U.S. patent application number 13/888123 was filed with the patent office on 2014-05-15 for predicting performance of gas condensate reservoirs.
This patent application is currently assigned to Saudi Arabian Oil Company. The applicant listed for this patent is Saudi Arabian Oil Company. Invention is credited to Ali M. Al-Shawaf.
Application Number | 20140136112 13/888123 |
Document ID | / |
Family ID | 50682517 |
Filed Date | 2014-05-15 |
United States Patent
Application |
20140136112 |
Kind Code |
A1 |
Al-Shawaf; Ali M. |
May 15, 2014 |
PREDICTING PERFORMANCE OF GAS CONDENSATE RESERVOIRS
Abstract
Multiphase flow behavior in gas condensate reservoirs is
analyzed, and in particular estimating gas condensate well
deliverability. Inflow performance relationship (IPR) measures for
gas condensate wells are analytically generated and made available.
The inflow performance relationship measures of gas condensate
wells incorporate the effect of condensate banking as pressure near
the well bore drops below the dew point. The inflow performance
relationship measures are based on formation rock relative
permeability data and Constant Composition Expansion (CCE)
experiment data.
Inventors: |
Al-Shawaf; Ali M.; (Dammam,
SA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Saudi Arabian Oil Company |
Dhahran |
|
SA |
|
|
Assignee: |
Saudi Arabian Oil Company
Dhahran
SA
|
Family ID: |
50682517 |
Appl. No.: |
13/888123 |
Filed: |
May 6, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61724534 |
Nov 9, 2012 |
|
|
|
Current U.S.
Class: |
702/6 |
Current CPC
Class: |
E21B 47/00 20130101;
E21B 43/00 20130101; E21B 49/00 20130101 |
Class at
Publication: |
702/6 |
International
Class: |
E21B 47/00 20060101
E21B047/00 |
Claims
1. A computer implemented method of obtaining measures in a data
processing system of predicted performance of a gas condensate well
in a subsurface reservoir, the method comprising the computer
processing steps of: (a) receiving component composition expansion
data based on measurements from fluid from the well; (b) receiving
relative permeability data regarding formations containing the gas
condensate of the well; (c) receiving bottom hole pressure data of
the well; (d) obtaining a measure of dew point of gas condensate in
the well based on the component composition expansion data; (e)
determining if the bottom hole pressure of the well is above the
dew point of the gas condensate of the well, and, (f) if not: (1)
forming an estimated productivity index of the gas condensate well
for single phase flow of the well; (2) forming an estimated
productivity index of the gas condensate well for two phase flow of
the well; (3) forming an estimated predicted performance of the
well as a function of formation relative permeability and the
estimated productivity index of the gas condensate well for two
phase flow; or (g) if so: (1) obtaining a measure of borehole
pressure of the well; (2) forming an estimated predicted
performance of the well as a function of borehole pressure and
relative gas permeability of the well; and (h) assembling in the
memory the estimated predicted performance the well.
2. The computer implemented method of claim 1, wherein the
predicted performance of the well comprises the gas rate.
3. The computer implemented method of claim 1, wherein the step of
forming an estimated predicted performance of the well when the
borehole pressure is above the dew point of the gas condensate
comprises the step of: forming an estimated performance of the well
under pseudo steady state conditions for the gas condensate.
4. The computer implemented method of claim 1, wherein the step of
forming an estimated predicted performance of the well when the
borehole pressure is below the dew point of the gas condensate
comprises the step of: forming a measure of relative gas
permeability as a function of saturation of the well.
5. The computer implemented method of claim 1, further including
the step of: forming an output display of selected ones of the
determined measure of estimated predicted performance of the
well.
6. A data processing system for obtaining measures of predicted
performance of a gas condensate well in a subsurface reservoir, the
data processing system comprising: (a) a processor performing the
steps of: (1) receiving component composition expansion data based
on measurements from fluid from the well; (2) receiving relative
permeability data regarding formations containing the gas
condensate of the well; (3) receiving bottom hole pressure data of
the well; (4) obtaining a measure of dew point of gas condensate in
the well based on the component composition expansion data; (5)
determining if the bottom hole pressure of the well is above the
dew point of the gas condensate of the well, and, (6) if not: (i)
forming an estimated productivity index of the gas condensate well
for single phase flow of the well; (ii) forming an estimated
productivity index of the gas condensate well for two phase flow of
the well; (iii) forming an estimated predicted performance of the
well as a function of formation relative permeability and the
estimated productivity index of the gas condensate well for two
phase flow; or (7) if so: (i) obtaining a measure of borehole
pressure of the well; (ii) forming an estimated predicted
performance of the well as a function of borehole pressure and
relative gas permeability of the well; and (8) assembling in the
memory the estimated predicted performance the well; and; (b) an
output display forming a display of selected ones of the determined
measure of estimated predicted performance of the well.
7. The data processing system of claim 6, wherein the predicted
performance of the well comprises the gas rate.
8. The data processing system of claim 6, wherein the processor in
forming an estimated predicted performance of the well when the
borehole pressure is above the dew point of the gas condensate
performs the step of: forming an estimated performance of the well
under pseudo steady state conditions for the gas condensate.
9. The data processing system of claim 6, wherein the processor in
forming an estimated predicted performance of the well when the
borehole pressure is below the dew point of the gas condensate
performs the step of: forming a measure of relative gas
permeability as a function of saturation of the well.
10. A data storage device having stored in a computer readable
medium non-transitory computer operable instructions for causing a
data processing system to obtain measures in a computer system of
predicted performance of a gas condensate well in a subsurface
reservoir, the instructions stored in the data storage device
causing the data processing system to perform the following steps:
(a) receiving component composition expansion data based on
measurements from fluid from the well; (b) receiving relative
permeability data regarding formations containing the gas
condensate of the well; (c) receiving bottom hole pressure data of
the well; (d) obtaining a measure of dew point of gas condensate in
the well based on the component composition expansion data; (e)
determining if the bottom hole pressure of the well is above the
dew point of the gas condensate of the well, and, (f) if not: (1)
forming an estimated productivity index of the gas condensate well
for single phase flow of the well; (2) forming an estimated
productivity index of the gas condensate well for two phase flow of
the well; (3) forming an estimated predicted performance of the
well as a function of formation relative permeability and the
estimated productivity index of the gas condensate well for two
phase flow; or (g) if so: (1) obtaining a measure of borehole
pressure of the well; (2) forming an estimated predicted
performance of the well as a function of borehole pressure and
relative gas permeability of the well; and (h) assembling in the
memory the estimated predicted performance the well.
11. The data storage device of claim 10, wherein the predicted
performance of the well comprises the gas rate.
12. The data storage device of claim 10, wherein the instructions
include instructions causing the data processing system in forming
an estimated predicted performance of the well when the borehole
pressure is above the dew point of the gas condensate to perform
the step of: forming an estimated performance of the well under
pseudo steady state conditions for the gas condensate.
