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Top-Down Intelligent Reservoir Modeling

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Top-Down Intelligent Reservoir Modeling TOP -DOWN INTELLIGENT RESERVOIR MODELING (TDIRM); A NEW APPROACH IN RESERVOIR MODELING BY INTEGRATING CLASSIC RESERVOIR ENGINEERING WITH ARTIFICIAL INTELLIGENCE & DATA MINING TECHNIQUES ShahabD.Mohaghegh,WestVirginiaUniversity,Morgantown,WV,USA SUMMARY Traditionalreservoirsimulationandmodelingisabottom-upapproach.Itstartswithbuildinga geologicalmodelofthereservoirfollowedbyaddingengineeringfluidflowprinciplestoarrive atadynamicreservoirmodel.Thedynamicreservoirmodeliscalibratedusingtheproduction historyofmultiplewellsandthehistorymatchedmodelisusedtostrategizefielddevelopment inordertoimproverecovery. Top-Down Intelligent Reservoir Modeling approaches the reservoir simulation and modeling fromanoppositeanglebyattemptingtobuildarealization of the reservoir starting with well productionbehavior(history).Theproductionhistoryisaugmentedbycore,log,welltestand seismicdatainordertoincreasetheaccuracyandfinetunetheTop-Downmodel.Themodelis then calibrated (history matched) using the most recent wells as blind dataset. Althoughnotintendedasasubstituteforthetraditionalreservoirsimulationoflarge,complex fields,thisnovelapproachcanbeusedasanalternative(atafractionofthecostandtime)to traditionalreservoirsimulationincaseswhereperformingtraditionalmodelingiscost(andman- power)prohibitive.Incaseswhereaconventionalmodelofareservoiralreadyexists,Top-Down IntelligentReservoirModelingshouldbeconsideredacomplementto,ratherthanacompetition for the traditional technique. It provides an independent look at the data coming from the reservoir/wellsforoptimumdevelopmentstrategyandrecoveryenhancement. Top-Down Intelligent Reservoir Modeling is an elegant integration of state-of-the-art in ArtificialIntelligence&DataMining(AI&DM)withsolidreservoirengineeringtechniquesand principles. It provides a unique perspective of the field and the reservoir using actual measurements.Itprovidesqualitativelyaccuratereservoircharacteristicsthatcanplayakeyrole inmakingimportantandstrategicfielddevelopmentdecisions.Inthisarticle,principlesofTop- DownIntelligentReservoirModelingarediscussedalongwithanactualcasesstudy. TRADITIONAL RESERVOIR SIMULATION &MODELING Reservoirsimulationistheindustrystandardforreservoirmanagement.Itisusedinallphasesof field development in the oil and gas industry. The routine of simulation studies calls for integration of static and dynamic measurements into the reservoir model. Full field reservoir simulation models have become the major source of information for analysis, prediction and decision making. Traditional reservoir simulation and modeling is a bottom-up approach that startswithbuildingageological(geo-cellular)modelofthereservoir.Usingmodelingandgeo- statisticalmanipulationofthedatathegeo-cellularmodelispopulatedwiththebestavailable petrophysicalandgeophysicalinformationatthetimeofdevelopment.Engineeringfluidflow principlesarethenaddedandsolvednumericallysoastoarriveatadynamicreservoirmodel. Thedynamicreservoirmodeliscalibratedusingtheproductionhistoryofmultiplewellsina 1 Top-Down Intelligent Reservoir Models, Integrating Reservoir Engineering with AI&DM Extended Abstract, 2009 AAPG Annual Conventions, Denver Colorado complex process called history matching and the final history matched model is used to strategizethefielddevelopmentinordertoimproverecovery.Traditionalreservoirsimulation and modeling is a well understood technology that usually works well in the hand of an experiencedteamofengineersandgeoscientists. Characteristicsofthetraditionalreservoirsimulationandmodelingthatisrelevanttothisarticle andareaddressedbyTop-DownIntelligentReservoirModelingare: 1. Ittakesasignificantinvestment(timeandmoney)todevelopageological(geo-cellular) modeltoserveasthefoundationofthereservoirsimulationmodel. 2. Developmentandhistorymatchingofareservoirsimulationmodelisnotatrivialprocess andrequiresmodelerandgeoscientistswithsignificantamountofexperience. 3. Itisanexpensiveandtimeconsumingendeavor. 4. A prolific asset is required in order to justify a significant initial investment that is requiredforareservoirsimulationmodel. TOP -DOWN INTELLIGENT RESERVOIR MODELINGASAN ALTERNATIVE Top-Down Intelligent Reservoir Modeling approaches the reservoir simulation and modeling fromanoppositeanglebyattemptingtobuildarealization of the reservoir starting with well productionbehavior(history).Theproductionhistoryisaugmentedbycore,log,welltestand seismicdatainordertoincreasetheaccuracyoftheTop-Downmodelingtechnique.Although notintendedasasubstitutefortheconventionalreservoirsimulationoflarge,complexfields, thisuniqueapproachtoreservoirmodelingcanbeusedasanalternative(atafractionofthecost) to traditional reservoir simulation and modeling in cases where