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SPE 146984

Capacitance Resistive Model Application to Optimize Waterflood in a WestTexas Field Anh P. Nguyen, SPE , Leon S. Lasdon, Larry W. Lake, SPE, and Thomas F. Edgar, SPE, the University of Texasat Austin

Copyright 2011, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 30 October–2 November 2011.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenreviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

This paper describes the application of capacitance resistive modeling (CRM) to 99 wells in a waterflooded field in thePermian Basin of West Texas. CRM is a quantitative technique based on material balance that requires only injection,

production rates and well coordinates to identify and quantify interwell connectivity in a waterflood. The study uses CRMresults to optimize oil production by reallocating water injection and then assesses the resulting improvement by analyzingfield data after water injection has been rescheduled. This first field implementation of CRM theory shows that the oil

production rate has increased 45 bbl/day and the cumulative incremental oil production in the first year is 5,372 bbls,equivalent to $402,900 at $75/bbl.

This work serves as a demonstration of how to apply and analyze CRM results. Before optimizing injection, interwellconnectivities were established by fitting CRM over an appropriate fitting window selected using field events. The forecasting

ability of the model was examined by fitting part of the historical data and then using the fitted model to forecast the remainderof the historical data. CRM interwell connectivities were used to optimize future production for the next five years bychanging water injection rates. The optimized injection rates were applied for one year and the resulting production rates wereanalyzed.

The oil production rates increase after injection rates were rescheduled according to the CRM optimization. This first field testof CRM technology has demonstrated that CRM has the advantages of short computation time and of using readily availablefield data, and disadvantages of sensitivity to reservoir events and data errors. The connectivities are consistent with fieldgeological knowledge. CRM can be used to improve oil production with little additional cost.

The case study shows that CRM is a simple yet powerful tool for engineers in planning and monitoring waterflood. The workincreases confidence in the technique by demonstrating improvements made by using CRM in a practical context, andidentifying shortcomings of the technique. CRM should be applied with knowledge of the field geology and history to

understand the results and use them to enhance waterflood performance.

Introduction

Large reservoir waterflood management and capacitance resistive model

The traditional approach of reservoir simulation is based on the continuity equation and Darcy’s law for flow through porousmedia. Because of the number of required input data, traditional reservoir simulation studies may take months to set up anddays to complete one computer run. Especially for large mature waterflood fields with hundreds of wells, full reservoirsimulation is an enormous task or can only be done on separate well patterns. Furthermore, original geological data might nolonger be applicable and new measurements on a large mature field will require additional equipment and labor costs thatmight not be justifiable.

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An approach to characterizing the perforamce ofsuch large fields is a surrogate model that uses available field information.The capacitance resistive model (CRM) is derived from material balance to infer inter-well connectivity from injection and

production rates only. CRM characterizes waterflood based on two properties: the fraction of the injection that affects aspecific producer’s production – the gain and the time lag of the injection effect – the time constant. With these two

parameters, CRM not only identiiesy the connections, but also quantifies their strength. The CRM idea originates from thework of Albertoni and Lake (2003), which infers inter-well connectivity from well rate fluctuations using multivariate linearregression and balanced multivariate linear regression. The reservoir is viewed as a system that converts an input signal

(injection) into an output signal (production). Albertoni’s work opened a new direction of research to infer information from production and injection rates. Starting from a purely statistical relationship among producer and injector rates, Gentil(2005)tried to explain the physical meaning of the gain as the ratio of transmissibility in a producer-injector channel over the totaltransmissibility of the entire area surrounding an injector. Yousef(2006) proposed a model based on material balance thatincludes capacitance (compressibility) as well as resistive (transmissibility) effects and named it the Capacitance ResistiveModel (CRM). Sayapour(2008) developed an analytical CRM solution for different control volumes. Weber et al.(2009)developed a practical technique to pre-screen the data to remove inactive wells and outliers. The implemented algorithm thenfit each producer individually to find initial parameter values and repeated the fitting process three times to achieve consistentresults. Weber’s technique brought CRM theory into a practical context to work with large reservoirs with hundreds of wells.Although CRM was developed for mature waterflood, Izgec and Kabir (2009) extended the use of CRM to immaturewaterfloods and found good agreement with streamline simulation results. Up to now, CRM has been validated extensively onvariety of synthetic and actual field data and is a good candidate for field application. This paper will present the first fieldexperiment using CRM to optimize water injection in a field in West Texas.