13. The data storage device of claim 10, wherein the instructions
include instructions causing the data processing system in forming
an estimated predicted performance of the well when the borehole
pressure is below the dew point of the gas condensate to perform
the step of: forming a measure of relative gas permeability as a
function of saturation of the well.
14. The data storage device of claim 10, wherein the instructions
includes causing the data processing system to form an output
display of selected ones of the determined measure of estimated
predicted performance of the well.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority from U.S. Provisional
Application No. 61/724,534, filed Nov. 9, 2012. For purposes of
United States patent practice, this application incorporates the
contents of the Provisional Application by reference in
entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to reservoir analysis of
performance of subsurface hydrocarbon reservoirs, and more
particular to prediction of the performance of gas condensate
reservoirs.
[0004] 2. Description of the Related Art
[0005] Gas condensate reservoirs differ from dry gas reservoirs.
Understanding the phase and fluid flow behavior relationships has
been required in order to make accurate engineering computations
for gas condensate systems. Condensate dropout occurs in the
reservoir as the pressure falls below the dew point. As a result of
such condensate dropout, gas phase production from gas condensate
wells decreases significantly.
[0006] Well productivity is an important issue in the development
of most low and medium permeability gas condensate reservoirs.
Liquid build up around the well has been found to cause a
significant reduction in productivity, even in lean gas condensate
reservoirs where the maximum liquid drop out indicated by test data
is as low as 1%. However, accurate forecasts of gas condensate
productivity has been difficult because of the need to understand
and account for complex processes that occur in the near-well
region.
[0007] The production performance of a gas condensate well is easy
to predict as long as the well flowing bottomhole pressure (known
as FBHP) is above the initial reservoir fluid dew point pressure.
The gas condensate well production performance in such conditions
is similar to a dry gas well.
[0008] Once the FBHP of a gas condensate well falls below the dew
point, the well performance starts to deviate from that of a dry
gas well. With pressure below the dew point, condensate begins to
drop out, beginning first near the wellbore. Immobile initially,
the liquid condensate accumulates until a critical condensate
saturation (known as the minimum mobile condensate saturation) is
reached. This rich liquid zone grows outward deeper into the
reservoir as reservoir depletion continues.
[0009] Estimates have been made of the productivity of gas
condensate reservoirs. So far as is known, none of these estimation
methods have been simple to use. Some estimation methods required
use of modifications required to be made in the finite difference
simulation processing of reservoir data. Other estimation methods
have used simulation models of reservoir component gases and their
pressures and states during projected reservoir life, which
required simplification by certain assumptions. The estimates were
thus accurate only if the simplifying assumptions were sound.
SUMMARY OF THE INVENTION
[0010] Briefly, the present invention provides a new and improved
computer implemented method of obtaining measures in a data
processing system of predicted performance of a gas condensate well
in a subsurface reservoir. Component composition expansion data
based on measurements from fluid from the well is received in the
data processing system. Relative permeability data regarding
formations containing the gas condensate of the well is also
received, as well as bottom hole pressure data of the well. A
measure of dew point of gas condensate in the well based on the
component composition expansion data is obtained by the data
processing system, and the data processing system determines if the
bottom hole pressure of the well is above the dew point of the gas
condensate of the well. If not, an estimated productivity index of
the gas condensate well is formed for single phase flow of the
well; and an estimated productivity index of the gas condensate
well is formed for two phase flow of the well. An estimated
predicted performance of the well is then formed as a function of
formation relative permeability and the estimated productivity
index of the gas condensate well for two phase flow. If the bottom
hole pressure of the well is above the dew point of the gas
condensate of the well a measure of borehole pressure of the well
is obtained and an estimated predicted performance of the well as a
function of borehole pressure and relative gas permeability of the
well is formed in the data processing system. The estimated
predicted performance of the well is then assembled.
[0011] The present invention also provides a new and improved data
processing system for obtaining measures of predicted performance
of a gas condensate well in a subsurface reservoir. The data
processing system includes a processor which receives component
composition expansion data based on measurements from fluid from
the well, relative permeability data regarding formations
containing the gas condensate of the well, and bottom hole pressure
data of the well. The processor obtains a measure of dew point of
gas condensate in the well based on the component composition
expansion data, and determines if the bottom hole pressure of the
well is above the dew point of the gas condensate of the well. If
not, the processor forms an estimated productivity index of the gas
condensate well for single phase flow of the well, and also forms
an estimated productivity index of the gas condensate well for two
phase flow of the well. The processor further forms an estimated
predicted performance of the well as a function of formation
relative permeability and the estimated productivity index of the
gas condensate well for two phase flow. If the bottom hole pressure
is above the dew point, the processor obtains a measure of borehole
pressure of the well, and forms an estimated predicted, performance
of the well as a function of borehole pressure and relative gas
permeability of the well. The processor then assembles in memory
the estimated predicted performance the well. An output display of
the data processing system forms a display of selected ones of the
determined measure of estimated predicted performance of the
well.
[0012] The present invention also provides a new and improved data
storage device having stored in a computer readable medium computer
operable instructions for causing a data processing system to
obtain measures in a computer system of predicted performance of a
gas condensate well in a subsurface reservoir. The instructions
stored in the data storage device cause the data processing system
to receive component composition expansion data based on
measurements from fluid from the well; relative permeability data
regarding formations containing the gas condensate of the well; and
bottom hole pressure data of the well. The instructions stored in
the data storage device cause the data processing system to obtain
a measure of dew point of gas condensate in the well based on the
component composition expansion data, and determine if the bottom
hole pressure of the well is above the dew point of the gas
condensate of the well. If the bottom hole pressure of the well is
not above the dew point, the instructions cause the data processing
system to form an estimated productivity index of the gas
condensate well for single phase flow of the well, then form an
estimated productivity index of the gas condensate well for two
phase flow of the well and form an estimated predicted performance
of the well as a function of formation relative permeability and
the estimated productivity index of the gas condensate well for two
phase flow. If the bottom hole pressure of the well is above the
dew point, the instructions cause the data processing system to
obtain a measure of borehole pressure of the well, and form an
estimated predicted performance of the well as a function of
borehole pressure and relative gas permeability of the well. The
instructions then cause the data processing system to assemble in
memory the estimated predicted performance the well.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a plot of flow behavior in a gas condensate
well.
[0014] FIG. 2 is a plot of constant composition expansion data for
synthetic gas condensate compositions.
[0015] FIG. 3 is a diagram of a fine scale radial simulation model
for a well.
[0016] FIG. 4 is a plot of a group of sets of Corey relative
permeability curves.
[0017] FIG. 5 is a plot of well productivity index as a function of
time.
[0018] FIG. 6 is a plot of oil saturation profiles around a well as
a function of time.
