SYSTEMS AND METHODS FOR ESTIMATING FLUID BREAKTHROUGH TIMES AT PRODUCING WELL LOCATIONS

SYSTEMES ET PROCEDES D'EVALUATION DE DUREES DE PERCEE DE FLUIDE DES EMPLACEMENTS DE PUITS DE PRODUCTION

English Abstract

Systems and methods for estimating fluid breakthrough times at producing well locations based on fluid propagation simulation.

French Abstract

Systèmes et procédés d'évaluation de durées de percée de fluide à des emplacements de puits de production sur la base d'une simulation de propagation de fluide.

Claims

CLAIMS
1. A method for estimating a fluid breakthrough time at a production well
based on
fluid propagation simulation data, comprising:
identifying streamline tracking data;
calculating an average streamline travel time in each grid-cell based on the
streamline tracking data;
identifying a shortest or fastest streamline for the production well using the
average streamline travel time in each grid-cell;
calculating an average time-of-flight for the shortest or fastest streamline
over
each traversed grid-cell using a computer processor; and
estimating the fluid breakthrough time at the production well using the fluid
propagation simulation data and the average time-of-flight for the shortest or
fastest
streamline.
2. The method of claim 1, wherein the fluid propagation simulation data
comprises a
fluid invasion time represented by a number of simulation iterations needed
for a fluid to reach
the production well from an injection well through one or more grid-cells
representing a
reservoir property model.
3. The method of claim 1, wherein the streamline tracking data comprises a
number
of streamline segments traversing each grid-cell, a travel time for each
streamline segment in
each grid-cell, indices for each grid-cell and a total number of grid-cells
traversed by all
streamlines connecting an injection well with a production well.
4. The method of claim 3, wherein the average streamline travel time in
each grid-
cell is calculated by:
18

<IMG>
wherein (N SLN) represents the number of streamline segments traversing each
<IMG>
grid-cell and represents the travel time for each streamline segment in
each grid-cell.
5. The method of claim 1, wherein the shortest or fastest streamline for
the
production well represents a streamline with a lowest sum of average
streamline travel times in
grid-cells the streamline traverses between an injection well and the
production well.
6. The method of claim 5, wherein the average time-of-flight for the
shortest or
fastest streamline is calculated over each traversed grid-cell using the
lowest sum of average
streamline travel times for the shortest or fastest streamline and a total
number of grid-cells
traversed by the shortest or fastest streamline.
7. The method of claim 6, wherein the average time-of-flight for the
shortest or
fastest streamline is calculated by:
<IMG>
wherein <IMG> represents the total number of all grid-cells traversed by the
shortest or fastest
streamline, <IMG> represents the lowest sum of average streamline travel times
for the
shortest or fastest streamline and (u) represents a number of runs over all
indices of grid-cells
traversed by the shortest or fastest streamline.
19

8. The method of claim 2, wherein the fluid breakthrough time at the
production
well is estimated by:
<IMG>
wherein (N xyz) and (N p) represent a total size of the reservoir property
model and a total number
of production wells, respectively, (<TOF>min) represents the average time-of-
flight for the
shortest or fastest streamline, <IMG> represents a total number of grid-cells
traversed by
all streamlines connecting an injection well with the production well and
<IMG> represents
the fluid invasion time.
9. The method of claim 1, further comprising repeating the steps in claim 1
for each
production well.
10. The method of claim 1, wherein the reservoir property model is a
permeability
model.
11. A non-transitory carrier device tangibly carrying computer executable
instructions
for estimating a fluid breakthrough time at a production well based on fluid
propagation
simulation data, the instructions being executable to implement:
identifying streamline tracking data;
calculating an average streamline travel time in each grid-cell based on the
streamline tracking data;
identifying a shortest or fastest streamline for the production well using the
average streamline travel time in each grid-cell;

