Technical Proposal for Research Work onUse of Geomodelling and Visualization for Earth, Energy, Economy and Environmental (EEEE) Management Prepared by SYED ADNAN HAIDER ZAIDI Submitted To University of the Punjab, Lahore, Pakistan Introduction Preparing for the energy transition We are entering a transition period between situations - the current situation - in which oil is preponderant and a situation in which new energy sources will have taken its place. It will be a period during which we will still use oil, while at the same time gradually developing alternatives that are kinder to the environment.Reducing emissions of CO2

Becausewearegoingtocontinueusingoil,gas,andcoalduringthetransition,and becausetheircombustionproduceslargequantitiesofCO2,wemustdoeverythingwe cantocontroltheseemissionsandcombatthepredictedclimatechange.Geoscientists shouldbestronglycommittedtothesearchfornewtechnologiesforthecapture, transport, and storage of CO2.

Developing other energy sources Thereisnomiraclesolution:bioresources,fuel/electricityhybridization,synfuels, hydrogen,etc.Thesedifferentapproachesmustallbeexplored.Somearereadytoday, whileotherswilltakelongerbecauseofthetechnologicalobstaclesthatremaintobe overcome.Asapioneer,Geoscientistsshouldcontributetheirresearchworkinthe development of alternative energy resources.

Optimizing petroleum Thegoalisnottoproducepetroleumdowntothelastdrop,buttogivesocietytimeto develop energies likely to replace it. In this regard the key issues are, of course: 1)How to find more hydrocarbons (oil and gas), and2)How to recover more hydrocarbons from the reservoirs Continuousdepletionofhydrocarbonreservesallovertheworldandenvironmental consciousnessevenaboutalternativeenergyresources,demanddevelopmentofnew technologiesandaccesstotheopportunitiesforthedisposalofwastematerialsintoan environment friendly manner. To encounter the coming energycrisis, Energy Resources Geoscientistsshouldpreparethemselvestodevelopinnovativetechnologiesinthis regard.TounderstandtheinteractionofPhysicalandChemicalProcessescontinuedin Earth,EnergyandEnvironmental(EEE)systemsrigorously,UseofGeomodellingand VisualizationforEarth,EnergyandEnvironmental(EEE)Managementwillbemore prominent in near future especially into following categories:Petroleum Systems Evaluation and Prospectivity Analysis Use of Reservoir Characterization and Modelling in the Development ofOil and Gas Fields Better Utilization of Nuclear, Hydrodynamic and Alternative (NHA) Energy Resources Modelling of the interaction of Physical and Chemical Processes continued in Earth, Energy and Environmental Systems Data Synthesis Programme with Sedimentological, Chronostratigraphical, Geochemical, Isotopic and Seismic Analysis and Interpretations Targets 1)Definition of Sequence Stratigraphic Framework and a series of Depositional models 2)Establishment of a Chronostratigraphic Framework with Biostratigraphic and Isotopic analysis to provide essential age and environmental data 3)Correlations will make use of any available 2D / 3D Seismic data to prepare i.Depositional cross sections ii.Fence Diagrams iii.3D Block Diagrams iv.Chronostratigraphically resolved block diagrams or Geophantasmograms e.g. at Barremian or Aptian levels 4)1D, 2D, and 3D Basin Modelling and Prospect Evaluation 5)Definition and Prediction of Reservoir Distribution and the major controls on reservoir quality variation 6)Production of Reservoir Distribution, Reservoir Quality, Reservoir Carriers & Barriers and Seal Effectiveness maps for different Reservoir Intervals Sedimentology and Reservoir Geology 1)Facies analysis based on core descriptions 2)Augmentation of Core descriptions by Sedimentological interpretations of FMI logs 3)Additional Lithological and Facies analysis through reservoir sections based on conventional wireline log suites and integration with datasets derived from core and FMI studies 4)Reservoir Quality Investigation a)Primarily on core analysis data b)Log porosity evaluation beyond core control 5)Augmentation of Petrographic analysis to review the main controls on reservoir quality investigation 6)DevelopmentofSedimentologicalandPetrographicinputstothe Sequence