[0019] FIG. 7 is a plot of gas relative permeability as a function
of productivity index ratio for a rich condensate fluid.
[0020] FIG. 8 is a plot of gas relative permeability as a function
of productivity index ratio for a lean condensate fluid.
[0021] FIG. 9 is a comparative plot of well productivity index as a
function of time for rich and for lean condensate fluids.
[0022] FIG. 10 a plot of productivity index ratios of rich versus
lean condensate fluids.
[0023] FIG. 11 is a plot of pseudopressure as a function of gas
production rate for several reservoir pressures.
[0024] FIG. 12 is a plot of bottomhole pressure as a function of
gas production rate for several reservoir pressures.
[0025] FIG. 13 is a plot of inflow performance relationship for an
example reservoir pressure.
[0026] FIG. 14 is a plot illustrating threshold saturation in tight
relative permeability curves.
[0027] FIG. 15 is a plot of oil saturation distribution as a
function of various bottomhole pressures for an example reservoir
pressure.
[0028] FIG. 16 is a plot of inflow performance relationship for
another example reservoir pressure.
[0029] FIG. 17 is a plot of pseudopressure as a function of gas
production rate for an example reservoir pressure.
[0030] FIG. 18 is a plot of oil saturation distribution as a
function of various bottomhole pressures for an example reservoir
pressure.
[0031] FIG. 19 is a plot of oil saturation distribution as a
function of various bottomhole pressures for another example
reservoir pressure.
[0032] FIG. 20 is a plot of oil saturation distribution as a
function of various bottomhole pressures for another example
reservoir pressure.
[0033] FIG. 21 is a graphical illustration depicting development of
a linear relationship between oil saturation and constant
composition expansion data for a well.
[0034] FIG. 22 is a plot of inflow performance relationship
according to the present invention for an example reservoir
pressure.
[0035] FIG. 23 is a plot of pseudopressure versus gas rate for the
same reservoir pressure as that of the data of FIG. 22.
[0036] FIG. 24 is a comparative plot of inflow performance
relationships according to the present invention versus data
obtained from simulation models.
[0037] FIG. 25 is a plot of well productivity index as a function
of time.
[0038] FIG. 26 is a plot of oil saturation profiles around a well
as a function of time for radial cell models.
[0039] FIG. 27 is a plot of constant composition expansion data for
an example field case according to the present invention.
[0040] FIG. 28 is a plot illustrating the relative permeability of
the example field case.
[0041] FIG. 29 is a plot of production data of two tests conducted
according to the present invention.
[0042] FIG. 30 is a plot of pseudopressure versus gas rate for a
test according to the present invention.
[0043] FIG. 31 is a plot of pseudopressure versus gas rate for a
test according to the present invention.
[0044] FIG. 32 is a plot of the inflow performance relationship
according to the present invention for a second example reservoir
pressure.
[0045] FIG. 33 is a plot of pseudopressure versus gas rate for the
same reservoir pressure as that of the data of FIG. 32.
[0046] FIG. 34 is a comparative plot of inflow performance
relationships according to the present invention versus data
obtained from simulation models.
[0047] FIG. 35 is a plot comparing inflow performance relationships
according to the present invention versus data obtained from field
observed data.
[0048] FIG. 36 is a functional block diagram of a set of data
processing steps performed in a data processing system for
prediction of the performance of gas condensate reservoirs
according to the present invention.
[0049] FIG. 37 is a functional block diagram of a set of processing
steps showing in more detail portions of FIG. 36.
[0050] FIG. 38 is a functional block diagram of a set of processing
steps showing in more detail portions of FIG. 36.
[0051] FIG. 39 is a schematic block diagram of a data processing
system for rock facies prediction of subsurface earth formations
according to the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0052] In the drawings, FIG. 1 schematically indicates flow
behavior of a gas condensate well in three regions. Region 1
represents an inner near-wellbore region, as shown in FIG. 1, where
both condensate and gas are mobile. It is the most important region
for calculating condensate well productivity, as most of the
pressure drop occurs in Region 1. The flowing composition (GOR)
within Region I is constant throughout and a semi-steady state
regime exists. This means that the single phase gas entering Region
1 has the same composition as the produced well stream mixture. The
dew point of the producing well stream mixture equals the reservoir
pressure at the outer edge of Region 1.
[0053] Region 2 is the region where the condensate saturation is
building up. The condensate is immobile, and only gas is flowing.
The loss in productivity due to liquid build-up is mostly
influenced by the value of gas relative permeability (k.sub.rg)
near the well when compared with the value of k.sub.rg in the
reservoir further away. The loss in productivity is known to be
more sensitive to the relative permeability curves than to fluid
PVT properties. Condensate saturations in Region 2 are approximated
by the liquid dropout curve from a Constant Volume Depletion (CVD)
experiment, corrected for water saturation.
[0054] Region 3 is the region in the gas condensate reservoir where
no condensate phase exists (above the dew point). Region 3 only
exists in a gas condensate reservoir that is currently
undersaturated. It contains a single phase (original) reservoir
gas.
[0055] The pseudosteady state rate equation for a gas condensate
well is a known relationship available in the literature. For
example, according to "Natural Gas Production Engineering," M.
Kelkar, Penn Well Corporation, 2008, the relation as expressed in
field units is given by:
q sc = ( 703 .times. 10 - 6 ) kh [ m ( p r ) - m ( p wf ) ] T [ ln
( r e r w ) - 0.75 + S ] ( 1 ) ##EQU00001##
where q.sub.sc (the flow rate is in (Mscf/d), k (permeability is in
md), h (height is in ft.), m(p.sub.r) (the real pseudopressure) and
m(p.sub.wf) (the well flowing pseudopressure) are in
(psi.sup.2/cp), T is in (.degree. R), and r.sub.e and r.sub.w are
in feet.
[0056] The relationship can be used to estimate the gas production
rate as long as bottomhole flowing pressure (BHFP) is above the dew
point of reservoir fluids, that is, an undersaturated reservoir.
The relationship is, however, applicable only for single phase gas
flow. As soon as BHFP drops below the dew point pressure of
reservoir fluid, condensate begins to drop out. The condensate drop
out begins first near the well bore and the well performance starts
to deviate from that of a dry gas well. Liquid condensate
accumulates until the critical condensate saturation (the minimum
mobile condensate saturation) is reached. This rich liquid
bank/zone grows outward deeper into the reservoir as depletion
continues.
[0057] Liquid accumulation, or condensate banking, causes a
reduction in the gas relative permeability, and acts as a partial
blockage to gas production which leads to potentially significant
reduction in well productivity. To quantify the impact of gas
condensation phenomena the present invention provides methodology
to generate inflow performance relationships (IPR) of gas
condensate reservoirs using analytical procedures.