calculating an average time-of-flight for the shortest or fastest streamline
over
each traversed grid-cell; and
estimating the fluid breakthrough time at the production well using the fluid
propagation simulation data and the average time-of-flight for the shortest or
fastest
streamline.
12. The program carrier device of claim 11, wherein the fluid propagation
simulation
data comprises a fluid invasion time represented by a number of simulation
iterations needed for
a fluid to reach the production well from an injection well through one or
more grid-cells
representing a reservoir property model.
13. The program carrier device of claim 11, wherein the streamline tracking
data
comprises a number of streamline segments traversing each grid-cell, a travel
time for each
streamline segment in each grid-cell, indices for each grid-cell and a total
number of grid-cells
traversed by all streamlines connecting an injection well with a production
well.
14. The program carrier device of claim 13, wherein the average streamline
travel
time in each grid-cell is calculated by:
<IMG>
wherein (N sLN) is the number of streamline segments traversing each grid-cell
and <IMG>
represents the travel time for each streamline segment in each grid-cell.
15. The program carrier device of claim 11, wherein the shortest or fastest
streamline
for the production well represents a streamline with a lowest sum of average
streamline travel
times in grid-cells the streamline traverses between an injection well and the
production well.
21

16. The program carrier device of claim 15, wherein the average time-of-
flight for the
shortest or fastest streamline is calculated over each traversed grid-cell
using the lowest sum of
average streamline travel times for the shortest or fastest streamline and a
total number of grid-
cells traversed by the shortest or fastest streamline.
17. The program carrier device of claim 16, wherein the average time-of-
flight for the
shortest or fastest streamline is calculated by:
<IMG>
wherein (<IMG> ) represents the total number of all grid-cells traversed by
the shortest or fastest
streamline, (~~ min ) represents the lowest sum of average streamline travel
times for the
shortest or fastest streamline and (u) represents a number of runs over all
indices of grid-cells
traversed by the shortest or fastest streamline.
18. The program carrier device of claim 12, wherein the fluid breakthrough
time at
the production well is estimated by:
<IMG>
wherein (N xyz) and (N p) represent a total size of the reservoir property
model and a total number
of production wells, respectively, (<TOF>min) represents the average time-of-
flight for the
shortest or fastest streamline, <IMG> represents a total number of
grid-cells
22

traversed by all streamlines connecting an injection well with the production
well and <IMG>
represents the fluid invasion time.
19. The program carrier device of claim 11, further comprising repeating
the steps in
claim 1 for each production well.
20. The program carrier device of claim 11, wherein the reservoir property
model is a
permeability model.
23

