U.S. patent application number 14/526288 was filed with the patent office on 2016-04-28 for completions index analysis.
This patent application is currently assigned to EOG RESOURCES, INC.. The applicant listed for this patent is EOG RESOURCES, INC.. Invention is credited to Oscar A. Bustos, Evan Daniel Gilmore, Christopher Michael James, Eric Robert Matus.
Application Number | 20160115780 14/526288 |
Document ID | / |
Family ID | 55791588 |
Filed Date | 2016-04-28 |
United States Patent
Application |
20160115780 |
Kind Code |
A1 |
James; Christopher Michael ;
et al. |
April 28, 2016 |
COMPLETIONS INDEX ANALYSIS
Abstract
A method for determining a hydrocarbon-bearing reservoir quality
prior to a hydraulic fracture treatment based on completions index
is disclosed. The method comprises a step performing a test
determining a hydraulic pressure at which a hydrocarbon-bearing
reservoir will begin to fracture by pumping a fluid in a wellbore,
wherein the wellbore extends from a surface to the reservoir and
the wellbore has one or more perforations in communication with
reservoir; a step generating a pressure transient in the wellbore,
the pressure transient travels from the surface to the reservoir
through the perforations and reflects back the surface after
contacting the reservoir; a step measuring response of the pressure
transient at sufficiently high sampling frequency; a step
determining fracture hydraulic parameters of the perforations and
the reservoir using the measured response; and optimizing a
stimulation treatment to the reservoir based on the determined
fracture hydraulic parameters.
Inventors: |
James; Christopher Michael;
(San Antonio, TX) ; Bustos; Oscar A.; (San
Antonio, TX) ; Gilmore; Evan Daniel; (Forth Worth,
TX) ; Matus; Eric Robert; (Midland, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
EOG RESOURCES, INC. |
Houston |
TX |
US |
|
|
Assignee: |
EOG RESOURCES, INC.
Houston
TX
|
Family ID: |
55791588 |
Appl. No.: |
14/526288 |
Filed: |
October 28, 2014 |
Current U.S.
Class: |
73/152.51 |
Current CPC
Class: |
E21B 43/26 20130101;
E21B 49/008 20130101 |
International
Class: |
E21B 47/06 20060101
E21B047/06 |
Claims
1. A method for determining a hydrocarbon-bearing reservoir quality
comprising: performing a test determining a hydraulic pressure at
which a hydrocarbon-bearing reservoir will begin to fracture by
pumping a fluid in a wellbore, wherein the wellbore extends from a
surface to the reservoir and the wellbore has one or more
perforations in communication with the reservoir; generating a
pressure transient in the wellbore, the pressure transient
traveling from the surface to the reservoir through the
perforations and reflecting back to the surface after contacting
the reservoir; measuring the response of the pressure transient at
a sufficiently high sampling frequency; determining fracture
hydraulic parameters of the perforations and the reservoir using
the measured response; and optimizing a stimulation treatment to
the reservoir based on the determined fracture hydraulic
parameters.
2. The method according to claim 1, wherein the stimulation
treatment is a fracture treatment.
3. The method according to claim 1, wherein the step of determining
fracture hydraulic parameters of the perforations and the reservoir
using the measured response comprises comparing the measured
response to simulated responses generated by an electrical
model.
4. The method according to claim 3, wherein the step of determining
the fracture hydraulic parameters of the perforations and reservoir
comprising representing the fracture hydraulic parameters as a
lumped impedance component including a resistive element and a
capacitive element.
5. The method according to claim 1, wherein the step of determining
the fracture hydraulic parameter of the perforations comprises
determining a flow resistance of the perforations.
6. The method according to claim 5, wherein the step of determining
the fracture hydraulic parameter of the reservoir comprises
determining a completions index of the reservoir.
7. The method according to claim 6, wherein the step of determining
the fracture hydraulic parameter of the perforations is performed
prior to the step of determining the fracture hydraulic parameter
of the reservoir.
8. The method according to claim 6, further comprises generating
additional pressure transients to determine closure of fractures in
the reservoir and reduction in completions index versus time.
9. The method according to claim 1, wherein the step of determining
fracture hydraulic parameters of the perforations and the reservoir
using the measured response comprises numerically optimizing a
neural network by extracting variables from the measured response
as inputs to the neural network.
10. The method according to claim 9, wherein the variables are
depth of a drilling stage, rate of decay, slope ratio of the
initial reflection to the incident reflection, and initial pressure
drop in the test of determining a hydraulic pressure at which the
reservoir will begin to fracture.
11. The method according to claim 1, wherein the step of generating
a pressure transient generates a pressure transient by reducing or
stopping the pump rate of a pressure pumping equipment.
12. The method according to claim 1, wherein the step of generating
a pressure transient generates a pressure transient by rapidly
opening and closing a valve or by employing a pressure oscillator
or a mechanical shutter.
13. The method according to claim 1, wherein the determined
fracture hydraulic parameter of the reservoir is related to a
surface area inside the reservoir.
14. The method according to claim 1, further comprising determining
whether there is a hole in a casing of the wellbore.
15. The method according to claim 1, wherein the step of generating
a pressure transient occurs during the first 15 to 30 seconds of
the test.
16. The method according to claim 1, wherein the sufficiently high
sampling frequency is more than 2 Hz.
17. The method according to claim 1, wherein the step of optimizing
a stimulation treatment to the reservoir based on the determined
fracture hydraulic parameters comprises adjusting the volume,
properties or rate of a fracturing fluid required to fracture the
reservoir or the volume or type of proppant carried by the
fracturing fluid, or omitting the stimulation treatment.
18. The method according to claim 1, wherein the test is a leakoff
test.