performing conventional modeling is cost (and man-power) prohibitive. In cases where a conventional model of a reservoiralreadyexists,Top-Downmodelingshouldbeconsideredasacomplimentto,rather thanacompetitionfortheconventionaltechnique, toprovideanindependentlookatthedata comingfromthereservoir/wellsforoptimumdevelopmentstrategyandrecoveryenhancement. Top-Down Intelligent Reservoir Modeling starts with well-known reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, History Matching using single well numerical reservoir simulation, Volumetric Reserve Estimation and calculation of RecoveryFactorsforallthewells(individually)inthefield.Usingstatisticaltechniquesmultiple ProductionIndicators(3,6,and9monthscumulativeproductionaswellas1,3,5,and10year cumulativeoil,gasandwaterproductionandGasOilRatioandWaterCut)arecalculated.These analysesandstatisticsgeneratealargevolumeofdataandinformationthatarespatio-temporal snapshotsofreservoirbehavior.Thislargevolumeofdataisprocessedusingthestate-of-the-art in artificial intelligence and data mining (neural modeling 1, genetic optimization 2 and fuzzy patternrecognition 3)inordertogenerateacompleteandcohesivemodeloftheentirereservoir. This is accomplished by using a set of discrete modeling techniques to generate production related predictive models of well behavior, followed by intelligent models that integrate the discretemodelsintoacohesivepictureandmodelofthereservoirasawhole,usingacontinuous fuzzypatternrecognitionalgorithm. 2 Top-Down Intelligent Reservoir Models, Integrating Reservoir Engineering with AI&DM Extended Abstract, 2009 AAPG Annual Conventions, Denver Colorado TheTop-DownIntelligentReservoirModeliscalibratedusingthemostrecentsetofwellsthat havebeendrilledinthefield.Thecalibratedmodelisthenusedforfielddevelopmentstrategies toimproveandenhancehydrocarbonrecovery. DETAILSANDAN EXAMPLEOF TOP -DOWN MODELING Top-DownModelingisanelegantintegrationofstate-of-the-artinArtificialIntelligence&Data Mining (AI&DM) 1-3 with solid reservoir engineering techniques and principles. It provides a unique perspective of the field and the reservoir using actual measurements. It provides qualitativelyaccuratereservoircharacteristicsthatcanplayakeyroleinmakingimportantand strategicfielddevelopmentdecisions.Thekeytothetop-downmodelingistheintegrationof reservoirengineeringwithArtificialIntelligence&DataMining(AI&DM).Followingisabrief summaryofseveralcomponentsofthisapproachtoreservoirmodelingandmanagement: a. DeclineCurveAnalysis:Conventionalhyperbolicdeclinecurveanalysisisperformedon oil,gasandwaterproductiondataofallthewells.IntelligentDeclineCurveAnalysis 4is usedtomodelsomeproductiondatasuchasGORandWaterCutthatdoesnotusually exhibitapositivebutratheranegativedecline. b. TypeCurveMatching: Usingtheappropriatetypecurves,productiondatafromallwells areanalyzed.Specialtechniquesareusedtoremovetheinherentsubjectivityassociated withtypecurvematchingprocess. c. HistoryMatching: Historymatchingisperformedonallindividualwellsusingasingle wellradialnumericalsimulationmodel. d. ProductionStatistics: Generalstatisticsaregeneratedbasedontheavailableproduction data such as 3, 6, 9months cumulative production and one, three, five and ten years cumulativeproductions.SimilardataisgeneratedforGasOilRatioandwatercut. e. VolumetricReserveEstimation: UsingVoronoi 5graphtheoryinconjunctionwithwell logsvolumetricreservesareestimatedforeachwell,individually. f. Recovery Factor Calculation: Using the results of Decline Curve analysis and VolumetricReserveEstimation,awell-basedRecoveryFactoriscalculatedforallwells, individually.Afield-wideRecoveryFactorisalsocalculated.Thiswouldbeanitemthat willbeoptimizedintheconsequentstepsoftheanalysis. g. DiscretePredictiveModeling: Resultsoftheabovementionedanalyses area wealthof dataandinformationthataregeneratedbasedonindividualwells.Thisinformationis indicativeofreservoir/wellbehavioratspecifictimeandspacethroughoutthelifeofthe reservoir. Using AI&DM techniques discrete, intelligent, predictive models are developedbasedonthelargeamountofdataandinformationthathasbeengenerated. The predictive models represent all aspects of reservoir characteristics that have been analyzed. h. Continuous Predictive Modeling: Using two-dimensional Fuzzy Pattern Recognition (FPR) technology 4, discrete predictive models are fused into a cohesive full-field reservoirmodelthatiscapableofprovidingatoolforintegratedreservoirmanagement. i. ModelCalibration: Thefullfieldmodeliscalibratedbasedonclassifyingthereservoir into“most”to“least”prolificareas,priortobeusedinthepredictivemode.Thisisdone usingthelatestdrilledwellsinthefield.Thispracticeisananalogyofhistorymatching oftheconventionalreservoirsimulationmodels.Thecalibratedmodelcanthenbeused inpredictivemodeforfielddevelopmentstrategies. 3 Top-Down Intelligent Reservoir Models,
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