Capacitance resistive model theory

CRM is derived from the continuity equation based on several assumptions: stabilized flow, i.e: no new wells have been addedor removed; no aquifer; incompressible flow. Although the assumptions are essential for the equation to apply, many fields cansatisfy the last two assumptions but not the first one. Hence, in practice, the first assumption will be relaxed to a reasonableextent. The CRM equation used here follows Sayapour(2008)’s paper:

, , 1

1 1 1 j j j

t t

wf jk wf jk

jk jk ij ik p t

i

P P q q e f i e V c e

t

τ τ

⎛ ⎞ ⎛ ⎞ ⎛ ⎞Δ Δ−⎜ ⎟ −⎜ ⎟ −⎜ ⎟

⎜ ⎟ ⎜ ⎟ ⎜ ⎟−⎝ ⎠ ⎝ ⎠ ⎝ ⎠−

⎛ ⎞ ⎛ ⎞−⎛ ⎞⎜ ⎟ ⎜= + − − −⎜ ⎟⎜ ⎟ ⎜Δ⎝ ⎠⎝ ⎠ ⎝ ⎠

∑t

τ

Δ

⎟⎟

(1)

CRM contains the sum of three terms: the first accounts for exponential decline as in primary recovery production, the secondcontains injection rate, and the third covers the effect of change in bottom hole pressure (BHP). CRM has several differentforms because of different ways of treating the integral term and control volume. Details on other forms of CRM are given inSayapour’s dissertation (2008). The CRM equation above accounts for control volume surrounding a producer and assumelinear variation bottom hole pressure and constant injection rate from time step t0 to time step t. The equation is based on thechange in average pressure of a constant volume surrounding a producer. The pressure change is due to the producer’s rate andinflow from neighboring injectors. We can also assume that producer bottom-hole pressure does not vary much and drop thelast term. This assumption is also helpful in practice as many old waterflood fields are not equipped with a bottom-hole

pressure gauges.

Fluid production rates and injection rates are used to fit the CRM equation. The two parameters that result from the CRMfitting process are gain f ij and time constant τ as defined above. These two parameters can be used to calculate future

production rate based on pre-determined injection rates or to optimize future production rate by treating future injection rates

as variables. Injection rates are optimized on the basis of production net present value. The oil rate is calculated from anempirical fractional flow (FFM) model proposed by Gentil(2005):

1

1 joj b

j j

f a CWI

=+

(2)

The oil cut and cumulative injection can be determined from historical data. Knowing the oil cut, we can calculate oil production rate follows:

j oj jqo f q= (3)

The two variables are fitted to find a j and b j. a j and b j are then used to optimize future production. The objective function to

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maximize net present value follows Weber (2009):

( )0

1 1

max1

pt i

t

nnf n

k jk w ik k

k n j i

t NPV p qo p I

ir = = =

⎛ ⎞⎛ ⎞ Δ= − ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟+⎝ ⎠⎝

∑ ∑ ∑ ⎠

(4)

In this objective function, the oil production rate is calculated from the CRM equation and the fractional flow model. Since both CRM and FFM equations contain future injection rates, the objective function is actually a function of future injectionrates Iik only. Optimizing the objective function gives us future injection rates Iik .

Capacitance resistive study on a field in West Texas

To apply the CRM theory to a field, an analysis of the model behavior on the field has been carried out. The procedure utilizesreservoir engineering techniques to evaluate the model and obtain the best parameter estimates. The field consists of 209

producers and 135 injectors. Primary production started from 1947 and water injection commenced in 1964. The field is amature waterflood with average oil cut of 0.05 with inverted five spot well patern. The data used in this study are monthlyallocated production rates. The data preparation and fitting technique follow the method suggested by Weber (2009), in whichdata were screened before being fitted by identifying and removing liquid and oil rates outliers; the fit is restarted after wellshut-in. CRM equation is first fitted to find the resulting parameters as initial guess in the following fit. Small gains are

removed to reduce number of fitting parameters and residual outliers are also replaced by the average of adjacent datapoints.CRM is then refit to find a smaller set of parameters and this procedure is repeated three times. A similar method applies toFFM and the model is fitted two times. Figure 1 summarizes the fitting algorithm that was presented in Weber’sdissertation(2008).

