas within a target
reservoir can be included to optimize drilling locations.
[0064] Further, the above method can be used to quantify an amount
of depletion in a target reservoir. The quantity of pressure
depletion in a reservoir is a key characteristic affecting drilling
decisions.
[0065] Below are examples of a practical application of the present
embodiments:
EXAMPLE 1
[0066] In the Jonah field Section 35 a group of 48 wells were
selected, drilled, and completed in the Mesaverde formation within
the central fault block. No pre-fracture diagnostic testing was
evaluated for any of these well stages. The fracture stimulation
stages within each well was located from 13,100 feet to 13,400
feet, and were then designated as the priority group within the
data reservoir to be evaluated.
[0067] The stages were then reviewed to determine the validity of
the data to ensure they could be compared relative to one another.
Three stages were eliminated from the priority group due to screen
outs. One stage was eliminated because the pressure gauge readings
appeared to be erratic and therefore suspected to be the result of
equipment malfunctioning. Four other stages were eliminated since
over 8 pounds per gallon of proppant was present within the
fracture stimulation fluid at the end of the job as compared to a
typical job, which ends with approximately 2 pounds per gallon
proppant concentration.
[0068] The instantaneous shut in pressure was evaluated for each
stage and based on this data it was determined that all of the
stages had a similar reservoir pressure. Therefore a total of 40
wells made up the validated priority group of well stages to be
analyzed. A dimensionlessly scaled analysis was conducted on the
stimulation data for each stage. The rate of change of the
dimensionless data was then calculated from the 0.01 and 0.03
dimensionless time scale.
[0069] The rate of change of the leak off pressure versus time plot
was simultaneously calculated for the same data points used and
then compared to the rate of change calculated from the
dimensionless data analysis. The comparative analysis correlated
without identifying any outlier data points.
[0070] The dimensionless data leak off rate data was then organized
into a statistical distribution with 26 data points falling within
one standard deviation of the mean and designated as having a
medium effective post-fracture conductivity value. Eight data
points were plotted in the lower quartile and were predicted to
have a low effective post-fracture conductivity and the remaining
six data points were located within the upper quartile and were
designated to have high effective post fracture conductivity
values.
[0071] The effective post fracture conductivity (kh) values for
each well stage were then located within a 3D earth model of the
data reservoir. The 3D earth model contained production logs,
tracer surveys, electric line log measurements, drill mud log data,
total organic content measurements from drill cuttings analyses,
and high resolution whole core analyses on certain wells. The 3D
earth model was then loaded into a 3D seismic interpretation
project as a series of overlays within 3D space. A principal
component analysis was then completed to identify significant
seismic attributes that correspond to the earth model data set,
including the effective conductivity (kh) values.
[0072] Multi-attribute and multivariate regression self organizing
3D volumes were then created that qualitatively illustrate the data
reservoir. The 3D volumes were then compared on a qualitative basis
with actual well production and the available production logs and
tracer surveys plotted in 3D space. The comparison correlated very
well whereas higher quality reservoir volumes corresponded directly
with greater stage and overall well production of reservoir
fluids.
[0073] The multi-attribute multivariate constructed 3D seismic
volume described above was then used to predict higher quality 3D
volumes within a target reservoir located in Section 36 of the
Jonah field. The target reservoir is adjacent to the data reservoir
evaluated. Wells were then located and drilled in Section 36 within
the higher quality reservoir volumes which demonstrated higher
effective conductivity reservoir rock. The new drilled wells
performed within the top quartile of well production performances
within the field as a whole. The new wells significantly improved
the project economics through greater well productivity and the
elimination of two wells that no longer needed to be drilled, which
also reduced the environmental foot print in the field.
EXAMPLE 2
[0074] The validated priority group of stages selected in Section
35 of the Jonah field as discussed above were then evaluated based
on diagnostic test data gathered on ten of the 40 stages in the
data reservoir before they were fracture stimulated. The diagnostic
test data set included six diagnostic injection formation tests
(DFIT) and ten fluid efficiency tests (FET).
[0075] The instantaneous shut in pressure (ISIP) for the DFIT tests
were plotted and compared to the FET ISIP data for each specific
well stage. The comparison plotted as a straight line with an
approximate slope of one. This further validated that the same
reservoir was being compared during both types of tests, which were
completed approximately one month apart for each applicable stage.
A dimensionless data analysis was then conducted on all of the
diagnostic test data for each stage. Reservoir pressure and
reservoir conductivity (kh) were then deterministically calculated
for each well stage that had DFIT data, which is a requirement for
these calculations.
[0076] The rate of change of the dimensionless data curve was then
calculated from the 0.01 and 0.03 dimensionless time for both the
DFIT and FET tests. The rate of change of the leak off pressure
versus time plot was simultaneously calculated for the same
diagnostic test data points and then compared to the rate of change
calculated from the dimensionless data analysis. The comparative
analysis correlated without identifying any outlier data points.
