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SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2002 13

COUPLING THE RESERVOIRSIMULATOR POWERS WITH THESURFACE FACILITIES’ NETWORKSIMULATOR PIPESOFT

Al-Shaalan graduated from the University of Wisconsin-Madison in 1997 with a PhD in mechanical engineering.He is a developer working on Saudi Aramco’s massivelyparallel simulator POWERS in Saudi Aramco’sTechnology Development Division. He worked with LabResearch and Development for two years and has pub-lished five technical papers on combustion sciencetechnology with the National Association of CorrosionEngineers (NACE) and the Society of PetroleumEngineers (SPE).

Dogru is general supervisor of Saudi Aramco’sTechnology Development Division, in which he supervises 20 engineers and scientists involved with reser-voir and production technologies. His division developsand implements new technologies for reservoir and pro-duction engineering with an emphasis on multiphase flowmeters, downhole separation and downhole monitoring.Dogru has an MSc from the Technical University ofIstanbul and a PhD from the University of Texas inAustin (UT). Prior to joining Saudi Aramco, he workedfor Mobil Research & Development Company and CoreLabs Inc., both in Dallas. He held academic and teachingpositions at UT in mechanical engineering, at theCalifornia Institute of Technology in chemical engineer-ing and at the Norwegian Institute of Technology inpetroleum engineering. He has 25 years of experience inthe oil and gas industry and has published 25 technicalpapers for the SPE and other scientific journals.

Tareq M. Al-Shaalan,Ali H. Dogru andLarry S. Fung

14 SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2002

Fung supervises Reservoir Technology in SaudiAramco’s Technology Development Division. He is a leaddeveloper for the massively parallel simulator POWERS.Prior to joining Saudi Aramco he was with EpicConsulting Services Ltd. Prior to that he spent 11 yearsat the Computer Modeling Group, where he developedfeatures for its commercial simulators. He has publishedmore than 20 technical papers on reservoir simulationtechniques such as adaptive implicit method, dual porosi-ty dual/permeability modeling and control volume finiteelement method, as well as coupled petroleum geome-chanical modeling. Fung holds BS and MS degrees fromthe University of Alberta in Canada. He has 20 years ofexperience in the oil and gas industry.

ABSTRACT

Two simulators play an important role in navigating SaudiAramco’s reservoirs: the Parallel Oil, Water and GasSimulator (POWERS) is an in-house black-oil simulator;PIPESOFT is a commercial product that simulates steadystate three-phase flows of oil, water and gas in pipes andpipe networks. These simulators are coupled at the sandface via in-house software. This procedure eliminates theneed to generate hydraulic flow tables. With this coupling,fluid flow is modeled from the reservoir to the gas-oil sep-aration plant (GOSP). As a result, the uncertainty ofwellhead pressure required by the flow-table approach isno longer needed. As the coupled simulation proceeds,PIPESOFT receives continuous data for reservoir pressure,productivity index, water cut and gas-oil ratio from POW-ERS. This process mimics the field process better andresults in more realistic rate predictions than the conven-tional flow-table approach. Since it provides a morerigorous solution, the predicted rates can be substantiallydifferent than those of the flow-table method. The cou-pled simulator was tested on two offshore oil fields. Thefirst example comprised 20 years of prediction involving60 wells. For comparison purposes, simulation runs werecarried out with and without coupling. The results of thecoupled model showed that the use of flow tables withconstant wellhead pressure could over-predict or under-predict actual well performance. Decouplingunder-predicted the field target oil rates by 20 percent.The second offshore field, with a complicated surface net-work (250 wells), was tested for 40 years of prediction.The coupled simulation was stable and added only 10-13percent overhead to the total computer time.

INTRODUCTION

Super computers and large data storage systems make the mod-eling and simulation of complex problems more viable. Thelatest technology in parallel computers allows the simulation ofhydrocarbon movement in giant reservoirs coupled with thesurface network to be accomplished in a practical time frame.

Reservoir simulation can be done accurately without the sur-face network if the well flow rates are predicted correctly.However, it is very difficult to predict the correct well flow rateswithout knowledge of the capacity of the surface network.

