16

Simulation Throughoutthe Life of a Reservoir

Gordon AdamsonReservoir Management Ltd.Aberdeen, Scotland

Martin CrickTexaco Ltd.London, England

Brian GaneBritish PetroleumAberdeen, Scotland

Omer GurpinarDenver, Colorado, USA

Jim HardimanHenley on Thames, England

Dave PontingAbingdon, England

For help in preparation of this article, thanArcher, Chip Corbett, Ivor Ellul, Roger GooHonefenger, GeoQuest, Houston, Texas, UArchibald, GeoQuest Reservoir TechnologThames, England; Ian Beck, GeoQuest Renologies, Abingdon, England; George BesPanCanadian Petroleum Limited, Calgary, Canada; Kunal Dutta-Roy, Simulation ScieBrea, California, USA; and Sharon Wells, Reservoir Technologies, Denver, ColoradoECLIPSE, FloGrid, GRID, Open-ECLIPSE, PRTView are marks of Schlumberger. NETOPIPEPHASE are marks of Simulation Scien1. Peaceman DW: “A Personal Retrospect

voir Simulation,” Proceedings of the FirInternational Forum on Reservoir SimulAustria, September 12-16, 1988 and Se1989.

2. Wycoff RD, Botset HG and Muskat M: ics of Porous Flow Applied to Water-flolems,” Transactions of the AIME 103 (19Muskat M and Wyckoff RD: “An Approof Water-Coning in Oil Production,” Trathe AIME 114 (1935): 144-163.

3. Darcy’s law states that fluid flow velocitional to pressure gradient and permeabinversely proportional to viscosity.

4. Coats KH: “Use and Misuse of ReservoModels,” SPE Reprint Series No. 11 Nulation. Dallas, Texas, USA: Society of Peneers (1973): 183-190.

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data preparation and result-analysis pack-ages. Today, desktop computers may have5000 times the memory and run about 200times faster than early supercomputers.However, the most significant gain has notbeen in absolute speed, but speed at a mod-erate price. Computational efficiency hasreached a stage that allows powerful simula-tors to be run frequently.

Numerical simulation has become a reser-voir management tool for all stages in the lifeof the reservoir. No longer just for comparingperformance of reservoirs under differentproduction schemes or trouble-shootingwhen recovery methods come underscrutiny, simulations are also run when plan-ning field development or designing mea-surement campaigns. In the last 10 years,with the development of computer-aidedgeological and geostatistical modeling, reser-voir simulators now help to test the validityof the reservoir models themselves. And sim-ulation results are increasingly used to guidedecisions on investing in the construction oroverhaul of expensive surface facilities.

Motivation for SimulationA numerical simulator containing the rightinformation and in the hands of a skilledengineer can imitate the behavior of a reser-voir. A simulator can predict productionunder current operating conditions, or the

erful tools for guiding reservoir

nning early production wells and

gnosing problems with enhanced

mulators allow engineers to

ore efficiently than ever before.

ks to Bobdan and JimSA; Randyies, Henley onservoir Tech-serer, Alberta,nces Inc.,GeoQuest.VT andPT and

ces Inc.ion of Reser-st and Secondation, Alpbach,ptember 4-8,

“The Mechan-oding Prob-33): 219-249.

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Reservoir simulators were first built as dinostic tools for understanding reservoirs tsurprised engineers or misbehaved afyears of production. The earliest simulatwere physical models, such as sandboxwith clear glass sides for viewing fluid floand analog devices that modeled fluid flwith electrical current flow.1 These modefirst documented in the 1930s, were costructed by researchers hoping to undstand water coning and breakthroughhomogeneous reservoirs that were underging waterflood.2

Some things haven’t changed since t1930s. Today’s reservoir simulators generasolve the same equations studied 60 yeago—material balance and Darcy’s lawBut other aspects of simulation hachanged dramatically. With the adventdigital computers in the 1960s, reservmodeling advanced from tanks filled wsand or electrolytes to numerical simulatoIn numerical simulators, the reservoir is reresented by a series of interconnectblocks, and the flow between blockssolved numerically. In the early days, coputers were small and had little memolimiting the number of blocks that could used. This required simplification of treservoir model and allowed simulationproceed with a relatively small amountinput data.

Simulation is one of the most pow

management decisions. From pla

designing surface facilities to dia

recovery techniques, reservoir si

predict and visualize fluid flow m

Oilfield Review

ility, and

ir Simulationmerical Simu-troleum Engi-

As computer power increased, engineerscreated bigger, more geologically realisticmodels requiring much greater data input.This demand has been met by the creationof increasingly complex and efficient simu-lation programs coupled with user-friendly

reaction of the reservoir to changes in con-ditions, such as increasing production rate;production from more or different wells;response to injection of water, steam, acid

17Summer 1996

Up-

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Core plugs Whole cores

Borehole geophysics

Well logs Well testing

Outcrop studies 3D Seismic data

Geological expertiseLarge-scale structure

Small-scale structure

Simulation model Static reservoir model

Execution model

1st generation geomodel

■■Creating models for input to reservoir simulators. The first-generation geomodel is cre-ated through the combined efforts of geologists, geophysicists, petrophysicists andreservoir engineers. Reservoir properties are then upscaled to produce the static reser-voir model. Optimizing the grid and calibrating with dynamic data yield the simulationmodel. Finally, input from surface facilities analysis and risk calculations results in anexecution model that can guide reservoir management decisions.

or foam; the effect of subsidence; and pro-duction from horizontal wells of differentlengths and orientations.