19. A method for determining a hydrocarbon-bearing reservoir
quality comprising: performing a leak-off test by pumping a fluid
in a wellbore using a pressure pumping equipment, wherein the
wellbore extends from a surface to a hydrocarbon bearing reservoir
and the wellbore has one or more perforations in communication with
the reservoir; creating a pressure transient in the wellbore, the
pressure transient travels from the surface to the reservoir
through the perforations and reflects back the surface after
contacting the reservoir; measuring a pressure-time plot at
sufficiently high sampling frequencies of the pressure transient
traveling to the reservoir and reflecting back the surface;
comparing the pressure-time plot to electrical models representing
different hydrocarbon bearing reservoirs and wellbores or
numerically optimizing an electrical model to match the
pressure-time plot; determining a flow resistance of the
perforations between the wellbore and the reservoir based on the
comparison or the numerical optimization; and determining a
completions index of the reservoir based on the comparison or the
numerical optimization.
20. The method according to claim 19, the method may further
comprise optimizing a stimulation treatment to the reservoir based
on the determined flow resistance and completions index.
21. The method according to claim 20, wherein the stimulation
treatment being optimized is a fracture treatment.
22. The method according to claim 19, wherein the step of
determining the flow resistance based on the comparison is
determined by a rate of decay of the pressure-time plot.
23. The method according to claim 19, wherein the step of
determining the completions index based on the comparison is
determined by using shape of the pressure-time plot to solve a
capacitance in the electrical model.
24. The method according to claim 19, wherein the step of
determining the completions index based on the numerical
optimization is determined by a depth of the perforations for a
given stage, the flow resistance, an initial pressure drop in the
leak-off test, and a slope ratio of a reflected rate of change to
an initial rate of change.
25. The method according to claim 24, the flow resistance, the
initial pressure drop, and the slope ratio are obtained from the
pressure-time plot.
26. The method according to claim 19, the step of measuring a
pressure-time plot at sufficiently high sampling frequencies is
measured at frequencies higher than 2 hertz up to 500 Hz.
27. The method according to claim 21, wherein the step of
optimizing a stimulation treatment to the reservoir comprises how
much fluid and proppant are required to fracture the reservoir.
28. The method according to claim 21, further comprising generating
multitude pressure transients during the course of the fracture
treatment to monitor the stimulation treatment on the
reservoir.
29. The method according to claim 19, wherein the step of
determining the completions index of the reservoir based on
numerical optimization comprises interpolation by a neural
network.
30. The method according to claim 19, wherein the step of
determining the completions index of the reservoir based on
numerical optimization comprises optimizing a numerical model to
match the measured pressure-time plot.
31. The method according to claim 19, wherein the step of
determining the flow resistance based on numeral optimization and
the step of determining the completions index of the reservoir
based on numerical optimization occur simultaneously.
32. The method according to claim 19, further comprising generating
multitude pressure transients to compare most recently determined
flow resistance and completions index with prior determined flow
resistance and completions index.
33. The method according to claim 19, wherein the step of
determining the completions index comprises comparisons with
previously obtained completions indices.
34. The method according to claim 19, wherein the electrical model
comprises a nodal arrangement having impedance and resistive
components representing an area of the reservoir, a lateral portion
of the wellbore, and the flow resistance.
35. The method according to claim 19, wherein the method is
performed after a fracture treatment.
36. The method according to claim 19, wherein the determined flow
resistance and completions index are elements of a larger lumped
electrical component.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to a method for determining a
hydrocarbon-bearing reservoir quality, and in particular, to a
method for determining a hydrocarbon-bearing reservoir quality
prior to a hydraulic fracture treatment based on a completions
index.
BACKGROUND OF THE INVENTION
[0002] Hydraulic fracturing is a technique of fracturing rock
formations by a pressurized fluid in order to extract oil and
natural gas contained in the formations. A fluid, which usually is
water mixed with sand and chemicals, is injected into a wellbore
under considerable pressure to create fractures in the formations.
When the pressure is removed from the wellbore, the sand props the
fractures open allowing the oil and gas contained in the formations
to more readily flow into the well for extraction. This technique
has revolutionized oil and gas development, especially is shale
formations, because it permits extraction of formerly inaccessible
hydrocarbons. As a result, it has helped push U.S. oil production
to a new high and generate billions of revenues to mineral rights
owners, oil companies, as well federal, state, and local
governments.
[0003] Hydraulic fracturing, however, can be a very expensive
process, especially if the quality of the formations is unknown. In
a horizontally drilled oil well, hydraulic fracturing generally is
performed in several stages along the horizontal portion of the
well. Typically, the horizontal portion of the well is stimulated
in stages about every 200 to 250 feet. Although the horizontal
portion of the well generally extends through a given hydrocarbon
bearing formation, the lithology or rock quality may vary along the
length of the wellbore. When oil companies conduct a frac treatment
at a section of the formations that is sub-optimal, the stimulation
may be ineffective or produce marginal gains in productivity for
that particular stage. Assuming that the average cost for each
hydraulic fracture treatment is approximately $100,000 and that
some formations may have up to 80% of its sections be sub-optimal,
the cost and time spent in fracturing sub-optimal sections or in
determining whether to move onto another section can be
substantial. In one year, an energy consulting company estimated
that about $31 billion was spent in sub-optimal fracturing across
26,100 U.S. oil wells.
[0004] Moreover, even if the oil drilling companies treat a section
of the formation that happens to be optimal, the treatments may not
have been the optimal size. In other words, the treatment may have
been too small given the favorable rock qualities that existed for
that particular stage and that the well could have been even more
productive and the return on the investment of the stimulation
could have been even higher had a larger stimulation been pumped,
or had a different stimulation fluid or amount of proppant been
pumped. As such, knowing the quality of the formations prior to a
hydraulic fracture treatment is beneficial to stimulation
treatments.