[0058] The present invention utilizes constant composition
expansion (CCE) data or, alternatively, CVD data along with the
relative permeability curves. The present invention combines fluid
properties (CCE or CVD data) with rock properties (relative
permeability curves) to provide a methodology of analytical
solution that is accurate enough to estimate the IPR curves of gas
condensate reservoirs.
Fluid Description
[0059] FIG. 2 is a plot of CCE data for sample fluids used as
example reservoir gas condensates. The CCE data are obtained as
laboratory test data performed to measure the change in volume of a
gas condensate fluid as a function of pressure. Two different
synthetic gas-condensate compositions were used to generate the
Rich, Intermediate and Lean fluids represented in FIG. 2. The Rich
fluid is composed of three components which are methane (C1, 89%),
butane (C4, 1.55%) and decane (C10, 9.45%). While a four-component
composition was used to generate the Intermediate and Lean
condensate mixtures at different reservoir temperatures. The four
components are methane (C1, 60.5%), Ethane (C2, 20.0%), Propane
(C3, 10.0%), and decane (C 10, 9.25%). The characteristics of the
condensate mixtures are outlined in Table 1.
TABLE-US-00001 TABLE 1 Fluid Properties Rich Gas Intermediate Gas
Lean Gas Initial Reservoir Pressure 7000 5500 5000 (psia) Dew Point
Pressure (psia) 5400 3250 2715 Reservoir Temperature (.degree. F.)
200 260 340 Maximum Liquid Dropout 26 20 8.5 (%)
Reservoir Description
[0060] An Eclipse 300 compositional simulator of the type available
from Schlumberger was used for simulation of gas condensate
productivity for the gas condensate sample fluids identified above.
The conventional three parameter Peng-Robinson equation of state
was used to simulate the PVT properties of the gas condensate
fluids. A one-dimensional radial compositional model with a single
vertical layer and 36 grid cells in the radial direction was used
as a test case as shown in FIG. 3. Homogenous properties were used
in the fine scale model as described in Table 2.
TABLE-US-00002 TABLE 2 Reservoir Properties Used in the Fine Radial
Model Porosity (%) 20 Absolute permeability (md) 10 Reservoir
height (ft.) 100 Irreducible water saturation (%) 0 Rock
Compressibility (psi-1) 4.0E-06
[0061] A single producer well 20 in the simulation lies at the
center of the reservoir and is assumed to be perforated across the
height of the reservoir. The model has been refined near the well
bore to accurately observe the gas condensate drop out effect. For
that purpose, the size of the radial cells has been logarithmically
distributed with the inner most grid size is 0.25 ft according to
the following Equation:
r i + 1 r i = [ r e r w ] 1 / N ( 2 ) ##EQU00002##
[0062] Besides having very small grid blocks around the well, the
time steps have been refined at initial times which led to a very
smooth saturation profile around the well. The fully implicit
method was chosen for the gas condensate productivity simulation
runs.
[0063] The most accurate way to determine gas-condensate well
productivity is by fine-grid numerical simulation, either in
single-well models with a fine grid near the well or in full-field
models using local grid refinement. A large part of the pressure
drawdown occurs within 10 feet of the well, so that radial models
are used with the inner grid cell having dimensions of about one
foot.
Relative Permeability Curves
[0064] It is known that relative permeability changes affect the
flow significantly in a gas-condensate reservoir once the pressure
falls below dew-point pressure. Accurate knowledge about relative
permeability curves in a gas condensate reservoir would be ideal
information. Usually, however, this is not the case, as the
relative permeability curves are rarely known accurately.
[0065] Different sets of relative permeability curves were used in
the test data examples described herein. These curves were
generated based on Corey equations as illustrated below:
K rg = S g n ( 3 ) K ro = ( 1 - S g - S or 1 - S or ) m ( 4 )
##EQU00003##
where (n) is the gas relative permeability exponent, (m) is the oil
relative permeability exponent and (S.sub.or) is the residual oil
saturation. Fractures (X-Curves), Intermediate and tight relative
permeability curves were generated by changing (n) and (m)
exponents from 1 to 5 and changing (S.sub.or) from 0 to 0.60. A
naming convention (Corey-#) was used for the relative permeability
curves for identification purposes. FIG. 4 shows three sets of
relative permeability curves. Corey-1 (X-curve) is generated based
on n=1, m=I and S.sub.or=O. Corey-14 is generated based on n=3, m=4
and S.sub.or=0.20. The third curve, Corey-24 is generated based on
n=5, m=4 and S.sub.or=0.60.
Generation of IPR Measures
[0066] Inflow Performance Relationships (IPR) data in the form of
measures or curves indicating inflow performance relationships are
very important to predict the performance of gas or oil wells.
However, generating IPR curves using a simulator is not
straight-forward since the IPR represents an instantaneous response
of the reservoir at a given reservoir pressure for a given
bottomhole pressure. This cannot be generated in a single run since
the bottom hole pressure changes in a simulation run, depending on
how much oil or gas is produced. The average pressure also changes
and in a manner which does not directly correspond.
[0067] To generate IPR measures or curves, a composite method is
utilized with the present invention. A simulator is run at a fixed
bottomhole pressure. The bottomhole pressure is then varied from
high to low values. Rate profiles are generated for a particular
bottomhole pressure and average reservoir pressure as the reservoir
pressure depleted. Using various runs, the rate at a given
reservoir pressure and a given bottomhole pressure are then
selected, then combined them into one curve to generate an IPR
curve.
Analytical Approach for Estimating Gas Condensate Well
Productivity
[0068] The IPR measures or curves were plotted both as a function
of pressure as well as pseudo-real pressure, and it was noticed
that plotting the pseudopressure versus the gas rate results in two
clear straight lines for every reservoir pressure, as shown in FIG.
11. Correspondingly it was noted that plotting bottom hole flowing
pressure versus the gas rate results in IPR curves as shown in FIG.
12. A peculiar behavior of IPR curves is noted when plotted as a
function of pseudo-real pressure. The lines are parallel above dew
point, as expected, since the productivity does not change. Below
dew point, for different reservoir pressures, the lines are
parallel for certain pressure ranges. However, as the reservoir
pressure depletes, the slope becomes gentler. This is an indication
of improved productivity. This is a result of re-evaporation of
liquid phase as the pressure declines. This type of trend is
difficult to capture and then evaluate using pressure data.
[0069] As soon as reservoir pressure drops below the dew point
(P.sub.d), which is 3250 psi in this example, a productivity loss
occurs which is characterized by the straight line below P.sub.d in
the pseudopressure plot as shown in FIG. 11. To illustrate the
methodology of the present invention, P.sub.r=5400 psi is taken as
an example for illustration as shown in FIG. 13.
[0070] The pseudosteady-state gas rate equation (Equation 1 above)
is required for use according to the present invention, which
requires that a pseudopressure function be available in terms of
normal pressure. Data available in Tulsa University Center of
Reservoir Studies (TUCRS) was utilized to generate the
pseudo-pressures from normal pressures based on fluid properties
for each fluid composition of the fluid samples mentioned
above.