Reservoir simulation can be performed byoil company reservoir engineers or by engi-neering consultant contractors. Some con-tractors specialize in engineering consulting,while others offer a full range of oilfield ser-vices. In either case, the simulator is a toolthat allows the engineer to answer questionsand offer recommendations for improvingoperating practice.

To make simulation worthwhile, there mustbe a well-posed question of economicimportance: Where should wells be locatedto maximize incremental recovery per dollarof additional investment? How many wellsare required to produce enough gas to meeta contractual deliverability schedule? Shouldoil be recovered by natural depletion orwater injection? What is the optimum lengthof a horizontal well? Is carbon dioxide [CO2]injection feasible? Should we keep this reser-voir alive? As observed by K.H. Coats whileat the University of Texas at Austin, USA,“The complexity of the questions beingasked, and the amount and reliability of thedata available, must determine the sophisti-cation of the system to be used.”4 In allcases, a simulation study should result inrecommendations for intervention. This mayinclude a new strategy for data acquisition,or an infill drilling plan with the number,location and direction of wells and a com-pletion strategy for each well.

How a Simulator WorksThe function of reservoir simulation is tohelp engineers understand the production-pressure behavior of a reservoir and conse-quently predict production rates as a func-tion of time. The future productionschedule, when expressed in terms of rev-enues and compared with costs and invest-ments, helps managers determine both eco-nomically recoverable reserves and the limitof profitable production.

Once the goal of simulation is determined,the next step is to describe the reservoir interms of the volume of oil or gas in place,the amount that is recoverable and the rateat which it will be recovered. To estimaterecoverable reserves, a model of the reser-voir framework, including faults and layersand their associated properties, must beconstructed. This so-called static model iscreated through the combined efforts ofgeologists, geophysicists, petrophysicists andreservoir engineers (left). Much of the multi-billion-dollar business of oilfield services iscentered on obtaining information that

eventually feeds reservoir simulators, lead-ing to better reservoir development andmanagement decisions.5

The simulator itself computes fluid flowthroughout the reservoir. The principlesunderlying simulation are simple. First, thefundamental fluid-flow equations areexpressed in partial differential form foreach fluid phase present. These partial dif-ferential equations are obtained from theconventional equations describing reservoirfluid behavior, such as the continuity equa-tion, the equation of flow and the equationof state. The continuity equation expressesthe conservation of mass. For most reser-voirs, the equation of flow is Darcy’s law.For high rates of flow, such as in gas reser-voirs, Darcy’s law equations are modified toinclude turbulence terms. The equation ofstate describes the pressure-volume or pres-sure-density relationship of the various flu-ids present. For each phase, the three equa-tions are then combined into a single partialdifferential equation. Next, these partial dif-ferential equations are written in finite-dif-ference form, in which the reservoir volumeis treated as a numbered collection ofblocks and the reservoir production periodis divided into a number of time steps.Mathematically speaking, the problem isdiscretized in both space and time.

Examples of simulators that solve thisproblem under a variety of conditions arefound in the ECLIPSE family of simulators.These simulators fall into two main cate-gories. In the first category are three-phaseblack-oil simulators, for reservoirs compris-ing water, gas and oil. The gas may moveinto or out of solution with the oil. The sec-ond category contains compositional andthermal simulators, for reservoirs requiringmore detailed description of fluid composi-tion. A compositional description couldencompass the amounts and properties ofhexanes, pentanes, butanes, benzenes,asphaltenes and other hydrocarbon compo-nents, and might be used when the fluidcomposition changes during the life of thereservoir. A thermal simulator would beadvised if changes in temperature—eitherwith location or with time—modified thefluid composition of the reservoir. Such adescription could come into play in the caseof steam injection, or water injection into adeep, hot reservoir.

18 Oilfield Review

Local Grid Refinement

Block-Centered Geometry

Corner-Point Geometry

6200

5800

6600

7000

7400

6200

5800

6600

7000

7400

0 2000 4000 6000 8000

0 2000 4000 6000 8000

■■Block-centeredand corner-pointgeometries. Block-centered geometryfeatures flat-topped rectangularblocks that matchthe mathematicalmodels behind thesimulator. Corner-point geometrymodifies the recti-linear grid so thatit conforms toimportant reservoirboundaries. Three-dimensional gridsare constructedfrom a 2D grid bylaying it on the topsurface of thereservoir and pro-jecting the gridvertically or alongfault planes ontolower layers.

■■Local grid refine-ment (LGR). Localgrid refinementallows engineers todescribe selectedregions of the reser-voir in extra detail.Radial refined gridsare often usedaround wellbores toexamine coning orother phenomenaresulting from rapidvariation in proper-ties away from thewell. Refined gridsare also one way totreat property varia-tions near faults.

grids around wells in a larger Cartesiangrid.6 Locally refined grids also captureextra detail in other areas where reservoirproperties vary rapidly with distance, suchas near faults. And LGR, combined with gridcoarsening outside the region of interest,allows engineers to retain fine-scale prop-erty variation without surpassing computerspace limitations. The interactive GRID pro-gram was designed to help construct thecomplex reservoir grid efficiently (see“Developments in Gridding,” page 21).

Once the grid has been constructed, thenext step is to assign rock and fluid proper-ties from the reservoir framework model toeach grid block. Populating the grid withproperties is another time-consuming anddifficult task. Each grid block, typically afew hundred square meters areally by tensof meters thick, has to be assigned a singlevalue for each of the reservoir properties,including fluid viscosity, relative permeabil-ity, saturation, pressure, permeability, poros-ity and net-to-gross ratio.7 Log measure-ments made in wells yield high-densitydata, typically every 6 in. [15 cm], but pro-vide little information between wells. Datafrom cores may provide high-density“ground truth,” but these represent perhapsone part in 5 billion of the volume of thereservoir. Surface seismic reflections coverthe reservoir volume and more, but do nottranslate directly into the desired rock andfluid properties. How are these disparatedata sets merged?