The wells constitute the inner boundaries of the reser-voir. They are connected together on the surface through anetwork of pipes (fig.1). The deliverability of the wells andthe reservoir depends on the capacity of the surface net-work, in addition to reservoir pressure and reservoirproductivity, or injectivity. The flow rates of the wells maychange with time. Due to the nature of the dynamic flow inthe surface network, any change in the flow rate of one ofthe wells will affect the flow rates of other wells. Shuttingdown one of the wells could increase the deliverability ofthe other wells in the same surface network. Increasing theflow rate of one of the wells could cause backflow in theother wells.

Well

Interior node

GOSP

Linkage

Fig. 1. An example of a surface network.

To improve the accuracy of the well flow rates, the reser-voir simulator needs to be coupled with the surface networksimulator. Therefore, the fluid movement can be modeled allthe way from the reservoir to the plant (e.g., injection plant orGOSP). The coupling provides an efficient tool for betterfuture planning and development opportunities for both thereservoir and the surface network.

Coupling reservoir simulators with surface network sim-ulators goes back to Dempsey J. R. (1971). Moredevelopment and research on the coupling, or full-fieldmodel, have been done since then. Wallace and VanSpronsen (1983) and Stoisits, R. F. et al. (1992) implement-ed the full-field model in their studies of the simulation ofthe reservoir coupled with the surface network. All thestudies agree that the coupling gives a more realistic resultfor future development and optimization.

There are many ways to couple a reservoir simulatorwith a surface network simulator (Trick, M.D. (1998) andBreaux, E.J. et al. (1985)). The coupling can be done at thefollowing locations:

• Wellhead with inflow performance relationship; • Reservoir level with inflow performance relationship; • Sand face; and• Trap level with inflow performance relationship.Trick and Breaux et al., discuss these methods in detail.

Hepguler et al. (1997) integrated the famous reservoirsimulator Eclipse with the surface network simulatorNetopt. The data shared between the two simulators istransferred through a parallel virtual machine (PVM).Trick coupled Eclipse with another surface network simu-lator, FORGAS. PVM is employed to transfer the databetween the two simulators.

This paper discusses the integration of the reservoir sim-ulator POWERS, (Dogru, A.H., et al. 1999) with thesurface network simulator PIPESOFT.

RESERVOIR SIMULATOR: POWERS

POWERS is Saudi Aramco’s in-house black-oil simulator. Itutilizes the latest technology in parallel computing to simulatea multi-million cell model in a reasonable amount of time.The governing equations used to simulate the reservoir areintegrated in time using either implicit or IMPES methods.

POWERS well models apply many constraints on bottomhole pressure that will be used to flow the wells. The bot-tom hole pressures executed by POWERS are:

• Bottom hole pressure corresponding to maximum wellflow rate (BHPQ);

• Bottom hole pressure corresponding to bubble pointpressure for producer (BHPbp);

• Bottom hole pressure corresponding to fracture pres-sure for injector (BHPfr);

• Bottom hole pressure corresponding to maximumdraw-down pressure for producer (BHPDDP);

• Bottom hole pressure corresponding to maximumbuild-up pressure for injector (BHPBUP); and

• Bottom hole pressure corresponding to minimum wellhead pressure (BHPWHP).

For producer wells, the operating bottom hole pressure(BHPprod) that POWERS utilizes in its calculations is:

(1) BHPprod = max (BHPQ, BHPmin, BHPDDP, BHPWHP)For injector wells, the operating bottom hole pressure

(BHPinj) is:(2) BHPinj = min (BHPQ, BHPmax, BHPBUP, BHPWHP)The operating bottom hole pressures calculated from

equations 1 and 2 will be manipulated further to producethe target flow rate for a given group of wells.

Like other reservoir simulators, POWERS knows thecapacity of the surface network through the wellhead pres-sure (WHP). The bottom hole pressure BHPWHP iscalculated from a given WHP and flow table generatedfrom a simulator for the surface network. A constant WHPis assumed during the reservoir simulation.

However, in real time, WHP pressure is dynamic. Thetime characteristic of the pipe flow is within a minute. Thetime characteristic of the reservoir flow is within a day. Sincethe time characteristic of the pipe flow is very small, anysmall change in the flow rate of one of the wells will be feltinstantaneously by the other wells connected to the same sur-face network. As a result, WHP at one well is affected by theflow rates of others wells in the same network.

It assumes constant WHP is reasonable for high produc-tive, or injective wells. For low productive, or injectivewells, the assumption of a constant WHP pressure may fail.