[0005] A method called Distributed Fiber Optic Sensing has been
developed to provide this information. This method is based on
either temperature or acoustic sensing. In the method based on
temperature sensing, a unit including a laser source and a
photodetector is placed on the surface and a glass fiber is
permanently installed in the well. The laser source sends laser
pulses down the glass fiber and the temperature of the formations
can affect the glass fiber and locally change the characteristics
of light transmission in the glass fiber. The photodetector
measures the laser light reflections from different spots in the
glass fiber due to the temperature and the spectrum of the laser
light reflections can used to determine the properties of the
formations. The method based on acoustic sensing is similar to the
temperature sensing one except that this method employs a unit that
includes an acoustic signal generator and an acoustic signal
receiver and that this method measures the reflected acoustic
signals based on the strain or pressure of the formations exerted
on and along various points of the glass fiber. The measured
acoustic signals may have various amplitude, frequency, and phase
attributes that can also be used to determine the properties of the
formations.
[0006] The Distributed Fiber Optic Sensing method, however, has
several drawbacks. First, this method requires running a glass
fiber into the well that complicates the installation process.
Second, this method usually costs around $600,000 to implement and
the investment is only for one single well and is permanent. Third,
this method is not economically practical on smaller reservoir
wells. Fourth, to protect the fragile glass fiber, the glass fiber
is typically placed within a stainless steel sheath that can
attenuate the temperature or strain response, reducing accuracy of
the measurement.
[0007] Accordingly, there is a need for an improved method for
determining the quality of the rock formations prior to a hydraulic
fracture treatment.
SUMMARY OF THE INVENTION
[0008] In accordance with one embodiment of the present invention,
a method for determining a hydrocarbon-bearing reservoir quality
prior to a hydraulic fracture treatment based on a completions
index is described.
[0009] The method comprises performing a test determining a
hydraulic pressure at which a hydrocarbon-bearing reservoir will
begin to fracture by pumping a fluid in a wellbore, wherein the
wellbore extends from a surface to the reservoir and the wellbore
has one or more perforations in communication with the reservoir;
generating a pressure transient in the wellbore, the pressure
transient traveling from the surface to the reservoir through the
perforations and reflecting back the surface after contacting the
reservoir; measuring the response of the pressure transient at
sufficiently high sampling frequency; determining the fracture
hydraulic parameters of the perforations and the reservoir using
the measured response; and optimizing a stimulation treatment to
the reservoir based on the determined fracture hydraulic
parameters.
[0010] In a preferred embodiment of the invention, the stimulation
treatment being optimized is a fracture treatment.
[0011] In one embodiment, the step of determining fracture
hydraulic parameters of the perforations and the reservoir using
the measured response comprises comparing the measured response to
simulated responses generated by an electrical model.
[0012] According to a preferred embodiment of the invention, the
step of determining the fracture hydraulic parameters of the
perforations and the reservoir comprising representing the fracture
hydraulic parameters as a lumped impedance component containing a
resistive element and a capacitive element.
[0013] According to another preferred embodiment of the invention,
the step of determining the fracture hydraulic parameter of the
perforations comprises determining a flow resistance of the
perforations.
[0014] According to another preferred embodiment of the invention,
the step of determining the fracture hydraulic parameter of the
reservoir comprises determining a completions index of the
reservoir.
[0015] In a preferred embodiment, the step of determining the
fracture hydraulic parameter of the perforations is performed prior
to the step of determining the fracture hydraulic parameter of the
reservoir.
[0016] In addition to the above steps, the method may further
comprise generating additional pressure transients to determine
closure of fractures in the reservoir and reduction in completions
index versus time.
[0017] In the step of determining fracture hydraulic parameters of
the perforations and the reservoir using the measured response, the
step according to one embodiment may comprise numerically
optimizing a neural network by extracting variables from the
measured response as inputs. The variables are depth of the
perforations for a given stage, rate of decay, slope ratio of the
initial reflection to the incident reflection, and initial pressure
drop in the test of determining a hydraulic pressure at which the
reservoir will begin to fracture.
[0018] In the step of determining fracture parameters of the
perforations and the reservoir using the measured response, the
step according to another embodiment may alternatively comprise
numerically simulating a transient and using optimization methods
through history matching to determine fracture hydraulic
parameters.
[0019] In the step of generating a pressure transient, the step
generates a pressure transient by reducing or stopping the pump
rate of pressure pumping equipment.
[0020] In the step of generating a pressure transient, the pressure
transient may be generated by stopping or reducing the pump rate of
the surface pressure pumping equipment, rapidly opening and closing
a valve, or employing a pressure oscillator or a mechanical
shutter.
[0021] In the step of determining fracture hydraulic parameter of
the reservoir, the determined fracture hydraulic parameter is
related to a surface area inside the reservoir.
[0022] In addition to the above steps, the method may further
comprise determining whether there is a hole in a casing of the
wellbore.
[0023] According to a preferred embodiment, the pressure transient
is generated within the first 15 to 30 seconds of the test
determining a hydraulic pressure at which a hydrocarbon-bearing
reservoir will begin to fracture.
[0024] In the step of measuring response of the pressure transient
at sufficiently high sampling frequency, the sufficiently high
sampling frequency is more than 2 Hz.
[0025] The step of optimizing a stimulation treatment to the
reservoir based on the determined fracture hydraulic parameters
preferably comprises adjusting the volume, properties or rate of
the fracturing fluid required to fracture the reservoir, adjusting
the volume or type of the proppant in the fracturing fluid, or
omitting a particular stimulation treatment.
[0026] In one embodiment of the invention, the performed test is a
leakoff test.
[0027] In accordance with another embodiment of the present
invention, a method for determining a hydrocarbon bearing reservoir
quality is described.
[0028] The method comprises performing an initial leak-off test by
pumping a fluid in a wellbore using a pressure pumping equipment,
wherein the wellbore extends from a surface to a hydrocarbon
bearing reservoir and the wellbore has one or more perforations in
communication with the reservoir; creating a pressure transient in
the wellbore, the pressure transient travels from the surface to
the reservoir through the perforations and reflects back the
surface after contacting the reservoir, measuring a pressure-time
plot at sufficiently high sampling frequencies of the pressure
transient traveling to the reservoir and reflecting back the
surface; comparing the pressure-time plot to electrical models
representing different hydrocarbon bearing reservoirs and wellbores
or numerically optimizing an electrical model to match the
pressure-time plot; determining a flow resistance of the
perforations between the wellbore and the reservoir based on the
comparison or the numerical optimization; and determining a
completions index of the reservoir based on the comparison or the
numerical optimization.