In this work, theCRM theory has been evaluated based on consistency, sensitivity analysis, and reliability to forecast andoptimize future production on an actual field. It is important to obtain a relevant set of gains and time constants to be able to

predict and optimize future production. The best validation of CRM results is tracer test results, although tracer tests can onlyconfirm injector-producer connection if the tracer is detected in a producer, not the strength of the connection. However, tracertests are expensive and not feasible on a large scale application. The technique to evaluate CRM and FFM applicability on thisfield is described below. The study leads to recommended future injection rates to optimize production NPV.

Consistency

To check the consistency of the CRM fitting, different fitting windows have been used to compare the estimated parameters.First, CRM equation was fitted on a recent 10 years fitting window from 1998 to 2009. The fitting window is then moved

backward two years from 1996 to 2007. Figures 2 and 3 show gain matrices from the two fitting windows; the gain varies in both magnitude and direction. Differences in the gain matrices are caused by changes in the injection rates, new well added orwell shut-in. Absolute difference of the gain matrices from two different fitting windows has been obtained to observe gainvariation with time as in Figure 4. The gain matrix in Figure 4 shows that connectivity changes in the south and southwest areaof the field are quite significant. This is an under-performed area in the field with many well events.

Sensitivity Analysis

The CRM equation considers each producer with its neighboring injectors. In large fields, neighboring injectors are defined bya specific distance surrounding the producer named the radial limit. A radial limit helps reduce the number of parameters in thefitting problem by removing unrealistic connectivities. Fewer parameters also gives better estimates. In CRM, the radial limitallows the user to fit the producer beyond its pattern to find injector-producer connection. The purpose of this analysis is todecide which value should be used for the radial limit in this field. Different fittings were done on increasing radial limits from1000 ft to 5000 ft for the period from October 1998 to May 2005. The lowest radial limit was taken at 1000 ft because the wellspacing is around 1000 ft. A smaller radial limit includes only very few neighboring injectors in the fitting and removes

possible connectivities from calculation. The calculated liquid and oil production rates with different radial limits are thencompared with the actual rates as in Figures 5a and 5b. The forecast sensitivity is also examined by using parameters obtainedin the fitting windows to calculate liquid and oil production rates from May 2005 to Mar 2009 (Figures 6a and 6b). Increasingthe radial limit improves the fit but increases the computing time as more parameters are fitted. The longest computing time inthis CRM application is nine hours for the whole field. Therefore, 3000 ft is chosen as a compromise between reasonable fitand computation time.

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Reliability

The model reliability is tested by fitting CRM on a selected fitting window and using the obtained parameters to calculate production rates in the following period that contain the remaining data (forecast window) to examine the forecast ability ofthe model. Moving the fitting window along the production history and changing the forecast window accordingly shows ushow the forecast matches the actual data and also allow us to find the best fitting windows. It is noticeable that no matterwhich fitting windows is chosen, the calculated oil production rates in both fitting and forecast windows are smaller than the

actual rates, although both show similar fluctuations. The error is caused by taking outliers out of CRM and FFM fit and not putting the outliers back to calculate the production rate. The outliers not only include data points that are too big or smallcompared to its neighbor data but also new producers or injectors that have too few data points or not fitted well with CRM orFFM. Athough the number of outliers are small compared to the whole field data, removing it from the calculation affectsfuture prediction of the field. Hence, outliers of the field have been added back into the calculation. If a producer is identifiedand removed as outlier, its future production is calculated using exponential decline. Figures 7a and 7b show that adding theoutlier producers improve the fit significantly since about 10 new producers were added in 2008, which had too few data

points to fit by CRM or FFM. It is important to understand the history of the field to identify a period where the assumption ofconstant field performance remains valid. The best ftting window was selected from 2003 to 2009 for the most recent data withthe best fit.