Stratigraphic framework and generation of Depositional models 7)Regional mapping of variation in depositional environments and reservoir and seal quality a) Facies Analysis: 1)Generation of depositional models and interpretation of candidate stratal surfaces a)Core descriptions as primary database i)Sedimentological description ii)Petrographic, Isotopic and biostratigraphic analysis b)Calibration between the cores and the wireline logs b) Conventional Wireline Log and FMI Analysis 8)Understanding of the lithological, facies and mineralogical characteristics 9)Interpolationofthefaciesanalysisandreservoirqualityinterpretations beyond the cored intervals 10) Coretologcorrelationandafaciesbreakdownandlithological interpretation of the uncored intervals based on wireline log signatures and using the constructed depositional models 11) Interpretationwillbeconstrainedbyanyrelevantisotopic& biostratigraphic data 12) Generation of high resolution facies data by FMI logs interpretation 13) Calibration of FMI reviews / interpretation to the available core data will result into: i)Generation of Borehole Structures Log and typing of all relevant dip information The final interpreted information will have the potential: 1)To greatly improve the depositional modeling by extending the high-resolution information derived from detailed core sedimentology to a significantly thicker interval 2)To aid the Sequence Stratigraphic interpretation of reservoir and seal intervals, providing a basis for accurately picking flooding surfaces, sequence boundaries and parasequences b)Petrographic and Diagenetic Analysis 1)Reservoir quality estimation by Petrographic and Diagenetic analysis 2)Opticalpetrographicanalysisofselectedsamplestypicallyincluding300 point counts per thin section, to generate a representative digital dataset 3)Augmentation of Thin section petrography of specially chosen samples, to helpquantifyandcharacterizethemineralogy,studythequalityand provenanceofsediments,andtounderstandthedistributionand paragenesis of clays and cements4)Development of a high quality descriptive dataset and a diagenetic model to account for the observed pore system characteristics 5)Observationoftheimpactofchloriteauthigenesisanditsrelationshipto quartz cementation on Clastic lithostratagraphic units 6)UnderstandingoftheRadiogenicisotopesystematicsbyanalysingRadiogenic isotope data (mainly Rb-Sr, Sm-Nd, Lu-Hf, U-Th-Pb and Re-Os)usedasindicatorsofrock-formingprocesses,includingpetrogenesis ofrocks,provenanceandmassbalanceofsediments,evolutionofthe continental crust, ore-forming processes and fluid-rock interaction. c)Reservoir Qualtiy Investigation 1)Integrationofcoredescription,petrographicandcoreanalysisdatato investigatereservoirqualityandtheprimarycontrolsonreservoirquality variation 2)Derivationofporosityandpermeabilitycharacteristicsfromroutinecore analysis data and Investigation of the relative role of primary (depositional or facies related) and secondary (diagenetic) processes on these 3)Log porosity calculations calibrated to core analysis Chronostratigraphy using Biostratigraphic and Isotopic analysis 1)Biostratigraphic emphasis will be placed on quantitative palynology, with volumetricmicropaleontologyandsemi-quantitativenannopaleontology inwhichquantitativepalynologywillbeundertakenastheprime discipline 2)Somechronostratigraphicsectionsofclasticsedimentsarelessfacies controlledthereforevolumetricmicropaleontologyandsemi-quantitative nanno paleontologywill be run over intervals that are suggested from the palynological results 3)30 m palynological sampling interval will be adopted, with a provision for infill over boundaries/events or potential intra- formation hiatuses 4)Identificationofargillaceoushorizonsthathaveyieldedpalynofloras indicative of deposition under fully or near fully marine conditions 5)Recoveryofage-diagnostictaxatoassistincalibrationofbioeventse.g. dinocystwhichisconsideredasthebestagedatingcriteriaforsome Cretaceous Clastic Formations6)Interpretationofeventsandrelatethemtothesequencestratigraphic framework of Haq et al., 1987 or Hardenbol et al., 1998 7)Incombinationwiththesedimentologicalandisotopicanalysisprovision