Two processes are required: extrapolatingthe well data into the interwell reservoir vol-ume, then upscaling the fine-scale data tothe scale of a simulation grid block. Tradi-tionally log or core data were upscaled, oraveraged, over lithological units at the wells.Then these data were interpolated andextrapolated through the reservoir and mapsproduced for each layer—formerly a hand-drafting exercise by geologists. The mapswould be passed to the reservoir engineerwho would then generate grids, run prelimi-nary simulations on a series of grid sizes,and attempt further upscaling based on thereservoir flow characteristics.

In recent years, the process has beenreversed. The current trend is to use com-puter programs to build 3D geological mod-els bounded by seismic data, and to popu-late the models using geostatistical ordeterministic methods to distribute log andcore data.8

Scaling core and log properties up to grid-block scales is still a challenging task. Someproperties, such as porosity, are consideredsimple to upscale, following an arithmetic

averaging law. Others, such as permeability,are more difficult to average. And relativepermeabilities—different permeabilities fordifferent fluid phases—remain the most dif-ficult problem in upscaling. There is no uni-versally accepted method for upscaling, andit is an area of active research.9

After the model has been finalized, thesimulator requires boundary conditions toestablish the initial conditions for fluidbehavior at the beginning of the simulation.Then, for a given time later, known as thetime step, the simulator calculates new pres-sures and saturation distributions that indi-cate the flow rates for each of the mobilephases. This process is repeated for a num-ber of time steps, and in this manner bothflow rates and pressure histories are calcu-lated for each point—especially the pointscorresponding to wells—in the system.

But even with the best possible model,uncertainty remains. One of the biggest jobs

19Summer 1996

These and all other commercial reservoirsimulators envision a reservoir divided intoa number of individual blocks, called gridblocks. Each block corresponds to a volumein the reservoir, and must contain rock andfluid properties representative of the reser-voir at that location. The simulator modelsthe flow of mobile fluid through the walls ofthe blocks by solving the fluid-flow equa-tions at each block face. Parametersrequired for the solution include permeabil-ity, layer thickness, porosity, fluid content,elevation and pressure. The fluids areassigned a viscosity, compressibility, solu-tion gas/oil ratio and density. The rock isassigned a value for compressibility, capil-lary pressure and a relative permeabilityrelationship.

Creating the grid and assigning propertiesto each grid block are time-consuming tasks.The framework of the reservoir, including itsstructure and depth, its layer boundaries andfault positions and throws, is obtained fromseismic and well log data. The well-bred gridrespects the framework geometry as much aspossible. Traditionally, reservoir simulationgrid blocks are rectilinear with flat, horizon-tal tops in an arrangement called block-cen-tered geometry (previous page, top). Thisconfiguration ensures that the grids remainorthogonal and exactly match the mathemat-ical models used in the simulators.

However, this approach does not easilyrepresent structural and stratigraphic com-plexities such as nonvertical faults, pin-chouts or erosional surfaces using purelyrectangular blocks. The 1983 introductionof corner-point geometry in the ECLIPSEsimulator overcame these problems. In acorner-point grid, the corners need not beorthogonal. In modeling a faulted reservoir,for example, engineers have the flexibility tochoose between an orthogonal areal gridwith the fault positions projected onto thegrid or a flexible grid to exactly honor thepositions of important faults. Three-dimen-sional (3D) grids are constructed from anareal, or 2D, grid by laying it on the top sur-face of the reservoir and projecting it verti-cally or along fault planes onto lower layers.

Engineers’ requirements for more detail inthe model, particularly to examine coningand near-wellbore effects, has led to theconcept of local grid refinement (LGR) (pre-vious page, bottom). This allows parts of themodel to be represented by a large numberof small grid blocks or by implanting radial

5. For specific examples: Bunn G, Cao Minh C, Roesten-burg J and Wittman M: “Indonesia’s Jene Field: AReservoir Simulation Case Study,” Oilfield Review 1,no. 2 (July 1989): 4-14.Briggs P, Corrigan T, Fetkovich M, Gouilloud M, LoTien-when, Paulsson B, Saleri N, Warrender J andWeber K: “Trends in Reservoir Management,”OilfieldReview 4, no. 1 (January 1992): 8-24.Corbett P, Corvi P, Ehlig-Economides C, Guérillot D,Haldorsen H, Heffer K, Hewitt T, King P, Le Nir I,Lewis J, Montadert L, Pickup G, Ravenne C, RingroseP, Ronen S, Schultz P, Tyson S and Verly G: “ReservoirCharacterization Using Expert Knowledge, Data andStatistics,”Oilfield Review 4, no. 1 (January 1992): 25-39.Al-Rabah AK, Bansal PP, Breitenback EA, HallenbeckLD, Meehan DN, Saleri NG and Wittman M: “Explor-ing the Role of Reservoir Simulation,” Oilfield Review2, no. 2 (April 1990): 18-30.

6. For more on local grid refinement: Heinemann ZEand von Hantelmann G: “Using Local Grid Refine-ment in a Multiple-Application Reservoir Simulator,”paper SPE 12255, presented at the Reservoir Simula-tion Symposium, San Francisco, California, USA,November 15-18, 1983.Forsyth PA and Sammon PH: “Local Mesh Refinementand Modelling for Faults and Pinchouts,” paper SPE13524, presented at the Reservoir Simulation Sympo-sium, Dallas, Texas, USA, February 10-13, 1985.