Description

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SYSTEMS AND METHODS FOR ESTIMATING FLUID
BREAKTHROUGH TIMES AT PRODUCING WELL LOCATIONS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] None
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0002] Not applicable.
FIELD OF THE INVENTION
[0003] The present invention generally relates to estimating fluid
breakthrough times at
producing well locations. More particularly, the invention relates to
estimating fluid
breakthrough times at producing well locations based on fluid propagation
simulations.
BACKGROUND OF THE INVENTION
[0004] Various systems and methods are known for estimating the fluid
breakthrough
time at a producing well location, including history matching (HM). History
Matching (HM) is a
systematic procedure of altering a reservoir simulation model to reproduce the
dynamic field
response. In HM applications and conditioning reservoir models to production
data, the main
objectives are a) integration of production data into reservoir models; b)
flexibility, cost-
effectiveness and computational efficiency; and c) full utilization of dynamic
data.
[0005] In the last decade, HM technology has evolved tremendously and gained
major
recognition and expansion from the traditional (i.e. manual, deterministic)
approach, mostly built
on stratigraphic methods to new developments like probabilistic, streamline-
based HM,
sensitivity/gradient-based and experimental design.
[0006] HM workflows largely consider the minimization of the misfit between
the
measured and simulated fluid (e.g. oil or water) dynamic response at the
individual production
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well as one of the inversion main objectives. In water-flooding Enhanced Oil
Recovery (E0R)
studies, for example, the response misfit represents the differential or
cumulative water-cut
curves with two main attributes: 1) fluid breakthrough time; and 2) trend and
shape of the
response. While both attributes represent important variables in the process
of misfit
minimization, it is the fluid breakthrough time that bares the highest impact
on the economics of
the well production. Furthermore, the interval (i.e. time-frame) of the fluid
breakthrough is
always burdened with uncertainty, which makes the effort of estimation with
highest confidence
possible, even more relevant. In fact, it is a good practice in HM of dynamic
well data to
consider the breakthrough time as the first-order effect and the variations in
curve trend/shape as
the second-order effect, because they mainly reflect on the operating
conditions.
[0007] Despite the progress in HM technology, it is still by far the most time-
consuming
aspect of the model building/simulation study and the HM workflow faces many
difficulties,
which include:
i) non-linear results between the production response and reservoir
parameters;
ii) non-unique solutions, which require a definition of some semblance of
"uniqueness";
iii) the relative impact of key parameters may not be obvious;
iv) constraints are not bounded and uncertainties and in the variables are
seldom known;
and
v) the production data may be noisy and inherently biased.
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SUMMARY OF THE INVENTION
[0008] The present invention therefore, meets the above needs and overcomes
one or
more deficiencies in the prior art by providing systems and methods for
estimating fluid
breakthrough times at producing well locations based on fluid propagation
simulations.
[0009] In one embodiment, the present invention includes a method for
estimating a fluid
breakthrough time at a production well based on fluid propagation simulation
data, comprising:
i) identifying streamline tracking data; ii) calculating an average streamline
travel time in each
grid-cell based on the streamline tracking data; iii) identifying a shortest
or fastest streamline for
the production well using the average streamline travel time in each grid-
cell; iv) calculating an
average time-of-flight for the shortest or fastest streamline over each
traversed grid-cell using a
computer processor; and v) estimating the fluid breakthrough time at the
production well using
the fluid propagation simulation data, and the average time-of-flight for the
shortest or fastest
streamline.
[0010] In another embodiment, the present invention includes a non-transitory
program
carrier device tangibly carrying computer executable instructions for
estimating a fluid
breakthrough time at a production well. The instructions being executable to
implement: i)
identifying streamline tracking data; ii) calculating an average streamline
travel time in each
grid-cell based on the streamline tracking data; iii) identifying a shortest
or fastest streamline for
the production well using the average streamline travel time in each grid-
cell; iv) calculating an
average time-of-flight for the shortest or fastest streamline over each
traversed grid-cell using;
and v) estimating the fluid breakthrough time at the production well using the
fluid propagation
simulation data, and the average time-of-flight for the shortest or fastest
streamline.
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[0011] Additional aspects, advantages and embodiments of the invention will
become
apparent to those skilled in the art from the following description of the
various embodiments
and related drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The present invention is described below with references to the
accompanying
drawings in which like elements are referenced with like reference numerals,
and in which:
[0013] FIG. 1 is a flow diagram illustrating one embodiment of a method for
implementing the present invention.
[0014] FIG. 2A illustrates the velocity and the direction of fluid propagating
through a
wide sand pocket.
[0015] FIG. 2B illustrates the velocity and the direction of fluid propagating
through a
narrow sand pocket.
[0016] FIG. 3 illustrates an example of fluid propagation through a sand
fraction of a
facies model during the initial stage of simulation.
[0017] FIG. 4A illustrates a synthetic 2D permeability model with 2500 grid-
cells
(50x50) and a 5-spot-pattern of wells (linjection well (I) and 4 production
wells (Pi-P4)).
[0018] Fig. 4B illustrates a simulation of fluid propagation through the 2D
permeability
model in FIG. 4A from the injector well (I) in terms of the number of
iterations (2500) that the
simulation was run.
[0019] FIG. 5 illustrates a possible streamline distribution in the 5-spot-
pattern of wells
in FIG. 4B.
[0020] FIG. 6 illustrates the streamline travel time along its arc length
within a given
grid-cell (i,j,k) of a 2D permeability model.
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[0021] FIG. 7A illustrates the observed (measured) water-cut curve for the
producing
well P1 in FIG. 4A.
[0022] FIG. 7B illustrates the observed (measured) water-cut curve for the
producing
well P2 in FIG. 4A.
[0023] FIG. 7C illustrates the observed (measured) water-cut curve for the
producing
well P3 in FIG. 4A.
[0024] FIG. 7D illustrates the observed (measured) water-cut curve for the
producing
well P4 in FIG. 4A.
[0025] FIG. 8 is a block diagram illustrating one embodiment of a system for
implementing the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0026] The subject matter of the present invention is described with
specificity,
however, the description itself is not intended to limit the scope of the
invention. The subject
matter thus, might also be embodied in other ways, to include different steps
or combinations of
steps similar to the ones described herein, in conjunction with other present
or future
technologies. Moreover, although the term "step" may be used herein to
describe different
elements of methods employed, the term should not be interpreted as implying
any particular
order among or between various steps herein disclosed unless otherwise
expressly limited by the
description to a particular order. While the present invention may be applied
in the oil and gas
industry, it is not limited thereto and may also be applied in other
industries to achieve similar
results.
[0027] The present invention includes systems and methods to estimate fluid
breakthrough times at producing well locations based on the simulation of
fluid propagation.

