SPE 112246 Rapid Model Updating with Right-Time Data - Ensuring Models Remain Evergreen for Improved Reservoir Management Stephen J . Webb, David E. Revus, Angela M. Myhre, Roxar, Nigel H. Goodwin, K. Neil B. Dunlop, J ohn R. Heritage, Energy Scitech Ltd. Copyright 2008, Society of Petroleum Engineers This paper was prepared for presentation at the 2008 SPE Intelligent Energy Conference and Exhibition held in Amsterdam, The Netherlands, 2527February2008. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper havenotbeenreviewedbytheSocietyofPetroleumEngineersandaresubjectto correction by the author(s). The material does not necessarily reflect any position of the SocietyofPetroleumEngineers,itsofficers,ormembers.Electronicreproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract History matching reservoir models to production data has been a challenge for asset teams since the early days of reservoir simulation. Keeping these models evergreen as production data continues to arrive, knowing when a re-historymatchisrequiredandbeingabletore-history matcheasilyandefficientlyisalsoamajorchallenge whichisoftennotaddressedinatimelymanner.This needisbecomingevenmorepressingasreal-time reservoirperformancedataisincreasinglyavailable. Decisions can now be made with the support of the good qualityreal-timedatafromthereservoirusuallyinthe form of pressure data from downhole gauges and rate data from multiphase meters. With the recent integration of existing technologies, a rapidmodelupdatingworkflowisnowpossible.The historymatchingworkflowthatwasonceadiscrete processcannowbeacomputerassistedcontinuous process.Usingstatistically-basedassistedhistory-matchingtechnologyinconjunctionwithreal-timedata acquisition,datamonitoring,andreservoirsimulation software, new production data can be quickly assimilated into the reservoir model.Real-time data from the field measurementdevicesisfilteredforconsumptionbythe reservoirmodelingsoftwareandcomparedwiththe forecasts from the reservoir simulator to determine if re-history matching is required.The new data can be added to the history file and the model (revised if necessary) can be used in operational performance optimization. This rapid model updating workflow can be run semi-automaticallyonacontinuousbasisasnewproduction dataisgathered,therebykeepingreservoirmodels evergreen and providing the most up-to-date basis for the making of important reservoir management decisions. Introduction Sincetheearlydaysofreservoirsimulation,history matching has been identified1 as one of the best methods ofvalidatingareservoirmodelspredictivecapabilities. Often long periods of time have been spent adjusting the reservoirdescriptionsothatthereservoirsimulators calculatedresultsmatchtheobserveddatafromthe reservoir.Unfortunately,duetolimitationsofdata availability,computerhardwareperformanceand mantime,historymatchinghasbeenadiscreteprocess which is performed only at certain stages in a reservoirs life cycle rather than as a continuous process that updates the reservoir model as new data arrives. Although recent advancesinassistedmatchingtechnology2haveshown thereispotentialtocutthetimerequiredtoachieve history matches, often the reservoir model is out of date before the history matching process has been completed because the production data used in history matching is frozenintimewhentheprocessisstartedandisnot updated over the often many months that history matching can take. With the advent of digital oil field technology3,4,5, data isnowavailablefromthereservoirinreal-time.In particular,downholepressures6andmultiphasesurface flowrates7cannowbemeasureddirectlyand continuously.Thisreal-timedataisnowroutinelyused forreservoirmonitoring,flowassurancecalculations, production optimization8,9,10 and for reservoir engineering analysis11.However,despitemanygoodintentions12,13, reservoir modeling has been slow to take up the use of real-time production data to condition its models. While there have been some promising projects using Ensemble KalmanFilteringtoupdatereservoirmodels14,15,these have mostly been research oriented and have not yet made it into mainstream use. Theapproachtakeninthispaperisdifferentfrom those reportedpreviouslyinthatitbuildsuponassisted historymatchingtechnologythatisalreadyinthe commercialdomainandisinwidespreaduse.First,a process framework is established in which