Optimization of future production

From the above analysis, we have more confidence in the CRM and FFM results and are able to identify a radial limit and bestfitting window to fit the past production. Then, future optimization has been done on a portion of the field that consists of 33injectors as a pilot project. This part of the field has been selected as to have little variation in the gain and fewer well events(e.g., open new wells, shut-in, steam injection). The Net present value (NPV) has been optimized over the next five years.Total field injection is kept the same as the recent three month average. A constraint on maximum injection pressure of 1850

psi was also imposed on each injector by assuming a linear relationship between injection rate and pressure. The result fromoptimization shows an incremental production of 50 bbl/day equivalent to $50,000/month as in Figures 8 and 9. Figure 9 alsoshows that that the oil production peak will be reached about 6 months after changing the injection. Afterwards, the oil

production rate will gradually decline

Changing injection rates

The injection rates have been changed according to the recommended rates. Figure 10 shows a comparison of the current and

optimized injection rates in the testing area. Eight injectors have been recommended to be shut-in to divert the water to better performing injectors. Figure 11 shows the location of the injectors with their schedule. To maintain stable operation, the werechanged gradually by 50 bbl/day at a time as a precaution to prevent fracturing of the reservoir because of large suddeninjection increase. The seven recommended shut-in injectors were immediately reduced to the the lowest possible rate of 100

bbl/month. In this field, the rate was measured as daily basis on the whole field and well test was done to allocate injectionrates back to individual injector. This will affect the accuracy of the experiment. The new data are recorded for furtheranalysis.

Pilot study results and discussion

As of August 2010, the rate has been changed for one year; an analysis of the field production is carried out to find out resultsof the pilot study. After August 2010, the experiment was terminated to start CO2 injection. We will compare injection rates,fluid production rates and oil production rates in the period before and after August 2009 to see whether the CRM injectionschedule helps increase production. Allocated rates are used in the analysis.

First, each implemented injection rate was compared to the recommended rate. Recommended injection rates are achievedseveral months after the CRM run was done because the rates had been adjusted gradually. Thus, we should expect a slowerresponse in the production rates. Previously we predicted the incremental oil peak to occur after six months, now we shouldsee the peak increase after six months. Table 1 presents the summary of the implemented injection rates compared to therecommended rates. An injector is considered to follow the recommendation if its rate is not more than 50 bbl/day differentfrom the recommended rate. In general, most of the new injection rates follow the recommended schedule. The total injectionrate of the 33 injectors in the pilot area was calculated from December 2003 to August 2010 to compare injection levels beforeand after August 2009, when we started changing the injection rates. As shown in Figure 12, injection levels are similar beforeand after August 2009. However, in the period from August 2009 to May 2010, we observe a decrease of approximately 500

bbl/day in injection level. The decrease in this period is because of the way we implement the new injection schedule asdescribed in the previous section. The seven recommended shut-in injectors were reduced immediately while other injectionrates were slowly ramped up. Furthermore, an injector were running at 800 bbl/day suddenly had mechanical problem and

went offline from February 20010 to May 2010. The field reaches the desired injection schedule in May 2010.

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Since the field was at a smaller injection level than calculated from August 2009 to May 2010, we expect a lower fluid and oil production rate than predicted. The CRM results show that 66 producers that are connected to the 33 injectors in the pilotstudy. Thus, only the 66 producers’ rates are used in this analysis, and the rest of the field production rates are ignored. Figure13 shows the actual total fluid production rate compared to the calculated rate. Before August 2009, the figure indicates a goodCRM fit since the calculated rate is close to the actual rate. The gap in calculated rate from March 2009 to August 2009indicates the time that the CRM study was carried out. From August 2009, the actual fluid rate is smaller than the calculatedrate as predicted. The total fluid production rates are at the same level before and after August 2009.

We are more interested in the oil production rate. Figure 14 shows the actual total oil production rate versus the calculated rate.The straight line in the figure shows the linear decline of the rates in the period from December 2003 to July 2009. The actualoil production rate was also smaller than the calculated production rate. From Figure 14, we can see that the total oil

production rate increases by45 bbl/day by March 2010. The peak increase occurs after changing the injection rate sevenmonths, slower than predicted and the incremental oil production rate is slightly smaller than the predicted. The cumulativeincremental oil production from August 2009 to August 2010 is 5372 bbls, equivalent to almost $402,900 at $75/bbl. Figure15 shows the oil cut in the optimized area. After the injection change, the oil cut remain almost constant from August 2009 toMarch 2010 although the total injection rate was significantly lower than before because of reducing injection rates accordingto CRM optimized schedule. This fact proves that the seven recommended shut-in injectors did not support the oil productionrate in the optimized area. The failure of the injector from Februrary to May 2010 is also not a very good injector because ofthe fact that the total oil production rate increased during its downtime. The CRM results show that this injector has a weakconnection to other producers and the injector was suggested to maintain its rate. The oil cut in the last two months of the

experiement was lower because two producers were shut-in. The oil cut analysis confirms the results of CRM on lowconnected well pairs.