[0071] The pseudopressure plot in FIG. 13 clearly shows that there
are two distinct productivity indices. A first productivity index
(J) which is constant for single phase gas flow (where FBHP is
above P.sub.d), and a second productivity index (J*) which is for
two phase flow (where FBHP is below P.sub.d). Referring back to the
pseudosteady--state gas rate Equation (1) above, the productivity
index in terms of pseudopressure is given by:
J = q sc [ m ( p r ) - m ( p wf ) ] ( 5 ) ##EQU00004##
where J in field units is in: (MMscfd/psia.sup.2/cp).
[0072] Looking back at FIG. 13, the slopes can be defined as
follows:
Slope of the line above P.sub.d=(-1/J) (6)
Slope of the line below P.sub.d=(1/J*) (7)
[0073] After analyzing several cases, with the present invention it
was found that productivity ratio can be determined by dividing the
slope above P.sub.d by slope below P.sub.d as following;
Slope of the line above P d Slope of the line below P d = ( - 1 J )
( - 1 J * ) = J * J .apprxeq. Productivity Ratio ( 8 )
##EQU00005##
[0074] Since the productivity Index (J, for a single phase gas) is
always higher than productivity Index (J*, for two phase flow), the
productivity ratio (J*/J) is always less than one. Not only that,
it has been found with the present invention that the productivity
ratio (J*/J) is very much correlated to K.sub.rg (S.sub.or) for
each relative permeability curve used as will be described
below.
Procedure
Initial Reservoir Pressure is Above the P.sub.d
[0075] When initial reservoir pressure is above the dew point
P.sub.d, the pseudo steady state gas rate Equation (2) will be used
to estimate the gas rate when FBHP >P.sub.d. Since initial
reservoir pressure is above the P.sub.d, the productivity index (J)
is constant for bottomhole pressures above the P.sub.d, as
described above. When FBHP drops below the P.sub.d, it is necessary
as described below to estimate (J*) first to be able to calculate
the gas rate analytically.
[0076] After estimating (J), when initial reservoir pressure is
above dew point, knowledge of k.sub.rg (S.sub.o) as a multiplier is
used to get J* as following:
J * J = Productivity Ratio .apprxeq. K rg ( S o * ) ( 9 )
##EQU00006##
[0077] After estimating J* which has constant but higher slope than
J as shown before on the pseudo-pressure plot, J* is used to
estimate the gas rate for all bottomhole pressures below the
P.sub.d using the following equation, as follows:
y = mx + b ( 10 ) m ( P wf ) = ( - 1 J * ) q + b ( 11 )
##EQU00007##
[0078] Knowledge of the rate and FBHP at the P.sub.d is then used
based on the pseudo-steady state gas rate equation above dew point.
Then the intercept b can be calculated as follows:
b = m ( P d ) + q a J * ( 12 ) ##EQU00008##
where b in field units is in (psi2/cp).
[0079] Now, the straight line pseudo-pressure equation set forth
above is complete to estimate the gas rate for any FBHP less than
the P.sub.d, as follows:
q=[b-m(P.sub.wf)]J* (13)
where the units of measure are as identified previously.
Procedure
Initial Reservoir Pressure is Below the P.sub.d
[0080] FIG. 11 shows three examples of IPR lines where initial
reservoir pressure is below the P.sub.d. To be able to generate the
IPR curves for cases where initial reservoir pressure below the
P.sub.d, the following procedure is followed:
J = q sc [ m ( p r ) - m ( p wf ) ] = ( 703 .times. 10 - 6 ) kh T [
ln ( r e r w ) - 0.75 + S ] ( 14 ) ##EQU00009##
Estimate the Productivity Index (J*)
[0081] As described above, that productivity ratio (J*/J) is
correlated to k.sub.rg(S.sub.or), but in cases where initial
reservoir pressure is below P.sub.d, liquid re-vaporization plays a
very important role into determining productivity of gas condensate
reservoirs. By examining the constant composition expansion data as
shown in FIG. 2, it can be seen that as soon as pressure drops
below the P.sub.d, liquid saturation immediately reaches a maximum
value (Max_So_CCE) around the P.sub.d, then it falls gradually as a
function of pressure. The present invention utilizes constant
composition expansion data to generate the IPR curves to account
for this phenomenon of liquid vaporization as pressure drops below
the P.sub.d. It has been found that using a fixed value of
k.sub.rg(S.sub.or) or k.sub.rg(Max_SoCCE) underestimates the gas
productivity for cases where initial reservoir pressure is below
the P.sub.d.
[0082] Therefore, for any reservoir pressure below P.sub.d,
k.sub.rg needs to be estimated at the corresponding pressure and
oil saturation from the constant composition expansion data
according to the following equation:
J * J ( P r ) = Productivity Ratio .apprxeq. K rg ( S oCCE ) ( 15 )
##EQU00010##
[0083] To estimate the Productivity Index (J), if an IPR curve for
the case where reservoir pressure above the P.sub.d is available,
the productivity index (J) of this case could be used to estimate
J* as a function of pressure using constant composition expansion
data as will be explained. For cases where IPR curves above the
P.sub.d are not available, the productivity index (J) can be
estimated using pseudo-steady state gas rate equation, Equation (1)
as described above.
[0084] To estimate the Gas Rate, the gas rate can be directly
estimated from the following equation:
q=[m(P.sup.r)-m(P.sub.wf)]J* (16)
General Procedure for Generating IPR Curves
[0085] The above described procedure for generating IPR curves
assumes that S.sub.or=Max_So_CCE, but it is not always the case in
real field applications. Since S.sub.or is a rock property while
Max_So_CCE is a fluid property, one can expect them to be different
in most of the cases in field applications.
[0086] For that purpose, several cases were analyzed where S.sub.o,
could be equal to, less than or greater than Max_So_CCE. Based on
an evaluation, it has been found according to the present invention
that the maximum of the two values should be used to correctly
capture the fluid behavior around the well bore, and hence
accurately estimate the gas productivity
[0087] The procedure to estimate productivity index (J*) for
generating IPR measures or curves is exactly the same as the
procedure outlined above for the case where S.sub.or=Max_So_CCE but
with some modifications as given by Table 3 below. This procedure
is used for flowing pressure less than dew point. In effect this
recognizes that if reservoir pressure is above dew point, then to
calculate the IPR curve for bottom hole pressure below dew point, a
constant slope (J*) based on K.sub.rg estimate is necessary to be
used as stated below. However, once the reservoir pressure drops
below dew point, it is necessary to use K.sub.rg as a function of
average pressure.