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The present invention includes a fluid propagation simulation, which is
generally static and
renders the invasion time(s) for fluid injected at the injection well(s) to
reach the production
well(s). The simulation affords full consideration of facies modeling, which
preserves control
over depositional continuity of geological models by directly constraining the
simulation with
the facies distribution. The simulation also preserves the stochasticity of
the fluid front
propogation. Despite the static nature of the simulation, the stochastic
sampling of the moving
fluid front is performed by using a uniform distribution.
[0028] The present invention converts the fluid invasion time(s) (given by the
simulation
in units of iterations) to the domain of physical time (given i.e, in days,
weeks, months...), which
is compatible with the well production history. The present invention
therefore, provides new
possibilities for the rapid estimation of valuable well production parameters
in a fast and cost-
effective manner. For example, a fast and accurate estimation of the fluid
breakthrough time(s)
associated with an individual reservoir model can be achieved prior to
commencing a full
inversion. Such an estimate would provide valuable information to the well
operators in terms of
,well valve dynamics, particularly in water/gas-flooding EOR projects where
the management of
oil and water/gas production bares a substantial economic impact.
[0029] In order to achieve rapid estimates of the fluid breakthrough time(s)
(TBT), the
present invention uses the combination of streamline tracking and associated
Time-Of-Flight
("TOF") with the simulation. The present invention therefore, enables quick
approximation of
fluid breakthrough times following the simulation run and one iteration of
streamline tracking in
the process of streamline-sensitivity assisted Automated History Matching
("AHM") of reservoir
models.
Method Description
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[0030] Referring now to FIG. 1, a flow diagram illustrates one embodiment of a
method
100 for implementing the present invention.
[0031] In step 102, fluid propagation simulation ("FPS") is performed. One
technique
for performing FPS is based on an algorithm in the RGeoS software package
developed by D.
Renard. The FPS algorithm simulates the distribution of several fluids known
at the injection
and/or production wells, which is conditioned by the facies information known
at the nodes of a
regular grid and tends to let the fluid encountered at the wells (e.g. the
injection well) to grow or
expand spatially. The velocity and the direction of growth depend on the size
of the sand pockets
that can be filled. In FIG. 2, for example, true velocity and the direction of
fluid propagating
through a wide sand pocket (FIG. 2A) and a narrow sand pocket (FIG. 2B) are
illustrated. The
larger the pocket 206, 208, the quicker the growth. Velocity vectors 202, 204
are utilized in the
FPS algorithm. The FPS algorithm is designed to perform one simulation of a
numerical
variable using the Eden simulation technique. The technique provides a faster
alternative
solution for a multiphase fluid flow simulation program. The technique
combines a dual medium
"black and white" example where white represents sand and black represents
shale with one or
more injection wells and one or more production wells as illustrated in FIG.
3. In this example,
the locations of sand facies 302, 304, 306, and of two injection wells 307,
308, are illustrated.
[0032] Referring now to FIG. 4A, a synthetic 2D permeability model is
illustrated with
2500 grid cells (50x50) and a 5- spot- pattern of wells (1 injection well (I)
and 4 production wells
(Pi-P4)). The FPS algorithm was executed in 2500 iterations because one cell
of the model is
populated per iteration. In FIG. 4B, a simulation of fluid propagation through
the 2D
permeability model in FIG. 4A from the injection well (I) is illustrated in
terms of the number of
iterations (2500) the simulation was run. In FIG. 5, one possible streamline
distribution in the 5-
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spot-pattern of wells in FIG. 4B is illustrated.
[0033] In order to implement the FPS algorithm as a rapid proxy estimate of
the fluid
breakthrough time in AHM inversion of water-cut curves, the conversions of the
fluid invasion
time(s) to the domain of physical time(s) has to be considered with the
following main
assumptions:
i) streamline TOF represents a crucial normalization factor;
ii) tracking TOF from the production well(s) indicates the drainage volume;
and
iii) tracking fluid from the injection well gives an assessment of swept
volume.
