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Survival of the Fittest an Optimised Well Location Algorithm for Reservoir SimulationG. Santellani, AGIP S.p.A., B. Hansen, Norsk Agip, and T. Herring, Norsk AgipCopyrfght 1998, Socieiy ofPetroleum Engin-rs, Inc.~ls pawr was prepared for presenta! lon at the 1998 SPE Asia Paci fi c Conference onIntegrated Mcdellingfor Asse t Management he ld In Kuela Lumpur, Malays ia, 23-24 March1e98.This pa~r wss se !ac fed Forpresemt lon by an SPE Program Committee fol lot ing review ofInformat ion contelnad In an abstrac t submit ted by the au thor (s ), Conten ts of the paper , aspresented , have not been rev iewd by the Smlefy o f Pa troleum Eng inaers and are subject tomnwcffon by the author(s) . The materi al, as presented, does not necessaril y refl ect any~i tion o f the Society of Petro leum Engineers , i ts o ff ice rs, or members . Pawrs presemed a tSPE meeli ngs are sub~cf to publ icat ion review by Edi torial Commi tt ees of the Soci et y ofPetroleum Engineers, Electronic reproduction, distribution, or storage of any part of this paperfor commercial purposes wfthout the written consent of the Society of Petroleum Engineers ispmhibl ted. Permiss ion to rapmduce In prfnt is restricted to an abs tr act of no t more than 300wrds; illustrations may not fm copied. me abstract must contain conspicuousa~dedgment of Were and by whom the pa~r wss presented. Wr~te librarian, SPE, P.O.Box 8X836 , Rchardacn , ~ ~83-3836 , U .S.A ., fax 01-972-952 -9435.

AbstractTraditionally, the optimization of well locations using anumerical simulator is time demanding and based on a manualtrial end error process, This paper develops an automatedtechnique to locate production wells.The automatic process is based on an algorithm that combinesa 3-D simulation model with an external optimization code.The process is genetic in nature. Starting from a maximumwell count, it proceeds in steps selecting a set of wells at theend of each 3-D simulation forecast and stops when a desirednumber of produci ng wells is reached.Two applications of the automatic well location process arepresented.The first example considers the Ekofisk field, the largest oilfield in the Norwegian sector of the North sea, with more than20 years of production, water and gas injection. In this case, 32new well locations have been automatically identified.The second example applies the process to the Sm@rbukk field,a very complex sandstone reservoir with oil and gascondensate layers and dry gas reinfection. 18 well locationshave been automatically defined and connected to 6 subseatemplates.In both cases, it has been possible to manage specific topicsrelated to the field and to obtain a large increase in theeconomic value, accelerating the production and increasing thefinal field recovery significantly.The total process run time does not exceed 24 hours.This new methodology proved to be reliable and quick and isstrongly recommended in studies where complex simulationwork is required to optimize well locations and when different

geological descriptions and development scenarios have toverified in short time, such as geostatistical realizations.The technical advantages of such a process are:. Consistent methodologies allow for true comparison betweemodels.. Added value and reserves when compared to the traditionmanual procedure.. Significant time saving.IntroductionCurrently, the use of a 3D reservoir simulator is recognizedone of the primary tool in defining the optimum productiostrategy in complex fields. Simulation is the only waydescribe quantitatively the flow of multiple phasesheterogeneous reservoirs.The 3D simulator proves to be particularly important whencomes to the planning of number of production wells and thlocations. Unfortunately, this process normally meanstedious and costly trial and error process, where the final resdepends on the ability of the reservoir engineer to fuunderstand the reservoir behavior and the operational Iimits.This paper presents an alternative approach to the manuoptimization of the production well locations in a 3D simulatmodel. This approach is automated and combinessimulation model with an external optimization code.The code follows a procedure similar to the natural selectioand applies the principle of survival of the fittest to allvertical production wells that is possible to generate insimulation grid. The selection process works in steps. Atend of every step, a fitness function is defined for eaproduction well based on results of a production forecasts.Automatic well location routineThe optimization program has been built in connection wthe in house 3D simulation model, however the code is generin nature and could be adapted to any commercial simulatchanging the reading format of the data.The routine is a combination of FORTRAN codes asimulation runs. A UNIX script controls and executeFORTRAN codes and simulation runs.The optimization process is divided in three phases:

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ase I, wellgeneration. During this phase thecode makesad blocks selection according to given constraints, based onetrophysical properties and potential productionerformances. It then generates all the possible verticalroduction wells connecting the selected grid blocks.II the information needed at this stage are read, for eachctive block of the grid, either from the 3D simulation outputiles at the end of the history match or, if the field does notave any production history, from the output files at the end ofhe initialization process.Several parameters, like the fluid saturations, or more complexunctions, for instance the production potential, the water cut,he gas oil ratio or the gas water ratio, can be selected by theuser to determine if a block should to be open to production.At the end of this first phase an input file for the simulationodel is built and a grid map displaying the new well locationss generated.o new wells are completed in blocks already containingisting wells to avoid calculation problems..t the end of phase I, the user can optionally decide to startdirectly the third phase of the process or to enter the secondphase.hase II, static wel[ screening. Optionally, a selection basedon well properties could be introduced to reduce the number ofvertical wells to be processed during the third phase.For instance, the well selection could be based on a minimumnumber of layers open to production or the well productionpotential. The main objective of this phase is to reduce CPUtime and computer memory required during Phase III.At the end of this phase a new input file for the simulationmodel is built and a grid map displaying the well locations isgenerated.

Phase III , dynamic optimization. This third phase is the realoptimization phase where the final selection is made. Asimulation run is submitted with the production well locationsresulting from the previous phases. At the end of theproduction forecast the wells are ranked according to a ~tness

and a new simulation input file is written deleting adefined number of wells according to the well ranking then anew run is submitted.The iteration process stops when a pre-defined number ofwells is reached.During this phase the process is completely automatic withoutany manual intervention once it starts.The ~tness function has to be defined by the reservoirengineer according to the final purpose of the reservoir study.It can simply be function of the well final hydrocarbonrecoveries or function of the discounted well production rates.Penalties related to excessive water or gas production can beadd in the function to match the field production constraints.In order to assure an acceptable areal distribution of the wellstwo subroutines verify that:

q if in the list of wells that have to be deleted there are morthan one well within a given interference radius r t~ttest is kept for the next run.

q If a well, in the list of wells that have to be deleted, doenot have other production wells within a maximum wedistance R it has to be kept for the next run

The Fust condition avoids deleting a well when its productionis reduced due to nearby wells and allows a double checkpoor production areas. The second conditions guaranteeminimum well distribution. Both conditions could be turneoff assuming the radii r and R smaller than the minimum grblock dimension.All wells are open , during the production forecast, at the samtime to allow a fair selection.Ekofisk field - reservoir descriptionThe Ekofisk field was discovered in late 1969 . Duringlifetime the field has undergone various phase developmentsfrom initial natural depletion to full field water injection. Thcurrent Ekofisk II redevelopment entails the installation of twnew platforms at the Ekofisk complex. One new wellheaplatform with 50 slots, and one new Processing platformDuring the redevelopment with 50 new wells to be drilledmethods are required to quickly reevaluate well locationbased on current reservoir characterization. 18 of the origina50 wells have already been approved for drilling and 11these are in production. As the 3D models are updated baseon the new results from the completed wells new locations aalso be reviewed for drilling. This was the underlyingchallenge which is addressed in this work.Ekofisk field - optimization codeThe 3D model is constructed in 33 X 40 X 12 grid and takeapproximately 45 min. CPU run time for the forecast case.Because it was the fwst application of the process, simpconstraints were introduced during the well generation phasesand 11:1. Only the grid blocks with a water saturation less than 0

could be open to production.2. At least 4 layersblocks should be completed for each wellBoth conditions were chosen on the base of a seriessensitivities runs, with the purpose to avoid an early watbreakthrough and to reduce the number of wells anconsequently the CPU time required during the 3D simulatioruns. No significant changes in the final well locations wedemonstrated by starting from a larger number of wells usina water saturation limit lower than 0,3 or reducing below 4 tnumber of layers open for each wells.Figures 1 shows the simulation grid with the 562 vertical wlocations obtained at the fist step of the process. No wells wecompleted in the mature water flooded areas of the field andthe surrounding aquifer.