TABLE-US-00003 TABLE 3 General Procedure for Generating IPR Curves
Cases where Case Cases where Pr above P.sub.d Pr below P.sub.d
S.sub.or = Max_So_CCE Use K.sub.rg(S.sub.or) K.sub.rg(So_CCE) =
f(P) S.sub.or < Max_So_CCE Use K.sub.rg(Max_So_CCE) S.sub.or
> Max_So_CCE Use K.sub.rg(S.sub.or)
Importance of Threshold Oil Saturation (S.sub.o*)
[0088] It has been found that accurate estimation of gas
productivity depends not only on S.sub.or but also depends on
Threshold oil saturation (S.sub.o*) for reservoirs having tight oil
relative permeability curves. FIG. 14 shows an oil relative
permeability curve that was generated based on S.sub.or=0.20 and a
high value of oil exponent (m=4). This higher value of oil exponent
makes the oil relative permeability very low and eventually makes
oil immobile until its saturation exceeds S.sub.or to a threshold
(S.sub.o*) which is in this case 0.48 as shown in FIG. 14. After
testing several tight relative permeability curves, it was found
that for practical applications, we can determine the threshold
(S.sub.o*) can be determined to be corresponding to
K.sub.ro=1%.
[0089] Therefore, in generating IPR curves, it is more important to
know S.sub.o* than S.sub.or. S.sub.o* can be defined as a minimum
saturation needed to make oil mobile (i.e., K.sub.ro is at least 1%
of the end point value). It is a strong function of the curvature
of the relative permeability curve. Hence, Table 3 can be used but
replacing S.sub.or with S.sub.o* as follows:
TABLE-US-00004 TABLE 4 General Procedure for Generating IPR Curves
with S.sub.o* Cases where Case Cases where Pr above P.sub.d Pr
below P.sub.d S.sub.o* = Max_So_CCE Use K.sub.rg(S.sub.or)*
K.sub.rg(So_CCE) = f(P) S.sub.o* < Max_So_CCE Use
K.sub.rg(Max_So_CCE) S.sub.o* > Max_So_CCE Use
K.sub.rg(S.sub.o*)
[0090] This is the most common case where in many field situations,
the residual oil saturation in condensate reservoirs can be as high
as 0.5. Keeping in mind that threshold saturation (S.sub.o*) plays
the most important rule in tight rocks as explained earlier.
[0091] The Rich condensate fluid with Maximum Liquid Dropout (26%)
is being used for this ease where it is less than S.sub.o*=0.48 as
shown previously in FIG. 14. Referring back to Table 3.4, it can be
seen that in this ease the productivity ratio is determined by
K.sub.rg(S.sub.o*).
[0092] FIG. 15 shows an observation similar to previous cases. The
near well bore saturation does not change with change in bottom
hole pressure for a given reservoir pressure.
[0093] Since in this case Threshold (S.sub.o*) is higher than
Max_S.sub.o.sub.--CCE. This value of S.sub.o* should be used to get
the corresponding K.sub.rg and hence estimate the well productivity
for the cases where reservoir pressure is above the P.sub.d. By
following the procedure outlined above for situations where initial
reservoir pressure is above the P.sub.d, an IPR curve can be
generated as shown in FIG. 16. It should be kept in mind that the
only change for the case where Threshold (S.sub.o*)>Max_So_CCE
is to use the larger value of the two, which is in this case the
S.sub.o*.
J * J = Productivty Ratio = Krg ( So * ) ( 17 ) ##EQU00011##
[0094] To illustrate an example for the case where Initial
Reservoir Pressure is below P.sub.d, to correctly generate the
slope of IPR curve on pseudo-pressure plot, it is necessary to
account for re-vaporization. Again the fine grid model is utilized
to capture the condensate behavior near the well bore as was done
in the earlier example.
[0095] FIGS. 18, 19 and 20 show S.sub.o distribution for saturated
reservoirs. On examination it is possible to notice that S.sub.o is
decreasing gradually as a function of reservoir pressure from about
0.62 when Pr=6900 psi (FIG. 15) to almost 0.30 when P.sub.r=1000
psi (FIG. 20). This observation is exactly what was concluded from
the previous example--that oil re-vaporization close to the well
bore is a strong function of decreasing reservoir pressure.
[0096] Another important conclusion that can be seen in the
previous example is that S.sub.o builds up to uniform value close
to the well bore for each saturated reservoir pressure. This
uniform S.sub.o remains almost constant as FBHP decreases.
Therefore, a valid assumption for the application of the present
invention is to assume a uniform S.sub.o for every saturated
pressure under consideration. FIG. 20 shows an example of an
extreme case where all the oil evaporates at very low flowing
pressure.
[0097] With this understanding of gas condensate behavior around
the well bore, the need to utilize the constant composition
expansion data as a tool to mimic condensate re-vaporization
process is evident as reservoir pressure depletes. The constant
composition expansion data of a Rich fluid is shown in FIG. 2.
[0098] Since in this case Threshold (S.sub.o*) is greater than
Max_So_CCE, the approach is to develop a linear relationship
between the S.sub.o* and the constant composition expansion data as
shown in FIG. 21.
[0099] Careful examination of FIG. 15 and FIGS. 18 through 20
indicates that actual liquid dropout around the well bore is much
greater than Max_So_CCE and is closer to Threshold (S.sub.o*).
After testing several cases under this category it was determined
that using K.sub.rg (Max_So_CCE) overestimates the gas rate, since
it does not account for revaporization of liquid.
[0100] From the foregoing it is very clear that condensate banking
(Accumulation) is tied up with two factors. The first factor is
Fluid Properties (Maximum S.sub.o from constant composition
expansion) and the second factor is Rock Properties (Immobile
S.sub.o). Accordingly, although actual liquid dropout around the
well bore is much greater than Max_So_CCE, it would still be
desirable to utilize the constant composition expansion data along
with relative permeability curves to come up with a robust
analytical procedure that is accurate enough to estimate the well
productivity.
[0101] As will be shown, different fluids have a similar
productivity loss for the same relative permeability curve used,
confirming that it is the relative permeability which is the most
important in determining the productivity loss.
[0102] An engineering approximation is thus to model the behavior
below dew point pressure. The constant composition expansion data
of the Rich fluid is shown previously in FIG. 2. As stated before
it is assumed that the area around the well bore behaves like the
constant composition expansion data for every designated saturated
pressure. Following the procedure outlined for situations where
initial reservoir pressure is below the P.sub.d, it is useful
consider an example at P.sub.r=4000 psi. After estimating the
Productivity Index (J) as shown in step (I) of the procedure, one
can estimate Productivity Index (J*) as following:
J * J = Productivity Ratio = Krg ( So * _CCE ) ( 18 )
##EQU00012##
[0103] At P.sub.r=4000 psi, one can estimate S.sub.o from the
linear relation between the S.sub.o* and constant composition
expansion data as shown in FIG. 21. The next step is to go back to
relative permeability curves to estimate K.sub.rg at the
corresponding S.sub.o from this linear relation. After that P can
be calculated directly from Equation (18). The IPR curve is shown
in FIG. 22 along with the pseudopressure plot in FIG. 23. The
complete IPR curves of this case are determined in this manner.