a history match of a reservoir model can be obtained. This process, which 2SPE 112246 will be summarized in this paper and is described in detail elsewhere2,16, allows the engineer to chose parameters in thereservoirmodelthattheassistedhistorymatching softwarecansensitizesothatitcanexplorewhich parameters need to be changed and how they need to be changedtoachieveahistorymatch.Theprocessalso producesanestimateoftheuncertaintyinthehistory match. While it is obtaining history matches, the process can be used to forecast reservoir performance and define confidenceintervalsintheforecastperformance.These confidence intervals can then be used as a measure of the forecastsaccuracyasnewreservoirperformancedata arrives.Ifthenewdatalieswithintheconfidence intervals,themodelisforecastingfieldperformanceto within the uncertainty of the modeling system. If the new data lies outside the confidence intervals, either the field isnotbeingoperatedasforecast(andthesimulators operatingconstraintsneedupdating)orthemodelitself requiresupdating.Theprocessframeworkprovidesan environmentwherethiscanbedoneinanalmost automatic fashion. This process will be illustrated by an example based onalargematureNorthSeareservoirwheretherapid modelupdatingprocessisillustratedbyrepeatedly assimilatingnewproductiondataintothehistory matching process, thereby keeping the model evergreen andasuptodateaspossibleforfieldoperatingand development decisions. Rapid Model Updating Process Components The rapid model updating process is comprised of the following components: Statistically-based assisted history-matching technology Robust full field black oil and compositional reservoir flow simulator Real-time field monitoring and production data management system These technology components are directly interfaced with each other to provide a comprehensive and integrated total system which is described in the subsequent sections of this paper. Assisted History Matching The availability of powerful and inexpensive computing hardware over the last decade has led to the development ofsoftwaretoolsthatcangreatlyhelpthehistory matching process. Additionally these tools allow for the inherentuncertaintyinthereservoirdescriptionand indeed the production data, and reflect that uncertainty in thereservoir performanceforecastsusedforoperational and field development decision-making. The technologies that have been employed are many, and include gradient methods,simulatedannealing,evolutionaryalgorithms andBayesianresponsesurface modeling2,16,17,18,19,20,21,22,23,24,25,26,27,28. Thetechnologyusedinthisworkistheadvanced linear Bayesian tool,EnABLE.It can be used with a number of different reservoir simulators and is designed to assist in the process of identifying multiple acceptable history matches by using a structured workflow process whichallowsmanyparameterstobeautomatically modified simultaneously. Advanced experimental design methods and linear Bayesian statistical routines are used which permit the use of objective data, subjective opinion andotherindirectinformationinspecifyingaprior distribution29. A statistical estimator based on a response surface model (the Estimator) is created as a proxy for the full reservoir simulation model and is updated after each simulationrun.Byusingtheproxytoapproximatethe simulator,extensiveexplorationofthesimulated responsesacrossthesolutionspacemaybeperformed without the computational expense of making a complete simulator run in each evaluation.Starting with a set of scopingrunsdesignedtobroadlyexploretheentire solution space followed by additional most informative runs (whereby the regions of the parametric space where the proxy model has the greatest uncertainty are explored) a reasonably predictive estimator is built.A number of historymatchsolutionsarethenobtainedmorerapidly than would be possible by simply running a large number of simulation cases. Fit for purpose history matching and confidenceintervalestimationusingtheworkflow described here typically is accomplished with the use of a number of reservoir model runs of the order of 100 to 300 runs. Methodsthatdonotemployanestimatorare variously reported to require a thousand or more runs30. The workflow process is shown in Figure 1: 1.Identify study objectives. 2.Setup simulation model as usual. 