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ith the purpose to maximize the final field recovery, thefunction were choose equal to the well final

ydrocarbon recovery expressed in barrels of oil equivalentBOE). The find number of well locations was set to 32. Theemaining 18 wells have previously been approved for drilling.hen this number is reached the process automatically ends.he 3D simtiation forecasts were run until December 2030,or each step of the process. a watercut limit of 90% was useds shut-in criteria for the production wells.igures 2 to 8 show the optimal well locations determined atach of the eight steps of the process. Figure 9 presents theomsponding field recovery factors versus number of wells.

o verify the location routine several tests were made.anually moving the automatic optimized well locations in theurrounding grid blocks led, in all cases, to a final recoveryower than that of the optimization code. In addition to thiserification an additional case was made starting from theriginal 50 wells to be drilled in Ekofisk. The 18 wells whichad previously been approved and completed all were locatedithin 200-300 meters of the optimum location of the code.hus not only verifying the methodology used but alsoroviding confidence in past Iocation decisions. This result isctually not entirely surprising as the analysis is based on theame 3D model. The major difference is that using theptimization code the results were available within 24 hours.he manual methodology took approximately 2 man months toobtain the same locations.Using the automatic well location process, 32 vertical wellshave been located with a large increase in the 3D model finalfield recovery compared with the old manual locations.Although, only vertical wells could be generated. It waspossible to obtain a good indication about possible horizontalor multilateral well paths identifying the areas with a highproduction potential from the vertical well location maps. Forinstance, In figures 7 and 8 several vertical wells, located innearby grid block cells, could be effectively replaced by asingle advanced well.Sm@rbukk field - reservoir descriptionThe Sm@rbukk field, discovered in 1985, is locatedapproximately 200 Km of the cost of Mid-Norway.The field production strategy-is divided in two stages. The firststage is a liquid production phase with four subsea productiontemplates. During this phase, most of produced gas isreinfected in the field for pressure maintenance. The secondstage is mainly intended for gas export. Two productiontemplates will be added and aIl the injection wells will beconverted to producers.A maximum number of four wells can be connected to eachsubsea template.The reservoir study focused essentially on optimizing thefuture production well locations for each subsea template andto verify the total number of wells for a complete exploitationof the field. Four drilled wells were considered fixed as the

injection well locations, however sensitivitiesremovingladding injectors were conducted.Because of the high geological uncertainties three differegeological models were incorporated in the 3D simulatiogrid. We refer to them as: Deterministic,lassparameters mapping based on well data; Seismic, result ofseismic Iithology study; Stochastic, one of the most probabrealizations of a geostatistical study.Smarbukk field - optimization codePhase I and II, well generation and static screening:1.2.3.

Only the grid blocks with a water saturation equal toirreducible water saturation were open to production.No wells could be located in the two nearest blocsurrounding existing gas injection and production wellsAt least 4 layers/blocks should be completed for each we

With these constraints, 450 production vertical wells wegenerated in the 3D simulation grid, avoiding an early waproduction and gas breakthrough.In addition, a subroutine was written to automatically connein the simulation model, each well to the closest substemplate and to assign the corresponding well flperformance table. In this way it has been possible to propersimulate the production constraints associated to each substemplate, like the production start up time, and to run the fiproduction forecasts under surface production limits.Figures 10 shows the Sm@rbukk field simulation grid withvertical well locations obtained at the fist step of the process.The dynamic well selection phase was run in parallel for eaof the six production templates. At the end of the productioforecasts the wells belonging to each subsea template wranked separately, based on a fitness function set equal towell final oil recovery, during the initial liquid productiophase, or equal to a combination of the well final oil andrecoveries, during the gas export phase.To complete the automatic well location routine 9 steinclusive of 3D simulation forecasts, were required fir a torunning time of about 20 ~U hours.Using the automatic well location process, 18 new vertiwell locations have been defined, over 450 possible locatiofor each of the three different geological models, considerabreducing the overall time to perform the simulation studyincreasing the net present value of the project, with a highand gas recovery acceleration, compared with the previowell locations, manually defined. A plot of hydrocarbrecovery versus number of wells was obtained for the enfield and for every production subsea template allowingeasy display of the incremental recovery associated to eproduction well and consequently an objective way to defthe final number of wells required for the field development.Figures 11 12 and 13 present the final well location genera