7. Net-to-gross ratio, sometimes called just net to gross(NTG), is the ratio of the thickness of pay to the totalthickness of the reservoir interval.

8. For examples of the technique: Schultz PS, Ronen S,Hattori M, Mantran P and Corbett C: “Seismic-GuidedEstimation of Log Properties,” The Leading Edge 13,no. 7 (July 1994): 770-776.Caamano E, Corbett C, Dickerman K, Douglas D, GirR, Martono D, Mathieu G, Nicholson B, Novias K,Padmono J, Schultz P, Suroso S, Thornton M and YanZ: “Integrated Reservoir Interpretation,” OilfieldReview 6, no. 3 (July 1994): 50-64.

9. Thibeau S, Barker JW and Souillard P: “DynamicalUpscaling Techniques Applied to CompositionalFlows,” paper SPE 29128, presented at the 13th SPESymposium on Reservoir Simulation, San Antonio,Texas, USA, February 12-15, 1995.

A simulation run itself can also helpreduce uncertainty. Outside the oil industry,simulators are used to determine the reac-tion of a known environment to externallyapplied perturbations. An example is a flightsimulator that tests varying visibility condi-tions. Although a reservoir environment islargely unknown, simulators can helpimprove the description. In a process knownas history matching, reservoir production issimulated based on the existing, thoughuncertain, reservoir description. Thatdescription is adjusted iteratively until thesimulator is able to reproduce the observedpressures and multiphase flow resultingfrom applied perturbations—that is, theknown production and injection. If the pro-duction history can be matched, the engi-neer has greater confidence that the reser-voir description will be a useful, predictivetool. The history-matching process is time-consuming and requires considerable skilland insight, but is a necessary prerequisiteto the successful prediction of continuedreservoir performance.

These new techniques and programs forloading data, computing simulations andviewing results are allowing engineers to usesimulators to guide reservoir managementdecisions throughout the life of many fields.The following case studies highlight some ofthe uses of simulators in four different stagesof reservoir maturity.

of a simulator is to evaluate the implicationsof uncertainty in the static reservoir model.Sometimes uncertainty or error is intro-duced through low data quality. Anothersource of error arises because laboratory,logging and geophysical experiments maynot directly measure the property of interest,or at the right scale, and so some otherproperty is measured and transformed insome way that adds uncertainty. There isalso uncertainty in how a property variesbetween measurement points. Many reser-voir descriptions rely on core sample mea-surements for rock and fluid property infor-mation. This information is uncertainlyextended through the reservoir volume, usu-ally in some geostatistical or deterministicfashion, guided by seismically derived sur-faces or other geological constraints.

One way to reduce uncertainty is to spotinconsistencies in the properties of the reser-voir model before simulation. Three-dimen-sional visualization software, such as theRTView application, helps engineers bemore efficient in finding inconsistencies byallowing them to view the reservoir model in3D. Results of simulation runs may also beviewed, allowing faster evaluation of simula-tion runs and providing immediate insightinto recovery behavior and physical pro-cesses occurring in the reservoir (above).

20 Oilfield Review

Forties

AberdeenLomond

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■■Visualizing the reservoir model in 3D. Visualization is a reliable means of checkingreservoir models before input to a simulator. Inconsistencies in model parameters may be flagged and corrected. After simulation, results may also be viewed, allowingfaster evaluation of comparative simulation runs and providing insight into recoverybehavior. In this example reservoir pressure is color-coded to show regions of high and low pressure.

■■Texaco Erskine Project in the North SeaCentral Graben region. The high-tempera-ture, high-pressure condensate field isdue to go on production in 1997.

6250.13Pressure, psi

8674.00

RTView 96APreproduction PlanningAn example of early use of simulationcomes from the Texaco Erskine Project inthe North Sea Central Graben region(below). The Erskine field comprises fourhigh-pressure, high-temperature (HPHT)condensate reservoirs, and will be the firstHPHT field in the North Sea to come online when production commences in 1997.

Production will be from an unmannedplatform, with a multiphase pipeline to theAmoco Lomond Platform for separation.Gas will be exported via the Central AreaTransmission System (CATS) pipeline, andliquids via the Forties pipeline. Initial pro-duction with be from three wells, with threemore to be added. The production mecha-nism will be natural depletion, with no gasrecycling. Other operators in the region whohave similar reservoirs to develop arewatching how Texaco handles the hostile,overpressured field.

Simulation was selected as a way topredict production of gas for drawing updeliverability contracts—contracts promis-ing delivery of designated volumes of gas ata specified time. The main challenge in sim-ulating these reservoirs is accounting forboth the permeability reduction due to rockcompaction and the productivity loss due tocondensate banking—explained below—inthe near-wellbore region of the formationwhen the reservoir pressure falls below thedewpoint pressure.10

Developments in Gridding

Since the first grids were built, the variety, range

and resolution of oilfield measurements have

increased, and computer power and efficiency

have grown. To take advantage of these develop-

ments, reservoir engineers require better and

more comprehensive simulation software tools.

Modern 3D seismic acquisition, processing and

interpretation techniques have resulted in more

reliable and higher-resolution definition of faults

and erosional surfaces. The engineer wants to

represent the full complexity of nonvertical faults,

curving or listric faults, and faults that intersect or

truncate against one another. Another develop-

ment that requires more complex models is the

increasing use of high-angle and horizontal wells

and multilateral wells. These requirements

stretch the traditional gridding programs based on

corner-point geometry—such as the GeoQuest

GRID program—to the limit.