3.Identifyparameters(modifiers)for investigationinthehistorymatch(and predictionsandoptimization31,if anticipated)andtheirrangesof investigation. 4.Makemodificationstothesimulatorinput data to sensitize these parameters. 5.Generateasetofscopingruns.These runs,madeusinganexperimentaldesign procedure,providetheinitialbasisforthe Estimator model.Typically 25 runs are used for this step, although less may be sufficient forsmallnumbersofmodifiers.Multiple runscanbesubmittedforsimultaneous computation,ifmultipleprocessorsare available.6.Importthehistoricaldataonwell performance and production. 7.Validate:decidebyinspectionwhetherthe model parameterization has the potential to leadtoahistorymatchofsufficient accuracy to meet project objectives. SPE 1122463 8.Identify the observed data that is considered to be most diagnostic of reservoir and flow physicsandpickpointsforhistory matching. 9.Initialize the Estimator models using results from the scoping runs.The tool will select the most important modifiers for each match pointandconstructaninitialstatistical response surface model. 10.Generateadditionalrunstoimprovethe Estimatormodelwithasetofmost informativeruns.Thisisasequential experimentaldesignapproach.Theseruns explore where there is the most uncertainty aboutthesimulationmodel.Eachofthese runscausesthetooltoperformaBayes update of the Estimator model using the new results. 11.Conductasequenceofbestmatch simulation runs.Since each run updates the Estimator model, a sequence of best match runs is needed, where each run is using an improvedEstimatormodel.Theparameter valuesarechosenbythetoolwhich performs an optimization that is informed by the Estimator model. 12.Evaluatetherunsgraphicallyusingthe toolsbuilt-incapabilities.Modify weightingofmatchobjectivefunctionto steermatch,ensuringthatthephenomena expectedtohavethemostimpacton predictions are targeted.Repeat from 9 (or 10)untilgoodmatchesareseentobe emerging. Forecasting Under Uncertainty Onceasetofacceptablehistorymatchrunshasbeen obtained,uncertaintyintheproductionforecastscanbe explored.Simply by extending the simulated time period toincludethechosenproductionforecastscenario,the range of expected production behavior can be explored. Byrepeatingstepsintheworkflowaboveforatime period that includes the original history match period and the forecast period, confidence intervals in the production forecast can be generated. Rather than the Estimator just providinginformationaboutthequalityofthehistory match, it is now providing details of the uncertainty in the productionforecastsgiventheknownqualityofthe associated history match. For simplicity, Figure 1 shows this as Start forecasting.The details of the workflow are: 13.Generateanumberofscopingruns (typically15)toscopethenewlyadded forecastperiod.Theserunsscopethenew solution space so that the new results in the forecastperiodfollowingonfromthe historywillbetakenintoaccountinthe Estimatorbeforestartingtherefinement runs.14.Select forecast points. 15.Generateaseriesofmostinformativeand bestmatchruns(e.g.12mostinformative runsfollowedby3bestmatchruns).The most informative runs reduce the Estimator uncertaintyinthesimulatorresponseat forecastpoints.Thebestmatchesensure thatthereareforecaststhatareassociated withgoodhistorymatchruns.The Estimator is updated with the run modifiers and results at the end of each simulation run. Afurtherstepthatcanbeaddedistooptimizethe operational plan of the reservoir. By adding controllable parameters that can be subjected to optimization to the set ofmodifiersthatisbeingused(e.g.chokesettings, workoverscheduling,wellplacement,compression timing,etc.)recommendationsforfieldoperationaland development optimization can be derived. Regardingconfidenceintervalsandestimated predictionuncertainty,thetechnologyestimatesthe confidence associated with the set of results for the match point.This is a measure of the uncertainty in the value of the production at a future point in time.The measure of confidencetakestheformofpercentilevalues,andthe confidence interval (CI) is the probability that a value is betweenanupperlimitandalowerlimit.Thedefault confidenceintervalsusedinthisworkwere80% intervals. Real-time Production Monitoring The real-timeproductionsystemusedinthisworkflow, andshowninFigure2,consistsofaspecializedfield monitoringsystem(atthelocalfieldlevel),anda comprehensiveproductiondatamanagementsystem(at theofficelevel).Thereal-timeproductionsystem