[0104] Before finishing this example it is helpful to examine the
well productivity index shown in FIG. 5 while running at constant
rate condition. As was expected, it was found that the productivity
ratio is very close to K.sub.rg (S.sub.o*) as following:
Min Well PI Max Well PI = 0.11 .apprxeq. Krg ( So * ) = 0.14 ( 19 )
##EQU00013##
[0105] Based on the PT ratio we can define productivity loss as
following:
Productivity loss = 1 - Min Well PI Max Well PI ( 20 )
##EQU00014##
[0106] In this example the productivity loss is 0.89. This means
that this well will experiences an 89% productivity loss as soon as
FBHP reaches the P.sub.d.
[0107] Looking back at FIG. 5 it can be seen that the well restores
some of its productivity after about 5 years of production which is
the same behavior we have seen in the previous example. FIG. 26
shows the saturation profiles as a function of time which shows the
re-evaporation process.
[0108] It can be seen through the examples that productivity ratio
is approximately equal to K.sub.rg estimated at S.sub.o, (or
S.sub.o*) for each set of relative permeability curves. A number of
relative permeability curves (over 20 sets of curves) ranging from
X-curves (Fractures), through Intermediate and ending up with tight
relative permeability curves. A sensitivity study also examined the
effects of fluid richness on gas productivity by using two fluid
compositions (Lean and Rich fluids).
[0109] The results of the sensitivity study have been checked with
simulation results. The simulation runs have been done under
Constant Rate mode of production utilizing the Fine Compositional
Radial Model. Testing this wide range of relative permeability
curves has confirmed that indeed a very strong correlation exists
between the Productivity Index Ratio and K.sub.rg(S.sub.o*). FIGS.
7 and 8 show clearly that for both Rich and Lean fluids, the
relationship between the PI Ratio and K.sub.rg (S.sub.o*) is linear
with a correlation coefficient close to one.
[0110] Another important outcome of this sensitivity analysis is
that the loss in productivity is more sensitive to the relative
permeability curves than to fluid pressure-volume-temperature or
PVT properties. FIG. 9 shows the well PI versus time for the Rich
and Lean fluids using the same relative permeability set. FIG. 9
also shows an example of what was observed by testing the wide
range of relative permeability curves, which is that by using the
same relative permeability set, the Rich and Lean fluids have the
same effect. This confirms that it is the relative permeabilities
which are most important in determining the productivity loss.
[0111] FIG. 10 summarizes the results of the sensitivity study done
on the Rich and Lean fluids by using the wide range of relative
permeability curves. FIG. 10 shows clearly that for each set of
relative permeability used, the Rich and Lean fluids have the same
productivity ratio and hence the same productivity loss.
[0112] Application of the methodology described above is now
presented for a field case. Both compositional model data and
relative permeability curves have been provided for this field
case. A nine component compositional model is being used with
Peng-Robinson equation of state (PR3) to simulate phase behavior
and laboratory experiment (constant composition expansion) are
shown in Table 5. Tables 5 and 6 show fluid composition and
properties and for the field case, respectively.
TABLE-US-00005 TABLE 5 Fluid Composition for the Field Composition
Component (Fraction) `H25` 0 `CO2` 0.0279 `N2` 0.0345 `C1` 0.7798
`C2-C3` 0.1172 `C4-C6` 0.215 `C7-C9` 0.0132 `C10-C19` 0.00445
`C20+` 0.00145
TABLE-US-00006 TABLE 6 Fluid Properties for the Field Case Field
Case Initial Reservoir Pressure (psia) 9000 Dew Point Pressure
(psia) 8424 Reservoir Temperature (.degree. F.) 305 Maximum Liquid
Dropout (%) 3
[0113] The Relative permeability curves are shown in FIG. 28. As it
is common in field applications what matters here is the Threshold
(S.sub.o*). Although S.sub.or=0.20, the Threshold (S.sub.o*)=0.32
which corresponds to about K.sub.ro=1% as a practical value. As has
been mentioned earlier above, accurate estimation of gas
productivity depends on the value of K.sub.rg estimated at
Threshold (S.sub.o*) which equals 0.32 in this example.
[0114] In situations where work is in a production environment in a
field where no knowledge is available about relative permeability
curves, and the only thing available is some production data, a
procedure as follows is used. Since initial reservoir pressure is
above the P.sub.d, it is known that the pseudopressure versus gas
rate plot will have two straight lines as explained earlier.
Therefore, in order to generate an IPR curve for a given reservoir
pressure, all that is needed are two test points. One point should
be above the P.sub.d and the other point should be below the
P.sub.d.
[0115] FIG. 29 shows an example of two production data tests. One
of the test data points as chosen to be at the P.sub.d. It should
be understood that any available test data above the P.sub.d is
suitable for this purpose.
[0116] The productivity index (J) is estimated utilizing P.sub.r
and the test data at the P.sub.d using the following equation:
J = q sc [ m ( p r ) - m ( p wf ) ] ( 21 ) ##EQU00015##
[0117] Another way to estimate J is to plot the test points above
the P.sub.d on the pseudopressure plot as shown in FIG. 30, and
then J can be calculated from the following equation:
slope = - 1 J ( 22 ) ##EQU00016##
[0118] Based on the value of J so determined, one is then able to
generate the first portion of the IPR curve using the following
equation:
q=[m(P.sub.r)-m(P.sub.wf)]J* (23)
where q is in (MMscfd), m(P.sub.r) and m(.sub.pwf) are in
(psi2/cp), and J* in (MMscfd/psi2/cp).
[0119] Then the test points below the P.sub.d are plotted on the
pseudopressure plots as shown in FIG. 31. Then J* can be determined
from the slope in the manner previously described. The generated
IPR curve and the pseudopressure plot are shown in FIGS. 32 and 33
respectively.
[0120] A flowchart F (FIG. 36) indicates the basic computer
processing sequence of the present invention and the computation
taking place in a data processing system D (FIG. 39) for prediction
of performance of gas condensate reservoirs according to the
present invention. The processing sequence of the flow chart F is
performed separately for wells in the reservoir of interest in the
gas condensate reservoir.
[0121] Receive and Store Input Data (Step 100): During step 100,
the data processing system D receives and stores in memory input
data of the types set forth above about the gas condensate well,
including constant composition expansion data, rock permeability
data, reservoir pressure data.
[0122] Initial Reservoir Pressure Above Dew Point Decision (Step
102): During step 102, a determination is made whether the initial
reservoir pressure is above the dew point P.sub.d for the gas
condensate well fluid.