Another drawback of using wellhead pressure is the errorof interpolation associated with the flow tables. Flow tablesare generated for different flow rates, well pressures (P),water cuts (WC), gas-oil ratios (GOR) and WHPs. The inter-polation is typically employed to determine the well flow ratefor a given P, WHP, WC and GOR. The accuracy of theinterpolation depends on the method used in the interpola-tions. The error of interpolation is generally high formultiphase flows (Schiozer, D.J. and Aziz, K. 1994).

To reduce the uncertainties in calculating WHP and toimprove the prediction of the reservoir simulation, POW-ERS needs to be linked with another simulator for thesurface network. The coupling eliminates the need for theflow table and WHP. The well flow rate is calculateddirectly from the surface network simulator.

SURFACE NETWORK SIMULATOR: PIPESOFT

On the surface, the wells are connected together through anetwork of pipes (fig. 1). The surface network consists of


nodes and linkages. It could be a gathering network, a dis-tributive network or a combination of both. A linkageconsists of pipes, tubes, traps, valves, pumps, compres-sors, separators, chock valves and other mechanicaldevices. The nodes are either boundary nodes or interiornodes. The boundary nodes are wells, sources and sinks.The interior nodes are gathering points for the linkages.The water injection plant is an example of a source node.The GOSP is an example of a sink. Detailed informationon the nodes and linkages can be found in any manual ofa surface network simulator.

PIPESOFT is a commercial software that simulatessteady-state three-phase flow in the surface network. Thegoverning equations required to solve for the surface net-work are complex and nonlinear. Therefore, PIPESOFTuses the Newton-Raphson method to solve the equationsiteratively. If the flow is multiphase and the surface networkis complex, the solution may not converge. Low well pres-sure may also cause the solution not to converge.

The simulation of a surface network requires either pres-sure or flow rate to be specified at the boundary node. Forthe case of multiphase flow simulation, WC and GOR arespecified at the source, or well node. The productivity orinjectivity index (PI) has to be given when the boundarynode is a well. Once the boundary values are given,PIPESOFT calculates, for example, temperature, pressuredrops and the flow rate in each branch of the network. Theoutput of PIPESOFT is valuable for optimizing the surfacenetwork. When POWERS is coupled with PIPESOFT, theflow rates or the pressures at the wells are valuable forreservoir simulation.

The values P, WC, GOR and PI for each well are chang-ing with time. Most surface network simulators model thereservoir as a big tank to mimic P as a function of time.However, for a heterogeneous reservoir, or tight reservoir,this assumption results in a high error. The big tank model,also, is not able to reproduce WC, GOR and PI as a func-tion of time. To accurately model the surface network andget the time data for P, WC, GOR and PI, PIPESOFT hasto be linked with a reservoir simulator.

COUPLING POWERS WITH PIPESOFT

A full-field model is essential to optimize production of areservoir and to design for the optimum surface network. Areservoir simulator coupled with a surface network simula-tor gives a more rigorous solution than when both run asstand-alones. There has been work already done to link thereservoir simulator Eclipse with the surface network simula-tors, FLOGAS (Trick, M.D., 1998) and Netopt (Hepguler,G. et al. 1997). In this paper, POWERS and PIPESOFT arelinked together.

PIPESOFT runs on IBM and POWERS runs on CM5,IBM or SGI. For these simulators to be linked together andwork as one, an interface (POPSI) is developed to transferdata between the two simulators. As mentioned in theintroduction, there are many ways to couple a reservoirsimulator with a surface network simulator. To eliminatethe need for the flow table, both POWERS and PIPESOFTare coupled at the sand phase (Trick, M.D., 1998;Hepguler, G. et al. 1997).

The algorithm of the interface POPSI is given in fig. 2.At the beginning of time integration, POWERS calls POPSIto send P, WC, GOR and PI for each well to PIPESOFT.To minimize the overhead of data transfer, POPSI puts allthe data in one buffer and sends it to the machine wherePIPESOFT runs. The data is transferred through a Unixsocket. Then POPSI extracts the data for PIPESOFT andwaits for PIPESOFT to finish its calculations. When the cal-culations have converged, POPSI extracts the well flowrates and sends them to POWERS. In case the solution doesnot converge, POPSI uses the old well flow rates and sendsthem to POWERS as a temporary solution for the currenttime step.

The good initial estimations for the unknown pressuresand flow rates in the surface network are necessary for thesolution of PIPESOFT to converge. POPSI employs the fol-lowing estimates to help the solution converge.