The rate of change for both the dimensionless data and the pressure
time data sets were then cross plotted and formed approximately a
straight line with a slope of one.
[0077] This further validated that the early time data for the DFIT
and FET diagnostic tests could be used as a single data set. The
DFIT rate of change from the dimensionless data analyses of well
stages was then plotted versus the calculated reservoir
conductivity from the DFIT test. A best fit curve was then plotted
within this data set and then described by a polynomial equation
that solved for the reservoir conductivity axis of the plot. The
dimensionless data rate of change of the FET data was then inputted
into the polynomial equation to solve for conductivity.
[0078] Since FET data was used in the equation we refer to the
solution as an effective pre-fracture conductivity value. The
calculated effective pre-fracture conductivity values from all of
the FET tests were then plotted versus their respective
dimensionless data rate of change values. The best fit curve was
then adjusted slightly upwards to better fit the entire data set
and the polynomial equation describing the adjusted curve was
solved for effective pre-fracture conductivity. The same analysis
of the pressure versus time data was completed and compared to the
dimensionless data analysis. Both the dimensionless data and the
pressure versus time analysis correlated but the dimensionless data
analysis proved to have better resolution of the data and
subsequently the calculated effective pre-fracture conductivity
values,
[0079] It was therefore decided to only integrate the dimensionless
data predicted effective pre-fracture conductivity values into the
3D earth model and seismic project described in Example 1, A
principal component analysis was then repeated to identify
significant seismic attributes that correspond to the earth model
data set, including the effective pre-fracture conductivity (kh)
values. Multi-attribute and multivariate regression self organizing
3D volumes were then recreated for the data reservoir.
[0080] The improved volumes illustrated greater resolution after
the effective pre-fracture conductivity values were included, since
these were discrete values rather than the high, medium, or low
conductivity values described by the effective post-fracture
conductivity methodology. The greater resolution of reservoir
quality was evident in that it improved the match between reservoir
quality and well production as measured with production logs and
tracer surveys. The multi-attribute multivariate constructed 3D
seismic volume described above was then used to predict with
greater resolution the higher quality 3D volumes within a target
reservoir located in Section 36 of the Jonah field.
[0081] Additional wells were then more precisely located and
drilled in Section 36 within the higher quality reservoir volumes.
The new drilled wells are predicted to perform ten percent better
than the wells located with just post-fracture data as described in
Example 1.
EXAMPLE 3
[0082] The validated priority group of stages selected in Section
35 of the Jonah field as discussed above were then evaluated using
both the post-fracture and pre-fracture data. The instantaneous
shut in pressure (ISIP) for the pre-fracture diagnostic tests were
plotted and compared to the post-fracture ISIP data for each
specific well stage. The comparison plotted as a straight line with
a slope of approximately one.
[0083] The 1:1 plot confirmed that the same polynomial equation
describing the best fit curve solving for effective conductivity
(developed with pre-fracture data) can be used with the rate of
change calculated from the dimensionless data analysis of the
post-fracture data. Therefore, the post-fracture dimensionless data
rate of change calculated in Example 1 was inserted into the
polynomial equation and an effective post-fracture conductivity
value was calculated for each well stage in the validated priority
group.
[0084] The 3D earth model was then repopulated with only the
dimensionless data predicted effective conductivity values from
both the pre-fracture and post-fracture data sets for each of the
applicable well stages. The revised 3D earth model was then
reloaded into our 3D seismic interpretation project as a series of
overlays within 3D space. A principal component analysis was then
completed to identify significant seismic attributes that
correspond to the earth model data set, including the effective
conductivity (kh) values. Multi-attribute and multivariate
regression self organizing 3D volumes were then created that
qualitatively illustrate the data reservoir.
[0085] The 3D volumes were then compared on a qualitative basis
with actual well production and the available production logs and
tracer surveys plotted in 3D space. The comparison correlated even
closer than the previous models. The improved volumes illustrated
even greater resolution since the effective post-fracture
conductivity values were now calculated using the polynomial
effective conductivity equation rather than the high, medium, or
low conductivity values described by the effective post-fracture
conductivity methodology.
[0086] The revised and improved multi-attribute multivariate
constructed 3D seismic volume described above was then used to
predict with greater resolution the higher quality 3D volumes
within a target reservoir located in Section 36 of the Jonah field.
Additional wells were then even more precisely located and drilled
in the target reservoir within the highest quality reservoir
volumes. The volumes defined the most economically attractive
reservoir. The new drilled wells are predicted to perform an
additional 30 percent better than the wells located with just
post-fracture methodology described.
[0087] While the present subject matter has been described with
emphasis on the embodiments, it should be understood that within
the scope of the appended claims, the embodiments might be
practiced other than as specifically described herein.
* * * * *