acquires,collects,andstoresreal-timeandhistorical intelligent instrumentation data, and provides a common desktop for visualization, field monitoring, analysis, and interpretation. It features life of the field storage capability at the officelevelwithflexiblesamplingratesaslowasper second with high precision.A broad range of specialized interpretation, analysis, monitoring, and diagnostics tools areavailabletostreamlineanalyses,processes,daily operations,productionoptimization,andreservoir management. Available configurations for the real-time production systemincludedirectinterfacewithintelligent instruments(downhole,subsea,andtopside/surface), and/or instrument interface via the operators automated processcontrolinfrastructureofDistributedControl Systems(DCS),SupervisoryControlandData Acquisition(SCADA),SubseaMasterControlSystems (MCS),InformationManagementSystems(IMS), Automated Systems, and third-party historians.4SPE 112246 The real-time production system utilizes the industry standardnetworkcommunicationsprotocol,TCP/IPto connecttheoperatorscommunicationsnetwork,andto connectremotelocationsviaatelecommunications provider, satellite services provider, over the internet, or via a wireless network for near proximity locations. Thisavailabilityofreal-timepressure,temperature (topside,subseaordownhole)andmultiphaseratedata (topsideorsubsea)meansthathighqualityreservoir performancedataarenowavailabletothereservoir engineer.Whilethesedatahavebeenenthusiastically taken up and used by operations and production engineers forfieldmonitoring,fieldsurveillance,flowassurance andproductionoptimization,thereservoirengineering community has been somewhat slower to react. Take up hasbeeninhibitedbythelackoftoolstoquickly assimilate this real-time data into reservoir models. Oneissueisthechallengeoftimescales.Whereas real-time data arrives and can be stored on a second by second basis, for this workflow, the reservoir engineer is typically interested in daily, weekly, or monthly averaged productiondata(Figure3).Soforreal-timeproduction datatobereadyforconsumptionbyreservoirmodels, some form of data aggregation is generally needed, which may include filtering, interpolation, averaging, and other summarizationmethods,dependinguponthe characteristics of the data. This aggregated data may be providedtoreservoirmodelsintheformofASCII, spreadsheet,orXML,usingtheemergingPRODML format32. This is often referred to as in-time or right-time data rather than real-time data. Therefore,priortoconsumptionbythereservoir engineer, the real-time data used in this work were filtered using a custom formula defined by the user, or a default movingaveragesmoothingalgorithmsuppliedwiththe application.Theseformulaeenablecustomisednoise filtering to smooth extreme outliers without modifying the originalreal-timedata.Thecalculationisperformed automatically on the selected time span of real-time data.Oncesmoothed,theresultingfiltereddataareaveraged andinterpolatedtoproducedataatanydesiredtime period,andmaythenbeexportedintoanappropriate format. Currently the export is a manual process whereby the averaged and interpolated data are exported, with the help of templates, in specialized ASCII format files that, may be directly inserted as Include Files to the reservoir flow simulator data deck, or imported as well history into the assisted history matching application. However, in the future it is anticipated that this data transfer will be in an XML format (PRODML) and that the rapid model update applicationswillreceivethedatawhenitbecomes availableusingapublishandsubscribemechanism similar to that available with WITSML33 data. Another issue that has inhibited the use of real-time data in reservoir models is the lack of a robust automated process for repeatedly including new production data in thehistorymatchingprocess.Becausehistorymatches have often taken so long, engineers are reluctant to repeat theprocesseventhoughthemodelstheyareusingfor decisionmakingpurposescanbeoutofdate.Thenext section addresses that challenge. Rapid Model Updating Workflow A process has been established whereby right-time data can be extracted from a real-time monitoring application inasuitablyaggregatedformatand,employingthe workflow described in the sections above, used to update reservoirflowsimulationmodelsthatareinterfacedto assistedhistorymatchingtechnologyandpotentially