• The nodal flow rates of the previous time step are usedas initial estimates.

• The nodal pressures, other than well pressures, of theprevious time step are utilized as initial estimates.

• The wells that are closed at the previous time step arekept closed all the time.

When POWERS receives the well flow rates fromPIPESOFT (Qpipesoft), it calculates the bottom hole pres-sure, BHPpipesoft from Qpipesoft.

(3) BHPpipesoft = P - Qpipesoft / PIFor producer wells, the operating bottom hole pressure

(BHPprod) is calculated from (4) BHPprod = max (BHPQ, BHPmin, BHPDDP, BHPpipesoft)For injector wells, the operating bottom hole pressure

(BHPinj) is calculated from(5) BHPinj = min (BHPQ, BHPmax, BHPBUP, BHPpipesoft)Note that when POWERS is coupled with the surface

network, there is no need for BHPWHP (compare equations1, 2, 4 and 5). The pressures BHPprod and BHPinj calculat-ed from equations 4 and 5 will be manipulated further tomeet the target flow rate for a given group of wells.

Example Application

The coupled simulators were tested on two Saudi Aramcooffshore oil fields, A and B. Field A is modeled with the


grid points 84, 128 and 128 in the x, y and z directions,respectively. Two-phase flow (oil and water) was assumedin the reservoir (the reservoir is kept above its bubble pointpressure). Even though the reservoir pressure is maintainedabove the bubble point pressure during the reservoir simu-lation, the simulation of the surface network considers theevolution of the gas. The history match for field A coversthe period from 1970 to 1999. On the other hand, the fieldprediction period covers 1999 to the year 2020. The cou-pling of the two simulators started at the beginning of theprediction period. There were 60 producing wells coupledwith the surface network. The injector wells were not partof the surface network.

Field A was simulated with a bubble point pressure of

1,330 psi and a maximum injection pressure of 7,500 psi.The field oil target rate was set to 120 MBPD from 1999 to2000. Then it was raised to 140 MBPD until 2001. Afterthat it was reduced back to 120 MBPD to 2020. Therewere no drilling or workovers allowed to keep the target oilrate. When there was over production in the oil rates, thewells with WC less than 10 percent were scaled down. Anyproducer well with an oil rate of less than 250 BPD wasshut in.

Field B was modeled with the grid points of 70, 168 and8 in the x, y and z directions, respectively. Two-phase flow(oil and water) was assumed in the reservoir. The simula-tion of the surface network considers the evolution of thegas. The history period covered 1950 to 1998. The predic-tion period was from 1998 to 2040. During the predictionperiod, the field was operated to its potential rate to testthe stability of the coupling. The two simulators were cou-pled at the beginning of the prediction period. There were250 producing wells coupled with the surface network. Theinjectors were not part of the surface network.

Three cases were considered: Case A1 — field A withoutcoupling; case A2 — field A with coupling; and case B1 —field B with coupling. For case A1 (uncoupled case), thereservoir simulation was accomplished using flow tablesand wellhead pressures of 300 psi for producer wells.

RESULTS AND DISCUSSION

The effect of coupling on the field prediction is demonstrat-ed through case A1 and case A2. The field oil rates for caseA1 and case A2 are plotted in fig. 3. The oil target rate forcase A1 can be maintained until 2003. Then it starts todecline due to low reservoir pressure. However, with thecoupling (case A2), the oil target rate can be sustained foran additional four years before it starts to decline. Thenumber of active wells for both cases is plotted in fig. 4.The number of active wells for the two cases agrees verywell until year 2003, when case A1 predicts a decline inreservoir deliverability. In 2003, the flow table of case A1starts to shut in some of the producing wells due to back-flow resulting from high WC and low reservoir pressure.On the other hand, due to surface network calculations, thesurface network has enough capacity to flow these closedwells for four more years.

The use of flow tables and wellhead pressures of 300 psiunderestimates the oil potential rate for field A (fig. 5). Thedecline in field potential is due to a loss in the reservoirpressure and a high WC. The accurate field potential can-not be accomplished without the coupling. The fieldpotential is influenced by the capacity of the surface net-work in addition to the reservoir pressure. The larger thesurface network the higher the field potential. Installing


Start of time

POPSI extracts the data for PIPESOFT

POPSI runs PIPESOFT

Calculations

converge

POPSI

restores

old flow

rates

POWERS calls POPSI to send pressure, productivity

index, WC and GOR to PIPESOFT

POPSI extracts well flow rates

and sends them to POWERS

POWERS compares the flow rates

from PIPESOFT with other

constraints to choose the

optimum well flow rates

POWERS does the time

integrations

YES

NO

Fig. 2. The algorithm (POPSI) for the interface that couples POWERS withPIPESOFT.

pumps or compressors in the surface network increases thefield potential. Chocked valves in the surface networkreduce the reservoir potential. If the surface network goesthrough a high elevation, some of the wells cannot flow.