Conclusion

This study demonstrated the first real-field application of CRM theory. The project has three parts: tuning CRM parameters forthe West Texas field to find optimal injection schedule, changing injection rate according to the schedule and analyzing

production data after implementing the new injection schedule. The results show that CRM application has increased oil production rate 45 bbl/day and produces additional 5372 bbls of oil ($402,900 at $75/bbl) after the first year with no additionalequipment cost. The actual fluid and oil production rates are slightly lower than predicted by the CRM because the actualinjection rate is smaller than the optimized injection rate.

CRM can be used to optimize waterflood in large fields and the application procedure presented in this paper is recommended.Since CRM relies on data to obtain information, good data management practice is important in CRM applications. Thecondition for applying this model is a mature waterflood with larger than 0.5 producing water cut, no aquifer and not manyrecent activities. The field condition should be carefully examined to identify whether it is a good candidate for CRMapplication. The model should be tuned for the specific field before calculating the optimal injection schedule. Several issuesmight arise in large field CRM application: injection scheme might not follow exactly the recommended rates becauseunexpected operational failure, monitoring the injection change and analyzing the effects take time and effort because ofunavailable measured daily rates but are essential to guarantee the effectiveness and evaluation of the method.

Acknowledgments

This work was supported by the sponsors of the Center for Petroleum Asset Risk Management (CPARM) at The Universityof Texas at Austin. Anh P. Nguyen is a Vietnam Education Foundation fellow, Larry W. Lake holds the W. A. (Monty)

Moncrief Centennial Chair in Petroleum Engineering. Thomas F. Edgar holds the George T. and Gladys H. Abell EndowedChair of Engineering at The University of Texas. Leon S. Lasdon holds the David Bruton, Jr. Centennial Chair in BusinessDecision Support Systems, Department of Information, Risk, and Operations Management.

Nomenclature

a j = empirically derived constants for each production wellsb j = empirically derived constants for each production wellsct = total compressibility

CWI j = the total water injected from all injection wells in the reservoir that reach producer j f i = gain or fraction of injector i rate flow towards a producer f oj = oil fraction of producer j

i = injection rate

ir = discount rate per period

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I ik = injection rate of injector I at period knf t = total number of future time periodn p = number of producerni = number of injector

po = price per barrel of oil (value/volume) pw = cost of injecting one barrel of water (value/volume) P wf = producer flowing bottom hole pressure

q = liquid production rateqo jk = oil production rate of producer j at period k

V p= pore volume

Greek character

τ = Time constant measure the time it takes after injection rate change to see a significant effect in producerΔt = time step

Subscript

i = injector j = producerk = time

References

Albertoni, A. and Lake, L. W. 2003. Inferring Connectivity Only From Well-Rate Fluctuations in Waterfloods, SPE Reservoir Evaluationand Engineering Journal, 6, 6-16. SPE-83381-PA. DOI: 10.2118/83381-PA.

Gentil, P. H. 2005. The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients,M.S. Thesis, The University of Texas at Austin.

Izgec, O., Kabir, C. S., 2009, Establishing Injector/Producer Connectivity before Breakthough during Fluid Injection, SPE Western Regional

Meeting, San Jose, California, USA. SPE-121203-MS. DOI: 10.2118/121203-MS

Sayarpour, M, 2008, Development and Application of Capacitance-Resistive Models for Water/CO2 floods, PhD Dissertation, TheUniversity of Texas at Austin.

Weber, D., Edgar, F. T, Lake, L. W., Lasdon, L. S., Sawas K., 2009, Improvements of Capacitance Resistive Modeling and Optimization ofLarge Scale Reservoirs, SPE Western Regional Meeting. SPE-121299-MS. DOI: 10.2118/121299-MS.