This has led to the development of new gridding

software techniques such as the FloGrid utility,

which will produce grids that conform to the reser-

voir framework as defined by fault surfaces and

lithological boundaries. Unstructured perpendicu-

lar bisector (PEBI) and tetrahedral grid systems

are being developed and included in gridding and

simulation programs (above right). “Blocks” in a

PEBI grid may have a variety of shapes, and they

may be arranged to fit any reservoir geometry.

The smoother gridblock shape gives a more accu-

rate simulation solution because there is less

chance of choosing the wrong grid orientation—

a potential problem with traditional grids. A PEBI

grid also allows flow in more directions from a

given grid block, important in the modeling of hor-

izontal wells, gas injection schemes or the inter-

action of wells in an interference test. These grids

are also being used as a basis for a new genera-

tion of upscaling techniques.

A further gridding development is the linking of

well test analysis with simulator programs to give

the engineer a greater range of numerical reser-

voir models than exist in analytical models.

Unstructured PEBI grids are of great benefit in

these situations, allowing the radial components of

flow into the wellbore to be combined with linear

or planar features such as the trajectory of a hori-

zontal well or a fault plane. Simulations run with

PEBI grids tend to take longer than those run on

structured grids, but the ability to capture the

structural complexity of the reservoir’s flow units

outweighs the need for speed. A compromise can

be reached by building a structured grid in the geo-

logically simple parts of the reservoir, and splicing

in an unstructured grid when geologic complexity

requires more flexibly shaped grid blocks.

■■A perpendicular bisector (PEBI) grid showing localgrid refinement around wells. Grid blocks may havea variety of shapes and can fit any reservoir geome-try. The smoother grid-block shape also gives amore accurate simulation solution because there isless chance of choosing the wrong grid orientation.

21Summer 1996

Because of overpressure conditions in thereservoir, the rock is expected to compactwith depressurization. This means the rockis expected to decrease its porosity andeffective permeability as production pro-gresses. To quantify these effects, laboratoryexperiments were conducted on rock sam-ples. The experiments showed that at theassumed well abandonment pressure of4000 psi, permeability would be reduced byabout 33% from the initial value, whileporosity would be negligibly reduced.

Modeling flow in condensate reservoirsrequires additional considerations. As pres-sure drops around the well, condensation,or dropout, occurs and liquid forms. The liq-uid saturation increases—in what is calledcondensate banking—until it is greatenough to overcome capillary trappingforces and the liquid becomes mobile. Butuntil the liquid becomes mobile, the pres-ence of immobile liquid reduces the relativepermeability to gas, resulting in a loss inproductivity. The rapid change in fluid satu-ration away from the well requires a finegrid to accurately model reservoir proper-ties. The ECLIPSE compositional simulatormodeled the regions around the wells witha refined radial grid, and the remainder witha Cartesian grid.

In addition, condensate yields varybetween the four different reservoirs, soeach reservoir fluid was represented by itsown equation of state. The local grid refine-ment and multiple equation of state capabil-ities were added to the ECLIPSE simulatorfor this project, and now form part of thecommercial package.

The simulation was used to conductuncertainty analysis for risk management.To maximize revenues, the tactic is to maxi-mize gas rates without being penalized forcoming up short. To understand the risksbehind promising a given gas rate, it isdesirable to understand the sensitivity of thesimulation results to each important inputparameter. In this case, repeated simula-tions indicated that the parameters with the

10. Crick M: “Compositional Simulation for HPHT GasCondensate Reservoirs: Follow-up,” presented at theSecond ECLIPSE International Forum, Houston,Texas, USA, April 15-19, 1996.Hsu HH, Ponting DK and Wood L: “Field-WideCompositional Simulation for HPHT Gas Conden-sate Reservoirs Using an Adaptive Implicit Method,”paper SPE 29948, presented at the InternationalMeeting on Petroleum Engineering, Beijing, China,November 14-17, 1995.

41 Water saturation % 100

Perpendicular Bisector (PEBI) Grid

Cumulative Production

ParametricMethod

ParametricMethod

Monte CarloAnalysis

Sensitivities

InitialDeliverability Distribution

Normalized Average Profile

Reserves Distribution

Probabilistic Production Profile

Deliverability

Predictedproduction

Del

iver

abilit

y

■■Schematic of deliverability and cumulative production computed for best- and worst-case scenarios. The sensitivity profiles (left)represent curves for best and worst cases, such as the lowest and highest permeability, lowest and highest compaction and all otherparameters mentioned above. Not all curves were plotted because of space constraints. All the sensitivities were combined througha parametric method modified for oilfield application. (From Smith et al, reference 11.) A normalized average profile (center) wascombined with initial deliverability and reserves distributions in a Monte Carlo method to give a probabilistic—90% confidence—pro-duction profile (right). The upper curve is the deliverability and the lower curve is predicted production. The cyclic nature of the pro-duction curve reflects the alternation between summer and winter demand for gas.

most influence on the results included gasin place, permeability and compaction(left).

Deliverability and cumulative productiondistributions were calculated from the sensi-tivity results using the parametric methoddeveloped for oilfield applications by P.J.Smith and coworkers at British Petroleum.11

A normalized average profile was combinedwith these distributions in a Monte Carlosimulator to give a probabalistic productionprofile (below).