[0034] For the estimation of fluid breakthrough time in a production well, it
is assumed
that the following computations are completed for a given reservoir model
using any technique
well known in the art to track streamlines based on a forward simulation of
fluid pressure and
velocity: a) the computation of fluid invasion time (i.e. step 102); and b)
the first iteration of
streamline tracking and TOF computation (i.e. step 106). These computations
will provide a) the
fluid invasion time from the FPS algorithm given by the number of simulation
iterations
(assuming 1 iteration per grid-cell); and b) the total number of streamlines
traversing any
reservoir model grid-cell with (i,j,k) coordinates.
[0035] In step 104, the FPS data results from step 102 are identified, which
includes the
fluid invasion time given by the number of simulation iterations needed for
the fluid to reach any
production well (Pm) from an injection well through one or more grid-cells
representing the
reservoir property model.
[0036] In step 106, the streamline tracking data are identified using any well
known
technique, which include the number of streamline segments traversing each
grid-cell (NsLN), the
travel time (&z) for each streamline segment ( tgli;j,;,k ) in each grid-cell,
the grid-cell indices and
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the total number of grid-cells traversed by all streamlines connecting an
injection well with a
production well. Referring now to FIG. 6, the streamline travel time along its
arc length within a
given grid-cell of a 2D permeability model is illustrated. Indices (n) and (m)
run over all
streamline segments in each grid-cell and all production wells, respectively
(n = [1..Nsud and
m=[1..Np]). Travel time ar(y,i,;j,;,k ) for a streamline segment in each grid-
cell may be calculated
by integrating the "slowness" of the streamline tracer along each streamline
trajectory using the
following equation:
fasoodr (1)
where as(x) corresponds to the "slowness" of the streamline tracer (defined as
the inverse of the
tracer velocity) and dr corresponds to the arc length of the streamline
segment ( vr;i,i,k ) between
the inlet and outlet locations on the bounding surface of the grid-cell with
(i,j,k) coordinates.
[0037] In step 108, the average streamline travel time in each grid-cell ( )
is calculated
by taking into account all streamline segments traversing each grid-cell,
which may be calculated
using the following equation:
NSLN
E i
= _________________________ ,j,k\
ni,n (2)
N SLN n=1
where (NsLN) is the number of streamline segments traversing each grid-cell
from step 106 and
ar(ctfn,i ) is the travel time for each streamline segment in each grid-
cell from step 106.
[0038] In step 114, the shortest/fastest streamline is identified for each
production well
(Pm) using the average streamline travel time in each grid-cell from step 108
and any well-known
searching algorithm. The shortest/fastest streamline is the streamline with
the lowest sum of
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average streamline travel times (of min ) in the grid-cells the streamline
traverses between an
injection well (I) and a production well (Pm).
[0039] In step 116, the total number of all grid-cells (1Q.Gc min) traversed
by the
shortest/fastest streamline identified in step 114, and their indices from
step 106, are stored.
[0040] In step 118, the average TOF (<TOF>min) for the shortest/fastest
streamline
identified in step 114 is calculated over each traversed grid-cell using the
lowest sum of average
streamline travel times (aimin ) for the shortest/fastest streamline
identified in step 114 and the
total number of all grid-cells (11'GC min) stored in step 116, which may be
calculated using the
following equation:
kun
1 V¨IGC ( min TOF)min ¨ L ay,
(3)
Armin
-"GC u=1
where index (u) represents the number of runs over all indices of grid-cells
traversed by the
shortest/fastest streamline. The distinction between the "fastest" and the
"slowest" streamline
from the distribution of streamlines associated with each production well (Pm)
is relevant to
discriminate between the homogeneous and heterogeneous spatial distribution of
reservoir
properties such as, for example, channels. The difference between the
distribution of streamlines
in FIG. 5 reveals that production wells P2 and P3 are connected with injection
well (I) through a
distinctively different geological formation than production wells P1 and P4,
which might
correspond to an underlying channel structure.
[0041] In step 120, the method 100 determines if all grid-cells traversed by
the
shortest/fastest streamline have been considered. If all traversed grid-cells
have not been
considered, then the method 100 returns to step 118. If all traversed grid-
cells have been