[0029] In addition to the above steps, the method may further
comprise optimizing a stimulation treatment to the reservoir based
on the determined flow resistance and completions index.
[0030] In a preferred embodiment of the invention, the stimulation
treatment being optimized is a fracture treatment.
[0031] In the step of determining the flow resistance based on the
comparison, the flow resistance is determined by a rate of decay of
the pressure-time plot.
[0032] In the step of determining the completions index based on
the comparison, the completions is determined by using shape of the
pressure-time plot to solve a capacitance in the electrical
models.
[0033] In the step of determining the completions index based on
the numerical optimization, the completions index is determined by
a depth of the perforations for a given stage, the flow resistance,
an initial pressure drop in the leak-off test, and a slope ratio of
a reflected rate of change to an initial rate of change.
[0034] In one embodiment of the invention, the flow resistance, the
initial pressure drop, and the slope ratio are obtained from the
pressure-time plot.
[0035] In one embodiment of the invention, the flow resistance and
the slope ratio are determined by finding values through numerical
simulation to match the pressure-time plot. The slope ratio may be
used to calculate the completions index.
[0036] In one embodiment of the invention, the step of measuring a
pressure-time plot at sufficiently high sampling frequencies is
measured at frequencies higher than 2 Hz up to 500 Hz.
[0037] In the step of optimizing the stimulation treatment to the
reservoir, the step comprises how much fluid and proppant are
required to fracture the reservoir.
[0038] In another embodiment of the invention, the method may
further comprise generating multitude pressure transients during
the course of a fracture treatment to monitor or understand the
effectiveness of the stimulation treatment on the reservoir.
[0039] In the step of determining the completions index of the
reservoir based on numerical optimization, the step comprises
interpolation by a neural network.
[0040] In another embodiment of the invention, the step of
determining the completions index of the reservoir based on
numerical optimization comprises optimizing a numerical model to
match the measured pressure-time plot.
[0041] In one embodiment of the invention, the step of determining
the flow resistance based on numeral optimization and the step of
determining the completions index of the reservoir based on
numerical optimization occur simultaneously.
[0042] In addition to the above steps, the method may further
comprise generating multitude pressure transients to compare most
recently determined flow resistance and completions index with
prior determined flow resistance and completions index.
[0043] In the step of determining the completions index, the step
comprises comparisons with previously obtained completions
indices.
[0044] In one embodiment of the invention, the electrical model
comprises a nodal arrangement having impedance and resistive
components representing an area of the reservoir, the wellbore, and
the flow resistance.
[0045] In another embodiment of the invention, the method is
performed after a fracture treatment.
[0046] Since the present invention determines a hydrocarbon-bearing
reservoir quality by analyzing a completions index, the present
invention is also known as Completions Index Analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
[0047] For the purposes of illustrating the present invention,
there is shown in the drawings a form which is presently preferred,
it being understood however, that the invention is not limited to
the precise form shown by the drawing in which:
[0048] FIG. 1 shows one embodiment of the method for determining a
hydrocarbon-bearing reservoir quality.
[0049] FIGS. 2 and 3 show an example of a fracturing treatment
having a leak-off test performed at the beginning of the fracturing
treatment, an initial water hammering effect of the leak-off test,
and a final water hammering effect after the fracturing
treatment.
[0050] FIG. 4 is a closer or detailed view of the leak-off test
shown in FIGS. 2 and 3.
[0051] FIG. 5 shows an example of measured pressure transient
response.
[0052] FIG. 6 shows an example of multiple pressure transients
generated during the pressure decline of the leakoff test.
[0053] FIG. 7 shows that the measured pressure transient response
can identify a hydrocarbon-bearing reservoir quality.
[0054] FIG. 8 shows that the measured pressure transient response
can determine if there is a hole in the casing.
[0055] FIG. 9 shows a small section of an equivalent per unit
length electrical model of a hydraulic wellbore/fracture
system.
[0056] FIG. 10 shows matching between an electrical model response
and an actual measured response for two different stages.
[0057] FIG. 11 shows the comparison of high Efficiency Coefficient
and low Efficiency Coefficient.
[0058] FIG. 12 shows the comparison of high completions index and
low completions index.
[0059] FIG. 13 shows the slope ratio variable for calculating the
completions index and the correlation developed between slope
ratios, initial slope and stage depth.
[0060] FIG. 14 shows how the completions index changes throughout a
fracturing treatment.
[0061] FIG. 15 shows changes in Efficiency Coefficient and
completions index from initial water hammering to final water
hammering.
DETAILED DESCRIPTION OF THE INVENTION
[0062] Referring to FIG. 1, one embodiment of the method for
determining a hydrocarbon-bearing reservoir quality 100 is
illustrated. The method 100 comprises steps of performing a test
determining a hydraulic pressure at which the reservoir will begin
to fracture 110, generating a pressure transient during the test
120, measuring response of the pressure transient 130, determining
fracture hydraulic parameters using the measured response 140, and
optimizing a stimulation treatment to the hydrocarbon-bearing
reservoir based on the determined fracture hydraulic parameters
150.