The results of the risk analysis showed theeffects of different production scenarios onthe level of confidence in ability to delivervarious possible contracted rates of gas overthe initial plateau period. (next page,bottom). The required 90% confidencelevel for a three-year plateau period wasachieved by modifying the production ratein the first year, adding a contingency wellin the third year, and commingling produc-tion in one well between the main Erskinereservoir and the smaller but higher-perme-ability Kimmeridge reservoir.

As a result, Texaco has modified produc-tion plans, which now call for a lower pro-duction rate in the first year than in subse-

22 Oilfield Review

■■Sensitivity of Erskine simulation results to input parameters. Repeatedsimulations indicate parameters that have the most influence on simula-tion results. Quantifying the uncertainty in the most sensitive parametersis an important step toward quantifying project risk. Additional simula-tions were run with the high, low and middle values of each parameter,forming input sensitivities for the risk analysis shown below.

Gas in place

Permeability

Pentlandcontinuity

Compaction

Criticalcondensate

saturation

Trapped gassaturation

Well skinfactor

Faulttransmissibility

-20 -15 -10 -5 0 5 10 15 20

Percentage Changes in Reserves

23Summer 1996

quent years. Risk analysis suggested anadditional well in the third year, so platformconstruction has allowed a slot for a contin-gency well. In addition, production from theErskine and Kimmeridge reservoirs will alsobe commingled.

Infill DrillingInfill drilling is an expensive stage in the lifeof a reservoir. Simulation, in conjunctionwith other tools, can help guide the place-ment of wells and minimize their number.British Petroleum has harnessed simulationalong with new reservoir description to opti-mize infill drilling in the Forties field in theNorth Sea (right).

The Forties field was discovered in 1970,and produced its first oil in 1975 (middle).Current production is from five platforms,with 78 producers and 25 peripheral injec-tors. Estimated recovery of the 4.2 billionstock tank barrels (STB) of original oil inplace (OOIP) is 60%, or 90% of the mov-able oil.

The field is characterized by high perme-ability, high net-to-gross (NTG) pay thick-ness and a strong aquifer. A few years agothe Forties was considered to be essentiallya homogeneous reservoir. But early waterbreakthrough and water fingering indicateda greater level of heterogeneity thanexpected, and suggested the need for morewells to be drilled to reach bypassed zones.To understand the potential of infill drillingin the field, a simulation study was con-ducted, including careful reinterpretation ofexisting 3D seismic data and a new reser-

Confidence levels, %

1 2 3 4

90/90/90 None 4.5 75 75 75 40 0.707 0.898 1.139

80/90/90 None 4.5 85 75 75 40 0.699 0.889 1.119

90/90/90 4.5 85 85 75 45 0.738 0.937 1.176

80/90/90 Erskine andKimmeridge in E1

4.5 90 90 80 55 0.738 0.932 1.170

90/90/90 Erskine andPentland in E1

4.5 70 70 65 30 0.682 0.858 1.082

90/90/90 None 4.5 65 3095 95 0.704 0.892 1.119

90/90/90 None 5.5 3095 95 70 0.685 0.863 1.091

Erskine andKimmeridge in E1

80/90/90Extra wellin year 3

4.5 90 90 95 85 0.789 1.000 1.264

3

3

3

3

3

4

3

3

Erskine andKimmeridge in E1

Normalized reserves

90 50 10

Yearly rate,MMscf/D

Commingling Tubingsize, in.

Numberof wells Year Confidence level, %

■■Results of riskanalysis rankingsome of the simu-lated productionscenarios. Therequired 90% confidence level(bottom line) wasachieved by reduc-ing the productionrate in the first year,adding a well inthe third year andcommingling pro-duction from theKimmeridge and Erskine reservoirs.

N

BraePiperClaymore

BuchanBeatrice

Montrose

Britannia

Forties

Fulmar

Aberdeen Erskine

Lomond

Charlie

Delta

Bravo

Alpha

Echo

Forties field

U K

■■The Forties field inthe North Sea, oper-ated by BP with fiveplatforms and 103wells.

11. Smith PJ, Hendry DJ and Crowther AR: “The Quan-tification and Management of Uncertainty inReserves,” paper SPE 26056, presented at the SPEWestern Regional Meeting, Anchorage, Alaska,USA, May 26-28, 1993. ■■Production in the Forties field since 1975.

Pro

duct

ion,

103

B/D

0

100

200

300

400

500

600

1975 1980 1985 1990 1995 2000 2004

Oil production Water production

Year

Currentproduction

300-m Grid

50-m Grid

GeostatisticalModel

voir characterization to describe the hetero-geneities encountered in the turbidite sand-stone reservoir.

Simulation with a coarse full-field modelallowed identification of regions that mightbenefit from infill wells, but the results werenot refined enough for detailed well place-ment. Once a region was identified as con-taining possible infill well locations, otheraspects were considered, such as: water cutand production of surrounding wells; inter-ference tests confirming continuity or lackthereof with other layers; and reinterpreta-tion of 3D seismic data for channel identifi-cation—prospective locations tend to bealong submarine channel margins, wherethere is lower vertical permeability and soless efficient sweep.

Having passed these tests, the area wastapped for a new simulation study with localgrid refinement spotlighting the volume ofinterest (below right). The refined grid blocksize was about 50 by 50 m [164 ft by 164 ft]in area by 8 m [26 ft] in depth. Reservoirproperties were distributed in the LGR gridbased on a geostatistical model. Then theflow in the LGR grid was simulated with theECLIPSE black-oil simulator and checkedagainst the production history from wells inthe grid. The property distribution wasmodified and simulation rerun. This processwas repeated until a history match wasobtained, with only six iterations required.