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considered, then the method 100 proceeds to step 124, Alternatively, steps 118
through 120 may
be performed at the same time for each traversed grid-cell.
[0042] In step 124, an estimate of the fluid breakthrough time for each
production well
(Pm) is calculated by combining the streamline tracking data from step 106
with the FPS data
from step 104, which may be calculated using the following equation:
Nm
TBT = (TOTrin x INV x SLN (4)
N N
xyz
where (1\1) and (Np) represent the total size of the reservoir property model
and the total
number of production wells, respectively, (<TOF>min) represents the average
TOF for the
shortest/fastest streamline calculated in step 118, (NN) represents the total
number of grid-
cells traversed by all streamlines connecting injection well (I) with a
production well (Pm) and
(tile ) represents the fluid invasion time from step 104.
[0043] In step 126, the method 100 determines if all production wells have
been
considered. If all production wells (Pm) have not been considered, then the
method 100 returns
to step 104. If all production wells (Pm) have been considered, then the
method 100 ends.
Alternatively, steps 104 through 126 may be performed at the same time for
each production
well (Pm).
EXAMPLE
[0044] Referring now to the synthetic 2D permeability model in FIG. 4A, the
observed
(measured) water-cut curves for the configuration model in FIG 4A are given in
FIGS. 7A, 7B,
7C, and 7D for each of the four production wells (Pi, P2, P3, and P4).
[0045] The date/time data points on the x-axis in FIGS. 7A-7D correspond with
the
physical dates associated with the water injection plan (water breakthrough
data points)
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presented in Table 1 below:
Data point Physical Date (dd/mm/yyyy)
1 17/9/2000
2 4/6/2001
3 19/2/2002
4 6/10/2002
5 24/7/2003
6 9/4/2004
7 25/12/2004
8 11/9/2005
Table 1.
[0046] The observed water breakthrough times deduced from FIG 4A, are given in
Table
2 below. Moreover, Table 2 lists the water invasion times calculated by the
FPS algorithm the
water breakthrough times (TB7) calculated using the proposed method in FIG. 1
and the
uncertainty associated with result obtained by the proposed method in FIG. 1.
Producer Observed TBT Invasion Time TBT by proposed
Uncertainty
(days) (iterations) method (days) (days/%)
P1 263 2272 281.41
18.41/+7%
P2 121 2027 124.63
15.73/+3%
P3 263 2268 239.33 -
23.67/-9%
P4 1043 2491 1105.58
62.58/+6%
Table 2.
[0047] The results indicate that the proposed method in FIG. 1 is capable of
rapidly
predicting the fluid breakthrough time with an uncertainty of less than 10%
for the given 5-spot-
pattern of wells. The achieved uncertainty could be different (larger/smaller)
when fluid
propagation is applied through the field with significantly higher geological
complexity and the
dynamic model combines a significantly large number of producing wells.
System Description
[0048] The present invention may be implemented through a computer-executable
program of instructions, such as program modules, generally referred to
software applications or
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application programs executed by a computer. The software may include, for
example, routines,
programs, objects, components, data structures, etc., that perform particular
tasks or implement
particular abstract data types. DecisionSpace Desktop, which is a commercial
software
application marketed by Landmark Graphics Corporation, may be used as an
interface
application to implement the present invention. The software may also
cooperate with other
code segments to initiate a variety of tasks in response to data received in
conjunction with the
source of the received data. The software may be stored and/or carried on any
variety of
memory such as CD-ROM, magnetic disk, bubble memory and semiconductor memory
(e.g.,
various types of RAM or ROM). Furthermore, the software and its results may be
transmitted
over a variety of carrier media such as optical fiber, metallic wire, and/or
through any of a
variety of networks, such as the Internet.
[0049] Moreover, those skilled in the art will appreciate that the invention
may be
practiced with a variety of computer-system configurations, including hand-
held devices,
multiprocessor systems, microprocessor-based or programmable-consumer
electronics,
minicomputers, mainframe computers, and the like. Any number of computer-
systems and
computer networks are acceptable for use with the present invention. The
invention may be
practiced in distributed-computing environments where tasks are performed by
remote-
processing devices that are linked through a communications network. In a
distributed-
computing environment, program modules may be located in both local and remote
computer-
storage media including memory storage devices. The present invention may
therefore, be
implemented in connection with various hardware, software, or a combination
thereof, in a
computer system or other processing system.
[0050] Referring now to FIG. 8, a block diagram illustrates one embodiment of
a system
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for implementing the present invention on a computer. The system includes a