[0123] Form Single Phase Gas Rate Estimate (Step 104): If the
initial reservoir pressure is above the dew point, processing
proceeds to step 104 for forming a gas rate estimate for single
phase fluid. Further details of step 104 are shown in FIG. 37 and
described below.
[0124] Form Two Phase Gas Rate Estimate (Step 106):If the initial
reservoir pressure is determined during step 102 to be below the
dew point, processing proceeds to step 106 for forming a gas rate
estimate for single phase fluid. Further details of step 106 are
shown in FIG. 38 and described below.
[0125] Store/Display Gas Rate Estimate (Step 108): After gas rate
estimates are formed during either step 104 or 106, during step 108
the gas rate estimates so determined are stored in memory of the
data processing system D and are available for display for use by
analysts and engineers.
[0126] Gas Rate Estimate for Single Phase (Step 104): The
processing steps for determination or forming of gas rate estimates
for a single phase fluid of step 104 are set forth in FIG. 37. As
has been discussed above, the productivity index is constant in
this case as indicated at step 110, and the pseudo steady state gas
rate equation (Equation 2) is used as indicated at step 112 to
determine an estimate of the gas rate. Processing then proceeds to
step 108, as noted above.
[0127] Gas Rate Estimate for Two Phase (Step 106): The processing
steps for determination or forming of gas rate estimates for a
single phase fluid of step 106 are set forth in FIG. 38. As
indicated, an estimate of the productivity index J for single phase
flow is formed in the manner described with respect to Equation 11
during step 130. During step 132 an estimate of the productivity
index J* for two phase flow is formed as described above. During
step 134 an estimate of gas relative permeability k.sub.rg at the
corresponding pressure and oil saturation is formed by the data
processing system D according to Equation 17. During step 136, an
estimate of the gas rate is determined in the data processing
system D according to the relationship expressed in Equation 18.
Processing then proceeds to step 108, as noted above.
Data Processing
[0128] As illustrated in FIG. 39, the data processing system D
according to the present invention includes a computer C having a
processor 200 and memory 202 coupled to the processor 200 to store
operating instructions, control information and database records
therein. The computer C may, if desired, be a portable digital
processor, such as a personal computer in the form of a laptop
computer, notebook computer or other suitable programmed or
programmable digital data processing apparatus, such as a desktop
computer. It should also be understood that the computer C may be a
multicore processor with nodes such as those from Intel Corporation
or Advanced Micro Devices (AMD), an HPC Linux cluster computer or a
mainframe computer of any conventional type of suitable processing
capacity such as those available from International Business
Machines (IBM) of Armonk, N.Y. or other source.
[0129] The computer C has a user interface 204 and an output data
display 206 for displaying output data or records of predicted gas
performance of the gas condensate reservoir according to the
present invention. The output display 206 includes components such
as a printer and an output display screen capable of providing
printed output information or visible displays in the form of
graphs, data sheets, graphical images, data plots and the like as
output records or images.
[0130] The user interface 204 of computer C also includes a
suitable user input device or input/output control unit 208 to
provide a user access to control or access information and database
records and operate the computer C. Data processing system D
further includes a database 210 stored in computer memory, which
may be internal memory 202, or an external, networked, or
non-networked memory as indicated at 212 in an associated database
server 214.
[0131] The data processing system D includes program code 216
stored in non-transitory form in memory 202 of the computer C. The
program code 216 according to the present invention is in the form
of non-transitory computer operable instructions causing the data
processor 200 to perform the computer implemented method of the
present invention in the manner described above and illustrated in
FIGS. 36, 37 and 38.
[0132] It should be noted that program code 216 may be in the form
of microcode, programs, routines, or symbolic computer operable
languages that provide a specific set of ordered operations that
control the functioning of the data processing system D and direct
its operation. The instructions of program code 216 may be may be
stored in non-transitory form in memory 202 of the computer C, or
on computer diskette, magnetic tape, conventional hard disk drive,
electronic read-only memory, optical storage device, or other
appropriate non-transitory data storage device having a computer
usable medium stored thereon. Program code 216 may also be
contained on a data storage device such as server 218 as a
non-transitory computer readable medium.
[0133] The data processing system D can be a computer of any
conventional type of suitable processing capacity, such as a
mainframe, a personal computer, laptop computer, or any other
suitable processing apparatus. It should thus be understood that a
number of commercially available data processing systems and types
of computers may be used for this purpose.
[0134] From the foregoing, it can be seen that the present
invention provides a new analytical procedure is provided to
predict or estimate well deliverability of gas condensate
reservoirs. The present invention analytically generates inflow
performance relationship or IPR measures, which can be plotted as
curves, of gas condensate wells by incorporating the effect of
condensate banking as the pressure near the well bore drops below
dew point. Other than basic reservoir properties, the information
needed to generate the IPR measures is rock relative permeability
data and data from Constant Composition Expansion (CCE) experiments
on gas condensate reservoir fluids.
[0135] As has been described, it has been found that the most
important parameter in determining productivity loss is the gas
relative permeability at immobile oil saturation. It has also been
observed that at low reservoir pressures some of the accumulated
liquid near the well bore re-vaporizes. This revaporization can be
captured by using CCE data.
[0136] As described, the present invention provides two ways of
predicting IPR curves. One method involves an approach using the
basic reservoir properties, relative permeability data and CCE
information, so that one can predict IPR curves for the entire
pressure range. Comparison with simulation results validates this
approach.
[0137] Another method uses field data to predict the IPR curves
above and below the dew point pressure. This method does not
require reservoir data; instead, it uses point information from the
IPR curve and predicts the IPR curve for the entire bottom hole
pressure range. Both synthetic and field data are used to validate
this second approach. In addition to predicting the IPR curve under
current conditions, the present invention can also predict future
IPR curves if CCE data are available.
[0138] With the present invention a simple yet accurate analytical
methodology is provided to estimate the predicted performance and
in particular gas rate productivity to estimate the productivity of
gas condensate reservoirs without having to run reservoirs
simulations. Further, with the present invention, the production
rate can be determined based on knowledge obtained about the well
relatively simply. Data in the form of well pressures, CCE
(Constant Composition Expansion) data and formation relative
permeability data or curves are the required input data for gas
rate performance prediction according to the present invention. The
present invention allows well performance evaluation quickly
without time consuming reservoir simulations of reservoir gas
presence and states.
[0139] The invention has been sufficiently described so that a
person with average knowledge in the matter may reproduce and
obtain the results mentioned in the invention herein Nonetheless,
any skilled person in the field of technique, subject of the
invention herein, may carry out modifications not described in the
request herein, to apply these modifications to a determined
methodology, or in the performance of the same, requires the
claimed matter in the following claims; such techniques and
procedures shall be covered within the scope of the invention.
[0140] It should be noted and understood that there can be
improvements and modifications made of the present invention
described in detail above without departing from the spirit or
scope of the invention as set forth in the accompanying claims.
* * * * *