Yousef, A.A., Gentil, P.H., Jensen, J.L. and Lake, L.W. 2006. A Capacitance Model to Infer Interwell Connectivity from Production andInjection Rate Fluctuations, SPEREE 9 (5): 630-646. SPE-10.2118. DOI: 10.2118/95322-PA.

Table 1-- Summary of implemented vs. recommended injection rates

Number of injectors adjusted to follow recommendation 18

Number of injectors adjusted higher than recommendation 5

Number of injectors adjusted lower than recommendation 2

Number of injectors recommended to shut-in but has been reduced to minimum level 7

Number of shut-in injectors as recommended 1

Total 33

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Figure 1-- CRM and FFM fitting algorithm

0.85 - 10.75 - 0.85

0.65 - 0.750.55 - 0.650.45 - 0.55

0.35 - 0.450.25 - 0.350.15 - 0.25

0 - 0.15

Producer Injector

Figure 2-- Connectivities from 0.2 to 1 of the fitting window from October 1998 to March 2009

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0.85 - 10.75 - 0.85

0.65 - 0.750.55 - 0.650.45 - 0.55

0.35 - 0.450.25 - 0.350.15 - 0.25

0 - 0.15

Producer Injector

Figure 3-- Connectivites from 0.2 to 1 of the fitting window April 1996 to April 2007

Figure 4-- Connectivity differences between the 2 history matching windows

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20000

25000

30000

35000

40000

45000

50000

55000

Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05

P r o d u c t i o n

r a t e ( b b l / d a y )

Liquid Production

1000 ft

2000 ft

3000 ft

4000 ft

5000 ft

0

500

1000

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3500

Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05

O i l r a t e

( b b l / d a y )

Oil Production

1000 ft

2000 ft

3000 ft

4000 ft

5000 ft

Figure 5a --Liquid production fit with different radial limits Figure 5b-- Oil production fit with different radial limits

0

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50000

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Feb-05 Sep-05 Mar-06 Oct-06 Apr-07 Nov-07 Jun-08 Dec-08 Jul-09

T o t a l P r o d u c t i o n ( b b l / d a y

)

Actual Liquid Production

Liquid Production Forecast 1000 ft





0

500

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2500

Feb-05 Sep-05 Mar-06 Oct-06 Apr-07 Nov-07 Jun-08 Dec-08 Jul -09

O i l R a t e ( b b l / d a y )

Actual Oil Production

Oil Production Forecast 1000 ft





Figure 6a-- Liquid Production forecast with different radial limits Figure 6b--Oil Production forecast with different radial limits

1000

10000

Jun-2003 Mar-2006 Dec-2008 Sep-2011 Jun-2014

O i l P r o d u c t i o n

( b b l / d a y )

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Calc Oil Production fit 05-07



Calc Oil Prod fit 03-06


Injection/15

1000

10000

Jun-2003 Mar-2006 Dec-2008 Sep-2011 Jun-2014

O i l P r o d u c t i o n

( b b l / d a y )

Oil Production




Calc Oil Prod fit 03-06

Injection/15

Figure 7a & 7b--Oil production fit and forecast with different fitting windows

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1000

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Mar-2003 Feb-2005 Jan-2007 Dec-2008 Nov-2010 Oct-2012 Sep-2014

O i l P r o d u c t i o n R a t e ( b b l / d a y )

Oil Production

Fitted and forecast oil productionOptimized Oil Production

49 bbl/day

Figure 8-- Optimized vs. forecast oil production rate

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$ 1 0 0 0 / m o n t h

b b l / d a y

Oil production increment

Profit increment

Figure 9-- Monthly incremental production and profit from CRM optimization

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0

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I n j e c t i o n r a t e ( b b l / d a y )

Past Injection

Future Injection

Figure 10-- Optimized vs. current injection schedule

Figure 11-- Injector location and recommended schedule

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Figure 12-- Actual total injection rate of the optimized area vs. optimized injection level

Figure 13-- Actual vs. calculated total fluid production rate

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Figure 14-- Actual vs. calculated total oil production rate

Figure 15 – Oil cut in the optimized area

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