computing unit,
sometimes referred to as a computing system, which contains memory,
application programs, a
client interface, a video interface, and a processing unit. The computing unit
is only one
example of a suitable computing environment and is not intended to suggest any
limitation as to
the scope of use or functionality of the invention.
[0051] The memory primarily stores the application programs, which may also be
described as program modules containing computer-executable instructions,
executed by the
computing unit for implementing the present invention described herein and
illustrated in FIG.
2. The memory therefore, includes a fluid breakthrough time estimating module,
which enables
the methods illustrated and described in reference to FIG. 1 and integrates
functionality from the
remaining application programs illustrated in FIG. 8. The fluid breakthrough
time estimating
module, for example, may be used to execute many of the functions described in
reference to the
method 100 in FIG. 1. Decision Space Desktop may be used, for example, as an
interface
application to implement the fluid breakthrough time estimating module and to
utilize the results
of the method 100 in FIG. 1.
[0052] Although the computing unit is shown as having a generalized memory,
the
computing unit typically includes a variety of computer readable media. By way
of example,
and not limitation, computer readable media may comprise computer storage
media The
computing system memory may include computer storage media in the form of
volatile and/or
nonvolatile memory such as a read only memory (ROM) and random access memory
(RAM). A
basic input/output system (BIOS), containing the basic routines that help to
transfer information
between elements within the computing unit, such as during start-up, is
typically stored in ROM.
The RAM typically contains data and/or program modules that are immediately
accessible to
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and/or presently being operated on by the processing unit. By way of example,
and not
limitation, the computing unit includes an operating system, application
programs, other program
modules, and program data.
[0053] The components shown in the memory may also be included in other
removable/non-removable, volatile/nonvolatile computer storage media or they
may be
implemented in the computing unit through an application program interface
("API") or cloud
computing, which may reside on a separate computing unit connected through a
computer
system or network. For example only, a hard disk drive may read from or write
to non-
removable, nonvolatile magnetic media, a magnetic disk drive may read from or
write to a
removable, non-volatile magnetic disk, and an optical disk drive may read from
or write to a
removable, nonvolatile optical disk such as a CD ROM or other optical media.
Other
removable/non-removable, volatile/non-volatile computer storage media that can
be used in the
exemplary operating environment may include, but are not limited to, magnetic
tape cassettes,
flash memory cards, digital versatile disks, digital video tape, solid state
RAM, solid state ROM,
and the like. The drives and their associated computer storage media discussed
above provide
storage of computer readable instructions, data structures, program modules
and other data for
the computing unit.
[0054] A client may enter commands and information into the computing unit
through
the client interface, which may be input devices such as a keyboard and
pointing device,
commonly referred to as a mouse, trackball or touch pad. Input devices may
include a
microphone, joystick, satellite dish, scanner, or the like. These and other
input devices are often
connected to the processing unit through a system bus, but may be connected by
other interface
and bus structures, such as a parallel port or a universal serial bus (USB).

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[0055] A monitor or other type of display device may be connected to the
system bus via
an interface, such as a video interface. A graphical user interface ("GUI")
may also be used with
the video interface to receive instructions from the client interface and
transmit instructions to
the processing unit. In addition to the monitor, computers may also include
other peripheral
output devices such as speakers and printer, which may be connected through an
output
peripheral interface.
[0056] Although many other internal components of the computing unit are not
shown,
those of ordinary skill in the art will appreciate that such components and
their interconnection
are well known.
[0057] While the present invention has been described in connection with
presently
preferred embodiments, it will be understood by those skilled in the art that
it is not intended to
limit the invention to those embodiments. It is therefore, contemplated that
various alternative
embodiments and modifications may be made to the disclosed embodiments without
departing
from the spirit and scope of the invention defined by the appended claims and
equivalents
thereof,
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Representative Drawing