[0063] The step of performing a test determining a hydraulic
pressure at which the reservoir will begin to fracture, or a
leak-off test, 110 involves pumping a fluid, for example a
hydraulic fracturing fluid, into a wellbore using a pressure
pumping equipment. The wellbore extends from a surface to a
reservoir and has one or more perforations extending through the
production casing in communication with the reservoir. The pressure
pumping equipment may be any equipment that is capable of pumping
the fracturing fluid at a pressure into the wellbore. In addition
to determining the hydraulic pressure at which the reservoir will
begin to fracture, the leak-off test can also determine if the
perforations are sufficiently open to establish communication with
the reservoir. From the leak-off test, the ball seating pressure,
the fracturing gradient (FG) of the formation, and the fracture
closure time can be determined. A leak-off test is illustrated in
FIGS. 2 and 3.
[0064] FIGS. 2 and 3 show an example of a fracturing treatment
having a leak-off test performed at the beginning of the fracturing
treatment, an initial water hammering effect of the leak-off test,
and a final water hammering effect after the fracturing treatment.
Referring to FIG. 2, the fracturing treatment in which the leak-off
test is performed has a duration of approximately three hours from
start to finish. FIG. 3 is a breakdown of FIG. 2 that shows the
treatment rate (the top graph), the treatment pressure (the middle
graph), and the proppant concentration (the bottom graph) of the
fracturing treatment. The treatment rate in this example is
approximately 32 barrels per minute between about 0.45 hour and
3.15 hour. The treating pressure is between 2,000 and 3,000 PSI
from about 0.45 hour to 3.15 hour. The proppant concentration is
between 0.5 and 1 pound per gallon (PPA) from about 0.6 hour to 0.9
hour, and between 1.5 and 2 PPA from about 0.9 hour to 3 hour. The
leak-off test is labeled as the "acid and leak-off test" in FIG. 2
or the graph prior to the "rise" or "step" at approximately 0.45
hour in the treatment rate and the treatment pressure graphs. The
leak-off test is initiated and concluded within approximately 30
minutes, or at about 0.5 hour, from the start of the fracturing
treatment. After the leak-off test, and for the remaining two and
half hours, water with chemicals is pumping into the wellbore and
proppant is slowly added into the water to inject the stimulation
fluid into the fractures in the reservoir.
[0065] Near the end of the leak-off test, a response or water
hammering effect can be measured by generating a pressure transient
and monitoring how the pressure transient declines with time. The
very first 15 to 30 seconds after generating the pressure transient
shows a lot of noise when the pressure transient is measured under
low sampling frequency, such as 1 Hz, and that is the water
hammering effect of the pressure transient. The pressure transient
propagates to the perforations, reflects back to the surface, and
travels in this manner back and forth until it attenuates
completely. This response is shown as the initial water hammering
graph in FIGS. 2 and 3. The same response may also be measured at
the end of the fracturing treatment and is shown as the final water
hammering graph in the same figures. The final water hammering
graph shows more response or bounces because the fractures in the
reservoir have been opened.
[0066] FIG. 4 is a closer or detailed view of the leak-off test
shown in FIGS. 2 and 3. When the water hammering effect is measured
at sufficiently high sampling frequency, such as sampling
frequencies higher than 2 Hz up to 500 Hz, more water hammering
effect can be seen from the measurement as shown in the plot
labeled as "Water Hammering." The shape of the water hammering
effect signal is directly depending upon the type of rock in the
reservoir in communication with the perforations and is an
indication of the rock quality. Therefore, when the water hammering
effect signal is showing a good shape, i.e., more fluctuations and
slower attenuation, the oil company can pump in more stimulation
fluid in that stage to extract more oil and gas. If the water
hammering effect is showing a bad signal, i.e., less fluctuations
and faster attenuation, the oil company can skip that stage or
reduce the treatment size for that stage saving thousands of
dollars in fracturing treatment and move onto the next stage. As
such, the present invention provides real-time knowledge about the
rock quality that can vary in each stimulation treatment stage of
horizontal wellbores before the stimulation treatment is performed.
By understanding the water hammering effect signal in each stage,
oil companies can know when to pump more and when the pump less of
the stimulation treatment.
[0067] A leak off test, which is also known as mini-frac, is a
pumping sequence aimed to establish a hydraulic fracture, to
understand, among other things, what is the pressure required to
propagate a hydraulic fracture, and to estimate the minimum
pressure at which the hydraulic fracture closes. A critical
component of the test is the pressure monitoring after pumps as
shut-down, which is commonly known as leakoff period or pressure
fall off. During this period, fluid inside the open hydraulic
fracture will leak off into the formation, continues until this
process reaches a point that all fluid is leaked off and the
hydraulic fracture closes. Another component of the test is the
"step rate test," whereby the rate of fluid is gradually increased
at the beginning of the test until a fracture is established or
reaches the fracturing extension pressure and is reduced in a step
down fashion at the end of the test. This test allows engineers to
calculate the total pressure loss in between the rate steps so that
the total number of perforations hydraulically connected to the
fracture can be calculated. After the pumps are shut down, pressure
is monitored for some time to determine fracture hydraulic
parameters such as fracture closure pressure, presence of natural
fractures, and leakoff coefficient for the fluid. Pressure may be
monitored from several minutes to several hours and the fracture
hydraulic parameters may be determined by using a "G" function.
[0068] Referring back to FIG. 1, the step of generating a pressure
transient 120, the pressure transient is preferably generated by
stopping or substantially reducing the pump rate of the pressure
pumping equipment. But optionally, the pressure transient can be
introduced by other methods that generate a pressure wave from the
change of the inertia of the fluid, such as rapidly opening and
closing a valve on the injection well head, or by other devices,
such as a pressure oscillator or a mechanical shutter. The pressure
transient travels from the surface to the reservoir through the
perforations and reflects back to the surface after contacting the
reservoir at the speed of sound in the wellbore fluid (normally
water). Preferably, the pressure transient is generated within the
first 15 to 30 seconds of the test determining a hydraulic pressure
at which the reservoir will begin to fracture. After the pressure
transient attenuates, additional pressure transients can be
generated if desired (during the leak off test).