geological reservoir models. The key components of this workflow are illustrated in Figure 4.It should be noted thattherapidmodelupdateprocessandtheindividual componentapplicationsarecapableofhandlingany desired time period, including intra-day production data. This right-time data is then made available to the rapid modelupdatingworkflowtoappendtotheexisting history that has been used in the assisted history matching process.Thecontrolsontheexistingforecastrunsare replacedwiththenewlyobservedproductiondata controlsandtheconfidenceintervalsarounddependent aspects of the modeled production behavior re-calculated. Thisisachievedbyrerunningsteps13to15inthe workflow.Themodelsetupdetermineswhethera productionvalueisassignedordependent;dependent valuesaretypicallyphaseratesandpressuresbutmay includecompositionsandtemperatures.Newactual recordedvaluesarecomparedwithmodelgenerated valuestodeterminewheretheyliewithrespecttothe models confidence intervals. If the new data lies within the confidence intervals, then the field is operating within thelimitsofthemodelsabilitytoforecastandthe reservoirmodelisstillcapableofpredictingfield performance within the uncertainty of the model. If, however, the new data lies outside the confidence intervals, then the reservoir model is no longer capable of forecastingtheperformanceofthefieldanditneeds updating.This can be achieved by rerunning steps 9 (or 10)to12.Ifthisautomaticstepdoesnotleadtoan acceptablematchthenhumaninterventionisrequired.Action may be required of an engineer to steer the history matchdifferentlyasinstep12.Orifthemismatchis sufficiently severe the process may need to start at steps 2 or 3 where new modifiers are introduced. Oncethesestepshavebeenperformedtheforecast workflowisrepeated(steps13to15)sothatanew forecast and new confidence intervals are generated with thenewmodel.Thisnewmodelcanalsobeusedfor optimization runs to re-evaluate and revise, if necessary, the field operating and development plans. Thiswholeprocessis repeatedeverytimenew data becomesavailableandisillustratedinFigure5.Inthe SPE 1122465 reservoir management workflow this would normally not bemorefrequentthaneverymonth(seeFigure2)but could, if necessary be repeated on a more frequent basis if the field performance was changing quickly. In summary, as new history data (recorded data and operational controls) are added they are checked to see if they are consistent with the forecast based on the existing history match (i.e. within the confidence intervals). If they are,newforecastsaremadebasedontheaugmented history.Ifnot,are-historymatchisrequiredwhich includes all the previous history and the new data. This maybepossibleusingtheexistingmodifiersornew modifiers may have to be introduced. The new forecasts should be narrower than the previous forecasts from the newforecastpointbutmaybeoffsetfromprevious forecastsasoperationalcontrolshavemostlikelybeen changed. Also, the modifier ranges which generate good historymatchesshouldbereduced.Thereforeastime progressesandmoreandmoreproductiondatais assimilated,boththehistorymatchandforecastare refined.Newinformationreducesuncertaintyinthe geologicalandsimulationmodelswhichresultsinthe forecastbeingrefined(aconsequenceofrefiningthe historymatch).Thenewinformationmayallowthe selectionofanarrowerrangeofpossiblegeological models. Using this rapid model updating process to update the historymatchasdescribedabove,thesetofpossible reservoir models is always kept up to date ensuring that the best possible set of models is used for operating and field development planning decision-making. Finally, there is no inherent requirement that this rapid modelupdatingprocedureisperformedonlyusingthe reservoirsimulator.Ithasbeenshownthatduringthe history matching process using assisted history matching tools, geological models and reservoir simulation models canbekeptconsistentbyapplyingmodifierstothe geologicalmodelaswellasthesimulationmodel34.By applyingtherapidmodelupdatingworkflowtothe geologicalmodelaswellasthesimulationmodel,the geological model can be updated as new production data isassimilatedtherebykeepingboththesimulationand geological models evergreen. Example Anexampleofthisworkflowisbasedonapublicly available version35 of a simulation model of the Gullfaks field on the Norwegian Continental Shelf. The simulation model is of the