of a biostratigraphically constrained sequence stratigraphic framework 8)Recording of miospore component of palynofloras as the quantitative and qualitative events in different epochs which are known to be of age-dating and correlative value 9)Identificationoflargescalevegetationalchangesonthehinterlandfrom grosschangesinthemiosporecompositionandtheirrelationto isochronous climatic change or changes in the water table 10) Quantitativedocumentationofthemarinemicroplanktonandterrigenous miospores to determine lateral trends in the non-marine/marine ratios and their maps in relation to the biostratigrpahic framework to define onshore-offshore trends 11) Documentationofreworkedpalynomorphstoprovidevaluable information on the stratal composition of the sediment source area. As the compositionchangesthroughtimeandcertaintypesandvolumesof recycled material can be diagnostic of specific depositional packages 12) Micropaleontology analysis using semi-quantitative (five-fold abundance) loggingofalltaxainconjunctionwithvolumetrictechniquestoidentify maximum flooding surfaces in deepwater areas where variation in wireline log character may be limited 13) Individualcalcareousnannofossiltaxapresentinthesampleswillbe countedover200fieldsofviewandtheirtotalsexpressedsemi-quantitatively using a five-fold abundance scheme 14) Integrationoftheinformationresultingfromabovementioned Biostratigraphictechniqueswiththeinformationderivedfrom geochronologicallysignificantisotopesystemse.g.U-Pb,K-Ar,andAr-Arfordevelopingamoreaccurateandquantitativechronostaratigraphic units. Moreover use isotope geology to prepare Relative Sea Level curves for each basin adressing history of its indvidual tectonic evolution Integrated High Resolution Sequence Stratigraphic Framework 1)Integration of all available biostratigraphic, isotopic and sedimentological datatogenerateanintegratedhighresolutionsequencestratigraphic framework 2)Use of that integrated high resolution sequence stratigraphic framework to understand and predict reservoir geometries in the subsurface 3)Provision of chronostratigraphic framework and definition of depositional environmentswiththehelpofresultsderivedfrombiostratigraphicandisotopic analysis4)Filteringofthefaciesinfluenceonbiostratigraphicandisotopicrecovery toincreasethevalueofquantitativebiostratigraphicandisotopicevents whichwillconsequentlyincreasethenumberofpotentialdatumsfor correlation 5)Thisframeworkwillbeconstrainedbyknowledgeofthemostrelevant depositionalmodelsandthecharacterofcandidatestratalsurfacesfrom sedimentological analysis 6)This data driven (not model driven) sequence stratigraphic scheme would beusedtobetterunderstandreservoirandsealdistribution,predict reservoirconnectivity and flow characteristics, and act as a powerful tool for future exploration 7)RecognitionofType-1andType-2sequenceboundaries,normalor forcedregression,potentialby-passzonesorhiatusestosuggestthe potentialfordevelopmentofdetachedLowStandSystemTractsinan offshore/distaldirection(ifsedimentationrateswerehigh)and identificationofpossibleShelfMarginSystemTracts.Itrequires combinationofsedimentologicaldata,biostratigraphicdata,isotopicdata and seismic data. 8)Mappingexercisewithcombinationofsedimentologicaldata, biostratigraphic data, isotopic data and seismic data as the final element of the reservoir geological phase of the study. Petroleum Geochemistry 1)Determination of the quality of source rocks which are present with in different chronostratigraphic sections. This will include but not be limited to: a)Depositional environment b)Thickness, organic richness and Kerogen type c)Pyrolysis data d)Kerogen maturity data e)Biomarker data f)Oil data g)Oil to source correlation data h)Isotope data 2)Specific tasks will include the following: a)Review of current knowledge of the hydrocarbon shows and discoveries found to date in the basin and assess the likely character of the source rocks for those shows and tests. b)Review the available temperature data for wells drilled in the basin with the objective of creating a geothermal database.c)Review existing ID and any other modeling work that has been done to date, and report on the findings. d)Review the existing understanding of the distribution of hydrocarbon kitchens, timing of hydrocarbon generation and migration. e)Recognition of source rock intervals of potential interest from wireline logs (SRFL). f)Confirmation of the maturity of the section and derive from that the heat flow history. g)Making some broad conclusions on the likely effective source rocks contributing to the known hydrocarbons in the basin. 