Five wells are presented in this paper to demonstrate theeffects of the coupling on well performance. These wells aregiven the names W1, W2, W3, W4 and W5. The rest of thewells have more or less the same behavior.

For case A1, the flow table and the wellhead pressure of300 psi are used to estimate the capacity of the surface net-work. However, coupling (case A2) proves that theassumption of the wellhead pressure of 300 psi underesti-mates the capacity of the well W1 (fig. 6). From thecalculations of the surface network, the wellhead pressure isvery close to 300 psi at the start of the prediction period.Then it starts to decline to 200 psi at the end of the simula-tion. Therefore, the assumption of a wellhead pressure of300 psi is valid only at the beginning of the prediction peri-

od. The wellhead pressures for all producing wells have thesame behavior as W1.

The use of the flow table (case A1) may under-predict orover-predict well performance. For W2, case A1 underesti-mates the oil rates by 600 BPD at 2000 (fig.7). At the endof the simulation, both case A1 and case A2 predict rough-ly the same oil rate. On the other hand, case A1overestimates the oil rate for W3 by 2,000 BPD at 2000(fig. 8). Case A1 and case A2 predict roughly the same oilrates for W4 through all the periods of the simulation. Thedifference in the oil rate of W4 between the two cases is lessthan 50 BPD (fig. 9). Case A1 shuts in the well W5 (fig. 10)due to high water cut. The water cut for W5 is 92 percent.However, the surface network calculations show that thesurface network still has enough capacity for W5 to flow tothe network.

Fig.7 and fig. 8 show that when the prediction starts inyear 1999, case A1 predicts a sudden change in the oil rates


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Fig. 3. The field oil rate for case A1 and case A2 Fig. 5. The field oil potential rate for case A1 and case A2

Fig. 4. The number of producers’ wells that are flowing for case A1 and case A2 Fig. 6. Wellhead pressure for well W1 and case A2

for W2 and W3. It reduces the oil rate for W2 sharply to200 BPD and it increases the oil rate for W3 sharply to5,800 BPD. Case A2, however, makes a smooth transitionfrom the history simulation to the prediction simulation.

PIPESOFT utilizes the iterative Newton-Raphson methodto solve for the non-linear equations of the surface net-work; due to the high non-linearity of the equations, thesolution may require lots of iterations. Fig. 11 plots thenumber of iterations for the surface network for case A2.Initially, the number of iterations is very high, 52 iterations,because the initial estimates for the unknowns are far fromthe solution. The closer the initial estimates are to the solu-tion, the faster the solution will converge. For the reservoirsimulation, the well rate and pressure at the previous timestep are close to the solution of the current time step. Usingthe solution at the previous time step as the initial estimatedecreases the number of iterations greatly from 52 itera-tions per time step to five iterations per time step.

Sometimes the number of iterations gets as low as two iter-ations per time step. The average number of iterations pertime step is 6.7.

One reason a reservoir engineer may prefer to use theflow table over the coupled model is the increase in CPUtime for the coupled model. But it is proven in this studythat this is not always the case. In this study, POWERS runson CM5 and PIPESOFT runs on IBM or SP2. The totaltime of simulation for case A1 and case A2 is 21.6 minutesand 23.7 minutes, respectively. The calculations ofPIPESOFT add only 10 percent extra time to the total timeof the reservoir simulation. This 10 percent is very accept-able considering the accuracy resulting from the coupling.

The stability of the coupling is also tested on case B1where field B flows to its oil potential rate and it has 240wells coupled with the surface network. For this case,PIPESOFT adds 13 percent extra time to the total time ofthe reservoir simulation. The calculations of PIPESOFT are


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Fig. 7. Oil rate of W2 for case A1 and case A2 Fig. 9. Oil rate of W4 for case A1 and case A2

Fig. 8. Oil rate of W3 for case A1 and case A2 Fig. 10. Oil rate of W5 for case A1 and case A2. Note that the well is closedfor case A1.

stable all the time. Fig. 12 plots the number of iterationsfor the surface network to solve. The highest number ofiterations per time step is 300 and it happens at the begin-ning of the prediction period. The lowest number ofiterations per time step is four. The average number of iter-ations per time step is 11.