[0069] The response of the pressure transient, or the reflected
pressure transient, is measured at sufficiently high sampling
frequency such as at least 5 Hz. Alternatively, sample frequencies
higher than 2 Hz up to 500 Hz may be used. The response is measured
by a pressure transducer. An example of the measured response in
high sampling frequency is shown in FIG. 5. The measured response
is presented in a pressure-time plot. This measured response is
also known as the water hammering effect. The y-axis is the
pressure in pounds per square inch (PSI) and the x-axis is the time
in minutes. The rounded dot represents the number of bounces from
the surface. "t" represents the travel time of the pressure
transient in sonic speed from the surface to the reservoir and then
back to the surface through the wellbore fluid, which is the time
between a peak and a trough on the plot. Such information can also
be used to determine the distance of the perforations from the
surface since the time between bounces is directly related to the
distance to the perforations. A1 represents the initial decreasing
amplitude of the waveform and A2 represents the next decreasing
amplitude. A1 generally has a larger amplitude than A2. Based on A1
and A2, the initial rate of decay, or Efficiency Coefficient (EC),
can be calculated by the following equation:
EC= {square root over (A2/A1)}
Although FIG. 5 shows that A1 and A2 are the preferred amplitudes,
any two successive decreasing amplitudes may also be used. With the
measured response in FIG. 5, fracture hydraulic parameters such as
fracture closure pressure, fracture closure time, presence of
natural fractures, and resistance of the perforations can be
determined. Additionally, the shape of the waveform, which is
determined by the combination of t, amplitudes, slopes on the
waveform from the ISP (Instantaneous Shut-in Pressure) up to the A2
amplitude, can be used to calculate the fracture capacitance of the
reservoir. While FIG. 5 shows only one pressure transient response,
multiple pressure transients can be generated in each stimulation
stage to obtain multiple responses for determining closure of the
fractures in the reservoir. This determination is based on the
comparison of the multiple responses with each other or the
comparison of the most recently obtained response (or the most
recently obtained flow resistance and fracture capacitance, which
are described below) with a prior obtained response (or prior
obtained flow resistance and fracturing capacitance). FIG. 6 shows
an example of thirteen (13) pressure transients generated during
the pressure decline of the leakoff test. The closure of the
fractures can be observed from the reduction of the Efficiency
Coefficient of each pressure transient versus time (or until the
Efficiency Coefficient and fracture capacitance of each pressure
transient no longer change with time). The figure shows responses
measured at 1 Hz and 250 Hz sampling frequencies. Responses showing
inconspicuous fluctuations correspond to measurement at 1 Hz
sampling frequency and responses showing pronounced fluctuations
correspond to measurement at 250 Hz sampling frequency.
[0070] The measured response can identify reservoir quality.
Measured responses show significant differences for different
reservoirs or rocks having similar wellbores (for example, multiple
wells in a given field), as the pressure transient travels outside
the wellbore through the perforations of the wellbore and into the
adjacent formation/rocks. If the transient pressure did not travel
outside the wellbore, the expected responses would be similar for
comparable wellbores. This also proves that the perforations are
open and in communication with the formation. This identification
ability is shown in FIG. 7. On the left of FIG. 7, a siliceous rich
mud rock (shale), which has a lower Young's modulus and a lower
fracturing gradient, produces a lot of fluctuations or hammering
(higher capacitance). On the right of FIG. 7, a carbonate rich mud
rock (shale), which has a higher Young's modulus and a higher
fracturing gradient, produces a lot less hammering (lower
capacitance). Thus, based the amount of hammering, one can obtain
an initial impression of whether the rocks are prone to simple or
complex fracturing.
[0071] The measured response can also be used to determine if there
is a hole in the casing. Referring to FIG. 8, two wells A and B are
plotted. Well A is represented by lighter-colored dots whereas Well
B is represented by darker-colored dots. Well A has a casing
without any holes and its plot shows a pattern close to a linear
line for the various stages where pressure transients were
measured. The slope m of the linear line may be determined by the
following equation:
m = .DELTA. ( MD Top Perforation ) .DELTA. t ##EQU00001##
[0072] MD top perforation is the measured depth to the top
perforation. The slope m is measuring the change in measured
distance to the top perforation for successive stages in the well
divided by the change in time. The slope m may also be determined
by dividing the speed of sound C by 2.
[0073] Well B, on the other hand, has a casing with a hole and its
dots spread everywhere on the chart without a general pattern.
Based on the measured response, it was confirmed by a downhole
camera ran on this well that a hole was located in the casing at a
measured depth of 6987 feet.
[0074] For every hydraulic wellbore/fracture model, there is an
equivalent electrical model. The wellbore or casing may be modeled
as a lossy transmission line using resistors, capacitors, and
inductors. The values of all these electrical components are known
if one knows the depth of the well, the size of the casing, and the
temperature and type of the fluid used in the well. Some or all of
these values may be lumped into an impedance representing the
electrical property or mechanical property of the wellbore. The
generated pressure transient inside the casing may be modeled as an
input voltage on the transmission line. The perforations of the
casing, which provide communication to the reservoir, may be
modeled as a resistor. If the perforations are small, the
resistance is high and vice versa. The reservoir itself or the
quality of the reservoir may be modeled as a capacitor.