eastern part of the Gullfaks field (Block 30/10) and models the field from startup in 1986 until the end of 1996. Although there are some 60 wells defined in the model only about a quarter are active early on in the production of the field. This workflow was executed by using the advanced linear Bayesian tool described above, togetherwiththefullphysicsreservoirflowsimulator Tempest-MOREandreal-timeproductionmonitoring software.Thesimulationrecurrentdataandhistorical data were prepared from the actual field performance data on a monthly basis for this example case. The objective of the example was to illustrate how a reservoir model that is kept current can be an excellent reservoir surveillance tool, and thus improve the quality ofreservoirmanagement36.Waterproductionwasthe focusofattention,becauseitwasanticipatedthatthis parameterwouldprovidethegreatestdisparitybetween simulatedandobserveddata.Forthepurposesofthis example it was assumed that todays date (current date) is the end of September 1989 and that everything prior to thatdateispastandeverythingafterthatdateis future. The model was history matched up to September 30, 1989 using the workflow shown in Figure 1 and the cumulative water production results, for the well group of producers, are shown in Figure 6.Up until this point all thewellsinthemodelswerecontrolled onoilrateand waterproductionwasallowedtobepredictedbythe models.ForecastswerethenrunfromOctober1,1989 until J anuary 1991 with the wells on bottom-hole pressure control and a field oil production rate target (Figure 6). Confidence intervals for these forecasts were generated at approximately 3 month intervals, which was the planned time period for reservoir surveillance. The current date was then advanced to the end of December 1989. The confidence intervals were compared with the actual water production observed (Figure 7). It can be seen that the actual water production rate was still withintheconfidenceintervalssetfortheforecasts. Therefore,themodelswerestillabletoforecast performance of the field within the confidence intervals andnofurtheradjustmentofthemodelswasrequired. ForecastswerethenmadefromJ anuary1990onwards using the existing models. The current date was then advanced to March 31, 1990 and the process repeated. Now the actual cumulative waterproductionwasoutsidetheconfidenceintervals (Figure 8). The deviation between actual and forecast is seen even more dramatically in the water production rate (Figure9).Thismeantthatthemodelswerenolonger abletoforecastfieldperformanceandneededtobe adjustedtoassimilatethenewproductiondata.The forecast field performance controls were replaced with the actualfieldperformancecontrolsandthemodelsre-matched using the existing modifiers (as described above this may be achieved by re-running steps 9 to 12). The resultsofthisre-matchareshowninFigure10.A comparisonoftheactualmodifiervaluesforthebest matchhistoryrunstoSeptember30,1989andtherun closest to the actual history to March 31, 1990, indicated that the Kv/Kh ratio and aquifer strength were some of the key factors in achieving the re-history match. This match isnotperfect,however,andhadamoreaccuratere-history match have been required, the process could have beenre-startedatstep2andnewmodifiersintroduced. There-historymatchingprocess(steps2through12) would then have resulted in a modified set of models that 6SPE 112246 could better have matched the production performance of the field and represented a better set of models to make forecasts for future performance of the field. However, for the purposes of this example, the re-matching workflow had been demonstrated and the process stopped here. Conclusions A process has been described and illustrated whereby new productiondatacanbequicklyandcontinually assimilated into a set of history matched reservoir models. Byextendingtheframeworkofanassistedhistory matching process, new production data can be added to theold productionhistoryand,ifnecessary,beusedto updatetheexistinghistorymatch.Confidenceintervals fromthereservoirperformanceforecastsareusedasa guide to whether the existing reservoir models are capable ofmodelingthenewproductiondata.Iftheyare,the performance forecasts are updated by simply introducing thenewoperatingconditions.Ifnot,themodelsare adjusted,repeatingthehistorymatchingprocesswhich may,ifnecessary,includenewhistorymatching parameters. As data continues to arrive, it is monitored against the forecastperformanceandtheassociatedconfidence intervals. At discrete time intervals, new operational data replacestheforecastoperationalcontrolandtherapid model updating process is repeated. 