~=~ by the automatic well location process for each of the

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4 G. SANTELLANI, B. HANSEN AND T. HERRING WE 397different geological models. Due to the large differences in the presented at the 1994 SPE International Petroleum Conferepermeability distribution and absolute values, few production and Exhibition, Veracruz, Mexico, 10-13 October.wells were obtained in the same location in all the geological 3< Beckner B.L. and Song X.: Field Development Planning UsmodeIs. The wells with consistent locations are the wells with Simulated Annealing Optimal Economic Well Schedulingless associated risks and were recommended to be prioritized Placement; paper SPE 30650 presented at the 1995 SAnnual Technical Conference and Exhibition, Dallas, Tin the drilIing list. U.S.A., 22-25 October.Discussion

results of this work are of a very practical nature. The wellocations which are selected have proven to be correct in theince that the same locations have been selected usingraditional methods. Obviomly the locations are only asccurate as the simulation model used to define them, and thisilI remain the weakest link. The objective of improving thefficiency of locating wells has been met leading to increasedalue of the asset.t is recommended that this procedure be used in connectionith geostatistic realizations in order to evaluate well risk andverall reserves in alternative reservoir descriptions.he question of whether or not well locations defined with ourutomatic routine are truly the optimum in a rigorous sense isalid and has not been addressed.

An automated technique to optimize vertical productionwell locations in 3D simulation models as been definedproving to be a powerful and robust toolThe time savings of the method is significant and allowswell optimization to be utilized on a much larger scale.Verification using traditional methods confirms the validityof the results.Use of the method in various geological realizations leadsto a prioritization of low risk ~ells which are consistentlyobtained in similar locations.

he authors wish to thank the management of the Ekofisk andm@rbukk fieId partner companies, Phillips Petroleum Co.,ina Expiration Norway SCA., Elf Petroleum Norge AS,orsk Hydro Production AS, TOTAL Norge, Statiol, Mobilxploration Norway, Neste Petroleum, Saga Petroleum forermission to publish this paper. The opinions in this paperre those of the authors and do not necessarily represent thosef the partner companies.

. MrinaI, K. Sen, Akhil Datta-Gupta, Stoffa P,L., Lake L,W. andPope G.A.: Stochastic Reservoir Modeling Using SimulationAnnealing and Genetic Algorithms; paper SPE 24754presented at the 1992 SPE Annual Technical Conference andExhibition, Washington, D.C., U.S.A., 47 October.Rian D.T and Hage Asmund: Automatic Optimization of wellbcations in a North Sea Fractured Chalk Reservoir Using aFront Tracking Reservoir Simulator, paper SPE 28716 258

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PE 39754 SURVIVAL OF THE FITTEST AN OPTIMISED WELL LOCATION ALGORITHM FOR RESERVOIR SIMULATION

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ig. 3 - Ekofisk field, vertical well locations. Automaticetl location process step 3. Fig. 5 - Ekofisk field, vertical well locations. Automawell location process step 5.

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6 G. SANTELLANI, B. HANSEN AND T. HERRING SPE 3

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PE 39754 SURVIVAL OF THE FITTEST AN OPTIMISED WELL LOCATION ALGORITHM FOR RESERVOIR SIMULATION

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]g. 10- Smarbukk field, vertical well locations. Automatic;lI location process step 1. Areas belonging to differentbsea production templates.

ig. 12- Smorbukk field, final verticai weli locations. As aesult of the automatic well iocation process applied to thetochmtlc model. 9C 1

Fig. 11- Smarbukk field, final vertical well Iocations. Aresult of the automatic weil location process applied toDeferminhtic geological model.

Fig. 13- Smarbukk field, final verticai well iocations. Asresult of the automatic well location process applied to thSeismic model.

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