The final simulation based on the refinedgrid predicted a fluid distribution at the For-ties Alpha 31 sidetrack (FA31ST) location(above right). The predicted fluid distribu-tion closely resembled that encountered andthe predicted oil production matched thecurrent rate. However, the predicted net-to-gross rock volume of the upper zone wasoptimistic relative to measured values.Lessons learned from this work have beenfed back into subsequent studies with, forexample, seismic attributes helping to char-acterize the NTG variation in the reservoir.Simulation played a similar role in assessingthe potential for infill drilling around theother platforms.

24 Oilfield Review

■■Steps in the simu-lation study of theForties Alpha plat-form area. Simula-tion with a coarsefull-field model(top) identifiedregions that wouldbenefit from infillwells. Once aregion was identi-fied as a possibleinfill well location,the location wasselected for a newsimulation studywith local gridrefinement (middle)spotlighting thevolume of interest.Reservoir proper-ties were dis-tributed in the LGRgrid based on ageostatisticalmodel (bottom) ofthe turbidite sand-stones.

Shale Water Oil

Prediction Actual

FA31ST FA31ST

■■Fluid and formation distributions predicted (left) and encountered (right) at the FortiesAlpha 31 sidetrack (FA31ST) location. The predicted distribution closely resembled thelayering encountered, and predicted oil production matched the current rate.

25Summer 1996

R.14 R.13 R.12W2

T.7

T.6

T.5

S a s k a t c h e w a n

Saskatoon

Yorkton

Regina

Moose Jaw

SwiftCurrent

Weyburn Unit

U n i t e d S t a t e s

C a n a d a

■■Weyburn field of southeastern Saskatchewan, Canada. Discov-ered in 1955, the Weyburn field has produced 314 million STB, or28% of the unit’s original oil in place.

Planning Enhanced Oil RecoveryIn an example of simulation later in reser-voir life, PanCanadian Petroleum Limited isrelying on simulation to examine the feasi-bility of CO2 injection in Unit 1 in the Wey-burn field of Saskatchewan, Canada(right).12 This field was discovered in 1955and put on waterflood in 1964. By 1994,recovery had reached 314 million STB, or28% of the unit’s original oil in place. Ulti-mate waterflood recovery is expected to be348 million STB, or 31%, leaving a largetarget for enhanced recovery methods. Anopportunity to take advantage of onemethod, gravity segregation via CO2 injec-tion, is presented by the division of thereservoir into swept and unswept layers.Carbon dioxide injected into the lower,more permeable formation has the potentialto contact large amounts of unswept oil inthe tight upper formation since CO2 is 30%less dense than the reservoir fluids at theexpected operating pressures (below right).

Evaluating the feasibility of CO2 injectionproceeded in stages. First, using the Geo-Quest fluid PVT simulation software, a nine-component equation of state was developedthat reproduced the behavior of the oil-CO2

system. The equation of state also had topredict the development of dynamic misci-bility in flow simulations while still repre-senting the physical properties of the oil-CO2 mixtures. The equation was validatedby comparison of simulated and laboratoryfloods on cores.

Second, general performance parameterswere established for the formations to beswept. These included CO2 slug size, awater-alternating-gas injection strategy, CO2

start-up pressure and post-CO2 blow-downpressure.13 Then various orientations ofinjectors, producers and horizontal wellswere tested with the ECLIPSE compositional

12. Burkett D, Besserer G and Gurpinar O: “Design ofWeyburn CO2 Injection Project,” presented at theSecond ECLIPSE International Forum, Houston,Texas, USA, April 15-19, 1996.

13. Blow-down pressure is the average field pressuremaintained after CO2 injection is stopped. Usuallythis is lower than during CO2 injection to maximizeoil recovery due to expansion of CO2.

Density Porosity

Neutron PorosityGamma Ray

Marly

Vuggy

0 150 45 -15%API

Unswept Zone

SweptZone

Producer CO2 Injection

5 m

■■Division of the reservoir into swept and unswept layers, openingthe opportunity for gravity segregation of injected CO2. Carbondioxide (blue arrows) injected into the lower, more permeable for-mation will rise to displace the oil (green arrows) remaining in thetight, unswept upper formation.

26 Oilfield Review

k max

kmin

Weyburn Unit

40-acrevertical infill

Original80-acre infill

60-acrevertical infill

Horizontalsidetrack

■■A Weyburninverted nine-spotpattern showingvertical and horizontal infill well locations and directions ofmaximum andminimum perme-abilities (kmax,kmin). Various orientations ofinjectors, produc-ers and horizontalwells were testedwith the ECLIPSEcompositional simulator to determine optimalorientations andspacings.

simulator (left ).14 Each original nine-spotpattern was found to require two symmetri-cally positioned horizontal wells in theupper zone to take advantage of the CO2

segregation process. Results of the paramet-ric pattern studies, using a 30% pore vol-ume CO2 slug, indicated ultimate recoverywithout any new horizontal wells to be anestimated 37% of OOIP. By adding twohorizontal wells in each injection pattern,simulation predicted incremental recoveryof 7.2%.

On the SurfaceOnce hydrocarbons have made it up thewellbore, most reservoir engineers considertheir job done. But tracking fluid movementthrough a complex surface network withchokes, valves, pumps, pipelines, separatorsand compressors remains a daunting task.Optimizing flow through the surface net-work allows production managers to mini-mize capital investment in surface facilitiesand fine-tune field planning.