3)Basic data synthesis including the following tasks: a)Geothermal data collation and temperature correction; creation of maps of present day geothermal gradients and temperatures at key horizons; b)Source rock data synthesis by formation to create maps of posted source richness and quality (TOC and HI); c)Source rock richness and quality prediction from logs for all relevant wells using (SRFL). Resultant data in yield (Kgs/ton) to be added to the source rock richness maps as above; d)Determining maturity gradients (VR and SCI) for all possible wells and posting to maps with maturity at key source rock horizons; e)Reviewing all known hydrocarbon occurrences and posting to maps by reservoir with an analysis of any recognized variability. 4)Somerequirementforthegeochemicalanalysiswillencompassthefollowing issues: a)Kerogenmaturitydatatodeterminepalaeoheatflow(vitrinitereflectivity andsporecolouration).Aprovisionofsampleswillbemadeforanalysis from a selection of wells across the study area. b) Sourcerockcharacterizationwithselectedsamplesofprovensource potentialfromexistingscreeninganalysisandpossibleadditional screeningworkwheregoodsourcepotentialissuspectedfromother information is likely to be required. c)Characterization of fluids and shows where there are no existing data or existing data are inadequate. 5)Static modeling will involve the following major tasks: a)Creationofmapsoferosionatkeyhorizonsmostparticularlyofthe significanttectonostratigraphicevents.Thisworkwillincludethe identificationandestimationofamountsofupliftanderosion(inversion) from geological, geochemical and geophysical data sources b)1Dmodelingofselectedwellstoincludetheestablishmentofauniform breakdown of events for use in modeling in the study area, calibration and determinationofpresentdayandpalaeoheatflows,selectionof appropriatesourcerockkineticparameters,calculationofproductivityof selectedsourcerocksandcalibrationtoporosityandpressuredatawhere available; c)Creationand1Dmodelingoffurtherpseudowellsanditerationto determine hydrocarbon productivity. d)The work programme described above will ensure that all geochemical data is effectively integrated to produce the source quality, distribution, maturity, kitchen and timing maps. These will then be available for input to migration modeling. Seismic Interpretation 1)Seismicinterpretationofkeysurfacesrequiredformaturitymappingand migration modeling 2)Afterloadingofalltheavailableseismicdatatoaworkstation,allavailable ZVSP, VSI, Check shot and or any other velocity data will be used to tie the study wells to the seismic data 3)Thechronostratigraphicandsequencestratigraphicframeworkestablishedfrom biostratigraphic, isotopic and sedimentological analysis and interpretation will be usedtopickkeyhorizons.Principallythesewillcomprisethesource,reservoir, and seal surfaces which will be the main input for migration modeling. They will also assist with maturity mapping of the source facies. 4)Tectono Stratigraphic surfaces will also be mapped regionally to understand uplifting, erosion, and compaction to assist restoration of paleo-topography. This will then be used to establish geometry of the reservoir horizons and seal intervals at the time of hydrocarbon generation. 