[0075] FIG. 9 represents a small section of an equivalent per unit
length electrical model. This small section of the electrical model
corresponds to a small section of the horizontal portion of the
wellbore. This small section of the electrical model is divided
into three (3) nodes ((x-1), (x), and (x+1)) and each node is
associated with a stub that is spaced along the horizontal portion
of the wellbore for example, every 30 feet. The stub associated
with each node is used to represent an area of the reservoir and
any changes in casing properties, otherwise the impedance of the
stub, Zs, is set sufficiently high such that no current flows into
the stub. A simulated response similar to the actual measured
response can be obtained from each node by generating an input
voltage, or simulated pressure transient, to the electrical model
or circuit. A stub modeling a fracture, for instance, Zs(x+1) and
Rs(x+1), represents fracture capacitance Zs(x+1) and flow
resistance Rs(x+1). Each impedance, Zs(x-1), Zs(x), Zs(x+1),
Z(x-1), Z(x), or Z(x+1), has an inductive component and a
capacitive component and they are configured to be the equivalent
circuit of a transmission line (not shown). While fracture
capacitance appears to be impedance, or Zs, in the figure, the
value of the impedance is essentially capacitance. When the
electrical model in FIG. 9 is simulated, the inductive component of
Zs is treated as if it has little to no inductance, and therefore,
the impedance becomes a capacitor representing an area of the
reservoir or the fractures in that area of the reservoir. In other
words, the electrical model by default presents or sees an area of
the reservoir or the fractures in that area of the reservoir as
impedance but the value of the impedance is simulated to be based
on a capacitor. Although this figure shows only three nodes, there
can be more nodes as this figure represents only a small section of
the electrical model or the horizontal portion of the wellbore. The
distance between two adjacent nodes typically can range from 10 to
250 feet. Other ranges are also possible depending on the scale of
the hydraulic wellbore system.
[0076] R(x-1), Z(x-1), and R(x) (and R(x), Z(x), and R(x+1), etc)
are lumped impedance representative of a transmission line (Z(x-1))
and resistance (R(x-1) and R(x)) values and they represent a
lateral portion of the casing connecting adjacent nodes or adjacent
areas of the reservoir. All these values are fixed and can be
determined based on the depth of the well, the size of the casing,
and the fluid in the casing.
[0077] Therefore, by using an equivalent electrical model, one can
obtain a simulated response similar to the actual measured response
for each stage of the horizontal portion of the wellbore. A
simulated response can be created to match the actual measured
response by adjusting the resistor in the stub and the capacitor of
the impedance component in the stub or by solving their resistance
and capacitance through numerical optimization. Once the simulated
response matches to the actual measured response, the obtained
resistance is known as the flow resistance and the obtained
capacitance is known as the fracture capacitance. FIG. 10 shows
such matching for two different stages. At stage X, the simulated
response (the top graph) matches to the actual measured response
(the bottom graph) when the resistance is 33 ohm and the
capacitance is 0.1 farad. At stage Y, the simulated response
matches to the actual measured response when the resistance is 18
ohm and the capacitance is 1 farad.
[0078] Thus, by using an electrical model, simulated responses with
their associated flow resistances and fracture capacitances can be
obtained for previous actual fracture stimulation operations,
future actual stimulation operations, and any other stimulation
operations that one may encounter since the information regarding
the well, the casing, and the fluid are already known, will be
known, or can be predicted in advance. All these simulated
responses, flow resistances, and fracture capacitances may be saved
in a database or lookup table for comparison with future
stimulation operations. In one embodiment, the comparison may be
performed by adjusting the resistor in the electrical model first
to determine the flow resistance and then adjusting the capacitor
to determine the facture capacitance. Therefore, it is possible to
model every expected response and different combination of depth,
fracture flow resistance, fracture capacitance, and response at the
surface in terms of the pressure transient that is generated at the
surface for a given field. The benefit is that hydraulic properties
of the fracture system of the reservoir can be inferred by just
looking at the pressure responses observed at the surface during
the water hammering. The model allows one to infer the flow
resistance and the hydraulic capacitance of the fracture based on
the pressure response measured at the surface. In other words, if
the comparison shows a match, the flow resistance and fracture
capacitance of the actual fracture stimulation operation can be
obtained from the flow resistance and fracture capacitance of the
matched simulated response. With this lookup table, one does not
need to manually change the resistance and capacitance in the
electrical model for matching its simulated response to every
measured response. The benefit of having the lookup table or
database allows an operator to calculate these parameters very
quickly. The operator can get the transient response from the
initial injection or leak off test before the primary stimulation
of every stage in a horizontal wellbore, thereby providing the
operator valuable information needed on a near real time basis to
optimize each particular stage before pumping any proppant.
[0079] The flow resistance can also be approximated by the
Efficiency Coefficient. The Efficiency Coefficient is determined by
how fast the measured response decays (i.e., the initial rate of
decay) and the number of bounces the measured response contains.
These determining factors are directly related to the near wellbore
flow resistance or the flow through the perforations. High
Efficiency Coefficient means that the perforations are open and
have less resistance, and low Efficiency Coefficient means that the
perforations are narrow and have more resistance or that there is a
tortuous path connecting the wellbore with the hydraulic fracture.
This is shown in FIG. 11.
[0080] The fracture capacitance is also known as the completions
index. This value is directly related to the slope (darkened line)
of the simulated response as shown in FIG. 12, and it indicates
whether the reservoir is a compliant or non-compliant system.
Referring to FIG. 12, a positive slope is considered as high index
and it indicates that the reservoir is a compliant system. A
negative slope is considered as low index and it indicates that the
reservoir is a non-compliant system. A compliant or non-compliant
system provides information regarding how rigid the reservoir is. A
compliant system means that the reservoir is less rigid (presence
of natural fractures), the volume of a bounded fluid would expand
rapidly with increase in pressure and contact more surface area
inside the reservoir. A non-compliant system means that the
reservoir is more rigid, the volume of a bounded fluid would remain
relatively static with increase in pressure and contact less area
inside the reservoir. Generally, a rock showing a compliant system
is considered as good rock quality and is more ideal for a
stimulation treatment. Conversely, a rocking showing a
non-compliant system is considered as lower rock quality and is
less ideal for a stimulation treatment. As such, when one obtains
the completions index, or the value of the capacitance in the
simulated response, quality information of the reservoir is also
obtained.