CONCLUSIONS

The parallel reservoir simulator POWERS was coupled withPIPESOFT to improve the accuracy of reservoir deliverabili-ty predictions. The coupling removed the uncertainty in thewellhead pressures for the reservoir simulator and the reser-voir pressures for PIPESOFT. With this coupling, the fluidflow was modeled all the way from the reservoir to theGOSP. The coupled simulator was demonstrated in two oilfields for prediction times of 20 years and 40 years. Theresults showed that the use of constant wellhead pressuresand the flow tables could over-predict or under-predict thewell deliverability. The numerical experiments indicatedthat coupling was computationally stable and added only10 to 13 percent overhead to the reservoir simulation time.

NOMENCLATURE

BHP Bottom Hole PressureGOR Gas Oil RatioGOSP Gas Oil Separation PlantP Well PressurePI Productivity or Injectivity IndexQ Well Flow RateWC Water CutWHP Wellhead Pressure

Subscript

bp Bubble point pressureBUP Build-up pressureDDP Draw-down pressurefr Fracture pressureinj Injectorl Liquido Oilprod Producers

ACKNOWLEDGMENTS

The authors acknowledge Rick Pawlas for providing thefield data and K.M. Rafique for the surface network data.

REFERENCES

Breaux, E.J. et al. 1985. “Application of a ReservoirSimulator Interfaced with a Surface Facilities Network: ACase History,” SPE 11479, Society of PetroleumEngineering Journal, June.

Breaux, E.J. et al. 2000. “Linking Reservoir and SurfaceSimulators: How to Improve the Coupled Solutions,”SPE 65159 presented at SPE European PetroleumConference, Praise, France, Oct. 24-25.

Dempsey, J.R. et al. 1971/ “An Efficient Method forEvaluating Gas Field Gathering System Design,” JPT,P.1067-1073, September.

Dogru, A.H., et al. 1999. “A Massively Parallel ReservoirSimulator for Large Scale Reservoir Simulation,” SPEpaper 51886 presented at the 1999 SPE ReservoirSimulation Symposium, Houston, Texas, Feb. 14-17.


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Fig. 11. The number of iterations of PIPESOFT for case A2 Fig. 12. The number of iterations of PIPESOFT for case B1

Haugen, E.D. et al. 1995 “Simulation of IndependentReservoir Coupled by Global Production and InjectionConstrains,” SPE 29106 presented at 13th SPESymposium on Reservoir Simulation, San Antonio, Texas,Feb. 12-15.

Hepguler, G. et al. 1997. “Integration of a Field Surfaceand Production Network with a Reservoir Simulator,”SPE 38937, SPE Computer Applications, p.88-92, June.

Khodri, D. et al. 1997. “Modeling of Reservoir to Surfacenetwork Coupling: A Case Study of Gas StorageManagement,” SPE 38864 presented at the 1997 SPEAnnual Technical Conference and Exhibition, SanAntonio, Texas, Oct. 5-6.

Schiozer, D.J., and Aziz, K. 1994. “Use of DomainDecomposition for Simultaneous Simulation of Reservoirand Surface Facilities,” SPE paper 27876 presented atWestern Regional Meeting, Long Beach, Calif., March23-25.

Stoisits, R.F. et al. 1992. “Application of NonlinearAdaptive Modeling for Rigorous Representation ofProduction Facilities in reservoir Simulation,” SPE 24898paper presented at the 67th Annual Technical Conferenceand Exhibition of SPE, Washington, D.C., UAS, Oct. 4-7.

Trick, M.D. 1998. “A Different Approach to Coupling areservoir Simulation with a Surface Facilities Model,”SPE 40001 paper presented at 1998 SPE Gas TechnologySymposium, Calgary, Alberta, Canada, March 15-18.

Wallace, D.J., and Van Spronsen, E. 1983. “A ReservoirSimulation Model with Platform Production/InjectionConstraints For Development Planning and ReservoirManagement at the Kuparuk Revier Field,” SPE paper20045 presented at Reservoir Simulation Symposium, SanFrancisco, Calif., Nov. 15-18.


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