5)Fault distribution maps will also be prepared together with an understanding of fault movement through time and fault seal analysis. Participation in relevant research projects continued at various international research centers and institutes AfterdatasynthesisanditsintegrationintoauserfriendlyGISdatabasewecanget benefitfrom3DBasinmodelingsoftwaresbasedoncomplexrealcasestudiesof Geometrical, Analytical, Hydraulic and Diffusion Oriented Normal & Inverse Process to ResponseSimulationofSedimentationfor3Dmulti-lithologicalstratigraphicmodeling tounderstandbasinarchitectureandforsubsequentreservoircharacterizationand modeling. We will then focus our research work into Petroleum Systems Evaluation and ProspectivityAnalysisandUseofReservoirCharacterizationandModellinginthe Development of Oil and Gas Fields. Technical Proposal for Petroleum Systems Evaluation and Prospectivity Analysis 1) Migration Modelling Migrationmodelingwillbeconductedusingamigrationmodelingsoftwaree.g.IES PetroCharge. Given the location of source kitchens and depth structure maps of carrier / reservoirhorizonsatthetimeofhydrocarboncharge,migrationmodelingwillbe conductedtodeterminedrainageareaswithinthecarrier/reservoirhorizonsandthe likelyflowpathsormigrationroutesofhydrocarbonsfromthehydrocarbonkitchen through the carrier / reservoir system to individual closures. Variation in carrier / reservoir facies, their geomechanical properties and porosities, seal effectivenessandfaultdistributiontogetherwithanunderstandingoffaultmovement throughtimeandfaultsealanalysiswillbemodeledtodeterminetheirinfluenceon migration paths and establish the presence of potential migration shadows. 1Dmodelingresultscanhelptoestablishthetimingandtheamountofgenerated hydrocarbonsthathavetobeinjectedintothecarriersystems.Itwillbepossibleto analyzepotentialfillandpotentialspillhistoriesforgivenaccumulations.Furthermore closurevolumescanbecalculatedandfiguresforpossiblytrappedhydrocarbon accumulations can be given depending on reservoir thickness and porosity. Modellingparameterscanbemodifiedinordertorunsensitivityanalysis.Analysiscan be calibrated to best fit known accumulations and can establish the reason for dry holes. 2) Prospectivity Review Theresultsofmigrationmodelingwillbeusedtodefinehydrocarbonaccumulations. Theseaccumulationswillthenbesubjectedtoarigorousandconsistentprocessof prospect risking and ranking. 3) Prospect Risking Each prospect will be risked on the following criteria: Source quality Source maturity Reservoir presence Reservoir quality Trap geometry Trap seal Timing of charge in relation to timing of closure Presenceofmigrationroutesattimeofchargeincludingtheinfluenceoffaults and fractures Preservation of hydrocarbon in trap Degradation of hydrocarbon in trap 4) Prospect Ranking MonteCarloanalysiswillthenbeusedtodetermineupsideanddownsidehydrocarbon volumes (P10:P90). This will then be used to determine a final ranking of the prospects. TECHNICAL PROPOSAL FOR USE OF RESERVOIR CHARACTERIZATION AND MODELING IN THE DEVELOPMENT OF OIL AND GAS FIELDS By SYED ADNAN HAIDER ZAIDI SUBMITTED TO University of the Punjab, Lahore, Pakistan Abstract Eachapproachofreservoircharacterizationandmodelinggivesspecificconstraintsfor thegeometryofsedimentarybodiesandtheirinternalstructuresbutreservoir characterization and modeling with 3D reservoir restoration and de-compaction for paleo- topographic reconstruction, establishment of sequence stratigraphic frameworkand their associated diagenetic overprints with their impact on variations in porosity, permeability, andhydrocarbonsaturationsandforwardandinversemodelingofgeologicalprocesses (bothdepositionalandpostdepositional),oilandgasreservedeterminationand development methods can be improved, specially in case of complex reservoir types. Weaimtofillthescalegapbetweenmodelingonhydraulicscaleaspracticedby hydrologists,andatbasinscale,aspracticedbygeodynamicists.Withincreasingwire line logging expertise, seismic technology and computing power this technique is rapidly expandingnow,andinourviewaverypromisingapproachforthenearfuture. Ultimately,weplantotesttheapplicabilityofourforwardmodelsgeneratedbythese techniques with inverse modeling from existing data. Nevertheless, in order to be realistic thesesurrogated3Dstaticreservoirmodelsneedtobevalidatedwithdynamicflow simulations by production history matching. The expected findings of our planned case studies of oil and gas fields will be as follows: 1)Generationofbest-fitstratigraphiclayeringandcompartmentalizationofreservoirs using3DSeismicMulti-AttributeGeo-VolumeVisualizationInterpretation(GVI) techniques. 