Reservoir simulators are not designed tosolve for fluid flow all the way through thesurface-gathering facility, but they can beintegrated with network simulators built forthis purpose. An example of such a networksimulator is the Simulation SciencesPIPEPHASE system. The PIPEPHASE simula-

■■Reservoir link with surface facility. Integrating surface network simulators with reservoir simulators will allow production managersto optimize flow and fine-tune field planning.

27Summer 1996

The Next StepThe future of reservoir simulators may paral-lel developments in other oilfield technolo-gies that provide a view of fluid and rockbehavior in the subsurface. For example, theseismic industry, operating on a similarphysical scale and on equally staggeringamounts of data, has turned to massivelyparallel processors (MPPs) for data process-ing and to high-performance graphics work-stations for visualization of the results.

Simulation computer codes are being pre-pared for implementation on MPPs, but theswitch cannot be made quickly. A simulatortypically solves the fluid-flow equations onegrid block at a time. The solution does notnecessarily benefit by processing severalsteps in parallel.

For a typical simulation, doubling thenumber of processors cuts simulation timealmost in half, and increasing to 16 proces-sors reduces the time to one-tenth (above).Departure from ideal speed gains—16 timesfaster for 16 processors—is due to three fac-tors. First, the parallel linear equation solu-tion method is less efficient than the non-parallel solution. Second, it takes time toassemble and transfer data between pro-cesses. And third, load balancing betweenprocessors is uneven: some parts of thereservoir are easier to solve than others, butthe simulation must wait for the slowest.Also, the high cost of MPPs targets them forsharing within departments or companies,so one user is less likely to get sole access.

Early tests on parallelized versions of theECLIPSE simulator indicate that gains inspeed depend on the complexity of thereservoir model. A North Sea case with two-

14. Mullane TJ, Churcher PL, Tottrup P and EdmundsAC: “Actual Versus Predicted Horizontal Well Performance, Weyburn Unit, S.E. Saskatchewan,”Journal of Canadian Petroleum Technology 35, no. 3(March 1996): 24-30.

15. Dutta-Roy K: “Surface Facility Link: Production Plan-ning with Open-ECLIPSE and PIPEPHASE,” pre-sented at the Second ECLIPSE International Forum,Houston, Texas, USA, April 15-19, 1996.

■■Speeding up simulation withparallel processors. For a typicalsimulation, the 16-processor runis more than 10 times faster thana single-processor run.

Run

tim

e, s

ec

Number of processors1 2 4 8 16

Simulation Speedup with Parallel Processors

0

500

1000

1500

2000

2500tor, based on a pressure-balance techniquedeveloped originally at Chevron in the1980s, has been adapted to handle large,field-wide, multiphase flow networks,including wells, flowlines and associatedsurface facilities. Through a joint projectbetween GeoQuest Reservoir Technologiesand Simulation Sciences, the PIPEPHASEsimulator and the NETOPT production opti-mizer are being integrated with the Open-ECLIPSE system to provide a way to simulatefluid flow seamlessly from reservoir throughsurface network (previous page, top).15 Inte-gration is achieved through an iterative algo-rithm that minimizes the differencesbetween the well flow rates calculated bythe two simulators from a given set of flow-ing well pressures.

The recent focus on integrated reservoirmanagement teams is a major step in thedirection of integrated reservoir and surfacenetwork simulation. But the emphasis hasbeen on integration at the upstream end.The next step is to focus at the productionand surface facilities end.

Traditionally, the integrated study has beenapproached along two independent paths.For a project involving pressure mainte-nance through water injection, for example,the impact on the reservoir has been studiedin isolation. The reservoir simulation is car-ried out with a simplified well model:hydraulic behavior of injection or produc-tion wells is approximated through flowtables derived from single-well analysis. Asecond study is typically performed by thefacilities engineering group to evaluate theimpact of the injection water requirementson the surface facilities. The reservoirbehavior at the well is incorporated throughan injectivity index relating injection rate topressure drop at the formation.

A limitation of this divided approach isthat it ignores the true interaction betweenthe elements of the surface network, theproduction and injection wells, and thereservoir. The results of a truly integratedstudy could be quite different.

The iterative approach to integrating thePIPEPHASE and ECLIPSE systems, while rig-orous, may be limited by convergenceissues in more complex applications. Thetruly integrated solution, with the surfaceand reservoir equations solved simultane-ously, is expected to require a large effort,since significant restructuring will beneeded in both simulators. One promisingapproach is to initially develop a simple sin-gle-phase application for a gas field. Theexperiences developed in this effort couldthen be extended to address the larger prob-lem of multiphase fluids.

phase flow of oil and water in a relativelysimple reservoir with 50,000 grid blocksexhibited a four-fold speed up using eightprocessors, and even greater gains for biggermodels. But three-phase flow simulation ina 1.2-million block model filled randomlywith geostatistically derived data with highlyvariable permeability showed less dramaticimprovement.

One application of simulators that willundoubtedly benefit from implementationon MPPs is that of testing multiple scenar-ios. Simulation results are most valuable in acomparative sense. Comparisons can bemade of the production behavior of differentreservoir models to gain understanding ofsensitivity to input parameters. Or differentproduction scenarios may be tested on asingle reservoir model. Running such simu-lations simultaneously will save time andallow comparisons to be made efficiently.

In the family of tools designed to help oilcompanies make effective use of expensive,hard-won data, simulation plays a key rolein making sense of data acquired throughdifferent physical experiments, at differenttimes, at different spatial scales. Simulationis one of the few tools available for under-standing the changes a reservoir experiencesthroughout its life. Used together with othermeasurements, simulation reinforces con-clusions based on other methods and leadsto a higher degree of confidence in ourunderstanding of the reservoir. —LS