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DePauw,B.E.Ludvigsen,Integrationof GeologicandDynamicModelsforHistory Matching,MedusaField,SPE95930(2005) presentedattheSPEACTE,Dallas,TX, October, 2005. 31.H.A.J utila,SPE,andN.H.Goodwin,Energy ScitechLtd.ScheduleOptimizationto ComplementAssistedHistoryMatchingand PredictionUnderUncertainty,SPE100253 (2006),PresentedattheSPEEuropec/EAGE AnnualConferenceinVienna,Austria,12-15 J une 2006. 32.B.Weltevrede,A,Doniger,L.Ormerod,S, DeVries,Second-StageCallengeforthe PRODMLStandard:AdaptiveProduction Optimization,SPE110907(2007)presentedat the SPE ACTE, Anaheim, CA, November, 2007. 33.M.A.Kirkman,M.E.Symmonds,S.W. harbinson,J .A.Shields,M.Will,A.Doniger, Wellsite Information Transfer Standard Markup Language,WITSML,anUpdate,SPE84066 (2003) presented at the SPE ACTE, Denver, CO, October, 2003. 34.S.J .Webb,J .S.Bayless,K.N.B.Dunlop, Enabling the Big Loop- Ensuring Consistency of Geological and reservoir Simulation Models paperpresentedatthe2007AAPGAnnual ConventionandExhibition,LongBeach, California, April, 2007.35.Gullfaks Data released by Statoil is a selection of availiablepetroleumtechnicaldatafromthe Gullfaks field, finalized before 01.01.1998 36.C.C.Mattax,R.L.Dalton,Reservoir Simulation, SPE Monograph Series Volume 13, SPE, 1990. 8SPE 112246 Figure 1 Assisted history matching and forecasting workflow diagram Geomodeling, griddingRevise Geomodel No Identify modifiers & ranges Import model into EnABLE Revise modifiers or Review/Revise tolerances & match points Scoping runs (Experimental design) Import/Review Observed DataModel validated? Pick Estimator (match) Points Informative Runs Good matches emerging? Best match runs Start forecasting Agree study objectives Match, optimisation, forecasts complete? No Yes Yes Recommend Decision No Yes NoYes 12,3 4,5 6 78,9 10 11 1213,14,15 SPE 1122469 On-shoreofficesTopsideSubseaDownhole Figure 2 Offshore real-time production system Figure 3 Application of real-time data by duration and granularity10 Capacity Planning/Business DesignSecondsMinutesHoursDaysMonthsYearsPID/Regulatory ControlSupervisory ControlScheduling/Real-Time OptimizationOperational PlanningData Collection PeriodReservoir SurveillanceCapacity Planning/Business DesignSecondsMinutesHoursDaysMonthsYearsSecondsMinutesHoursDaysMonthsYearsPID/Regulatory Control PID/Regulatory ControlSupervisory Control Supervisory ControlScheduling/Real-Time OptimizationOperational PlanningData Collection PeriodReservoir Surveillance10SPE 112246 P, TQo, Qw, QgSand rateQo, Qw, QgSand rateQo, Qw, QgSand rateReal-TimeProduction SystemReal-TimeProduction SystemData HistorianField ControlsReservoir Geol ogi c ModelReservoir Flow Simulation ModelProxy Modelfor Uncert ai nty Analysi sAssi sted History Matching WorkflowASCII,PRODMLSmall LoopBig Loop Figure 4 Rapid Model Updating Workflow Figure 5 Rapid Model Updating Flowchart History match for (0, t0) Predict for (t0, tend) n = 0 Production model for (tn, tn+1) as expected? Do (tn, tn+1) data fit predicted CIs ? Change (t0, tn+1) to history and rematch, including prediction phase (tn+1, tend) Record data from (tn, tn+1) Remove runs covering original prediction phase (t0, tend) Redo runs covering (t0, tend) using new schedule, treating (t0, tn) as history and (tn, tend) as prediction n = n + 1 Yes No Yes No SPE 11224611 Figure 6 History match of cumulative water production to October 1989 and forecast with confidence intervals Figure 7 New cumulative water production history data to J anuary 1990 showing new data still within confidence intervals End of history Confidence Intervals for forecast P90P50P10New history still within Confidence Intervals after 3 months 12SPE 112246 Figure 8 At April 1990 water production data moving outside confidence intervals and re-history match is required Figure 9 Water production rate data shows an even more dramatic deviation from forecast After 6 months new history trending out of Confidence Intervals New history SPE 11224613 Figure 10 Effect of re-history match to April 1990 and re-forecast on water production rate match New re-matched run