[0081] In addition to obtaining the fracture capacitance by
comparing the simulated responses to the measured response in the
manner discussed above, one can also obtain the fracture
capacitance through numerical optimization. One way of performing
numerical optimization is via a neural network. In this invention,
the neural network is a computational model configured to receive
four variables extracted from the measured response, compares those
variables to the same variables in the simulated responses, and
calculates the completions index if the comparison matches. These
four variables are the depth of the stimulation stage, the
Efficiency Coefficient, the slope ratio, which is m1/m2 as shown in
FIG. 13, and the initial pressure drop in the test of determining a
hydraulic pressure at which the reservoir will begin to fracture.
FIG. 13 also shows the correlation developed between slope ratios,
the initial slope, and the stage depth. The neural network compares
the variables and calculates the completions index based on
training weights obtained from simulations and previous
measurements (optimizing a numerical model to match the measured
response). During this optimization, the Efficiency Coefficient and
the completions index are optimized together or simultaneously. The
optimization of the Efficiency Coefficient simultaneously optimizes
the completions index and vice versa. The neural network may also
be utilized to determine the fracture capacitance by interpolation.
The employment of a neural network provides speedy comparison and
calculation of the completions index.
[0082] Another way of performing numerical optimization to obtain
the fracture capacitance is via numerical simulation of the
electrical model in FIG. 9. One can use numerical optimization to
match the electrical model output to find a best fit to the
measured field data. Using the equivalent per unit length
electrical model shown in FIG. 9, one can determine the correct
flow resistance and completions index in order to match the
observed field response. These values are found through a process
of numerical optimization, wherein a numerical simulator solves
many iterations of the electrical model output with varying flow
resistance and completions index values. Each iteration is assigned
a fitness or a numerical value corresponding to the quality of the
match to the measured field response. The numerical simulator then
outputs the values of the flow resistance and completion index with
the best fitness values. Like the numerical optimization based on a
neural network, the flow resistance and fracture capacitance are
also optimized together or simultaneously. The optimization of the
flow resistance simultaneously optimizes the fracture capacitance
and vice versa.
[0083] Therefore, referring to the step of determining fracture
hydraulic parameters using the measured response 140 in FIG. 1, one
can determine flow resistance and fracture capacitance by either
comparing the simulated response to the measured response with help
from a lookup table or employing numerical optimization. Based on
the determined flow resistance and fracture capacitance, one can
optimize a stimulation treatment to the reservoir 150. The
stimulation treatment may be a hydraulic fracturing treatment. The
optimization of the stimulation treatment may be adjusting the
volume, properties or rate (i.e., number of barrels per minute) of
the fracturing fluid is required to fracture the reservoir,
adjusting the volume, size or type of proppant carried by the
fracturing fluid, or omitting a hydraulic fracturing treatment
altogether for a given stage.
[0084] FIG. 14 shows how the completions index changes throughout a
fracturing treatment. In this example, the fracturing treatment is
divided into three phases instead of one single continuous
fracturing treatment to better observe capacitance change and to
adjust stimulation fluid accordingly. Before any treatment is
performed, the reservoir initially has a completions index of 0.03.
After the first phase of the fracturing treatment is performed by
pumping stimulation fluid with 92,000 lbs. of sand and the pumping
is shut down, which corresponds to the step of the pressure
waveform to the left most of the plot, the completions index or
capacitance rises quickly to 0.087. This change in capacitance is
an indication of the rock quality and can be used to optimize the
fracturing treatment for a particular stage. The subsequent phases
of the fracturing treatment show that increasing the amount of
proppant yields minor increase in capacitance. As such, an operator
knows how rigid the rock is and the optimal amount of proppant to
fracture the rock in this particular stage. Based on this figure,
the operator may not want to add any more proppant into the fluid
after the second phase or after the third phase since the
completions index would not change much and the cost of fracturing
treatment can be reduced. By analyzing the completions index to
determine reservoir quality, the method is also known as
Completions Index Analysis.
[0085] While FIG. 14 shows that the method of the present invention
is conducted during a fracturing treatment, the method may also be
performed after the fracturing treatment to provide indications on
the quality of the fracturing treatment just performed.
[0086] Based on the foregoing, using the measured responses from
the water hammering effect allows an operator to see the variations
in the rock quality so one can recognize the good part of the
lateral (i.e., horizontal wellbore) and what is the poor part of
the lateral. Knowing this information, the operator can make near
real time decisions to optimize the stimulation treatments of the
various stages of a wellbore. Thus, an operator can determine which
sections of the wellbore may justify an even larger treatment than
was originally planned and which sections could be omitted, thereby
reducing the overall cost of the treatment and/or improving the
effectiveness of the treatment.
[0087] FIG. 15 shows a comparison of the initial water hammering
effect and the final water hammering effect for a stimulation stage
based on the Efficiency Coefficient and the completions index. The
initial water hammering effect is the effect measured prior a
fracture treatment whereas the final water hammering effect is the
effect measured after the fracture treatment. Both have similar
input slopes or utilize similar pressure transients. The final
water hammering effect shows that the signal decays much slower
after the fracture treatment, which indicates that the fracturing
fluid and the perforations have a better connection to the
reservoir. The fracture treatment has eroded the tortuous path of
the fractures and it becomes easier to establish a communication
from the wellbore to the reservoir. As such, both the Efficiency
Coefficient and the completions index are higher after the fracture
treatment. The Efficiency Coefficient before and after the fracture
treatment are 0.814 and 0.897, respectively. The completions index
before and after the fracture treatment are 0.235 and 0.820,
respectively.
[0088] While the disclosure has been provided and illustrated in
connection with a specific embodiment, many variations and
modifications may be made without departing from the spirit and
scope of the invention(s) disclosed herein. The disclosure and
invention(s) are therefore not to be limited to the exact
components or details of methodology or construction set forth
above. Except to the extent necessary or inherent in the methods
themselves, no particular order to steps or stages of methods
described in this disclosure, including the Figures, is intended or
implied. In many cases the order of method steps may be varied
without changing the purpose, effect, or import of the methods
described. The scope of the claims is to be defined solely by the
appended claims, giving due consideration to the doctrine of
equivalents and related doctrines.
* * * * *