2)Finetuningoftheinitialinterpretationbyintegratingresultsofnumericalmodels, whichexaminetherelationshipsbetweenfaultgeometries,slipdistributions,and horizondisplacements.Thecombinationofthesetoolsallowsustoaccurately constrain fault geometries in 3-D and to develop a hypothesis for fault evolution in oil and gas fields that augments previous reservoir characterizations. 3) Preparationofsub-seismicfaultsandthefractureintensitymapswhichwillform input for Discrete Fracture Network modeling to derive various flow properties like fracturepermeabilitytensors,matrix-fractureinteractionparameters,whichcanbe used as input for flow simulators.4)Establishmentof3DSeismiccubewiththecombinationofinversion,lithologic crossplotcut-offsandcoherencytovisualizetherelationshipbetweenvarious petrophysical and seismic parameters. 5) Developmentofareservoirscalemodel(intheformofDepositionalcrosssections, Fence Diagrams, 3D Block Diagrams and Geophantasmograms) that will predict and quantify facies variations and stratal architecture for the inter-well space, beyond the seismic resolution on the basis of correlation panels of conventional wireline log data and image log data, core data, net to gross ratio maps and recent and ancient outcrop analogues. 6) Aspartoftheproject,sensitivitystudiesandvariable-resolutionsimulationswillbe runfordifferentdatasets,toshowthatourexpectedmodelcanbeusedtoidentify optimum lateral and vertical scale characters of the reservoir, as well as to capture the salient lateral and vertical variations and facies continuity. 7) Diagenesis and subsequent compaction will be estimated for the computer generated sedimentarygeo-bodies.Thensyntheticlithologieswillbetranslatedintothe distributions of rock / flow unit properties, thus surrogated 3D static reservoir models will begenerated in a format, which will be suitable for a multiphase flow reservoir simulator. 8) Calculationanddisplayofvariables(suchaswatersaturationandneutron/sonic minusdensityporosity)ascolorattributestoshowinterparticle,intraparticleand totalporosity,bulkvolumeofwater(Swxporosity),bulkvolumeofhydrocarbons {(1-Sw)xporosity}andalsovisualdisplaysofempiricalestimatesofseparately developed porosities and permeabilities. 9)Applicationoffilterstotargetareasofthefieldswithspecificreservoirproperties based on selected clay volume, porosity and water saturation cutoffs. 10) Systematicalexaminationofvariablesinthemodeledreservoirvolumestoassess controlsandperhapsenhancedviewsofareastobetargetedforadditional hydrocarbon recovery. 11) Comparisonofthesevisual,empiricallyderivedmodelsofreservoirstothe simulations of temporally equivalent reservoirs.12) 4Dseismictimelapsemonitoringandproductionhistorymatchingtoremovethe constraints of static reservoir models and the dynamic flow simulations. 1) Introduction Integrated reservoir characterization and modeling is central to all aspects of hydrocarbon field development and when performed effectively can help to reduce development risks andtomaximizefieldreturns.Itisacontinuousprocess,fromfielddiscoveryto abandonment,andembracesanumberofcomplementarygeological,geophysicaland engineeringtechniques.Thesimulationofreservoirsneedsaquantifiedandrealistic description of formations, together with an assessment of their uncertainties. Reservoircharacterizationandmodelingrequiresamultidisciplinaryteameffort.It involvesasystematicintegrationofgeological,geophysicalandengineeringdataand improvesdescriptionofreservoirpropertiesinandbetweenwells.Thisresearchwork aims to utilize a comprehensive application of modern concepts in reservoir modeling for field simulation. A vast array of newreservoir characterization techniques has been developed in the last decadebyuseof3DSeismicMulti-AttributeGeo-VolumeVisualizationInterpretation (GVI) techniques, Establishment of 3D Seismic cube with the combination of inversion, lithologic crossplotcut-offs and coherency, multivariate statistics, geostatistics, artificial intelligence,boreholeimaging,nuclearmagneticresonanceandspectroscopymethods, dewnholeseismicimaginginrealtime,andsophisticateddirectionaldrillingmethods. Thesenewtechniquesgreatlyaidthepetroleumgeoscientistsinunderstandingand developing reservoirs, and we will actively investigate how these methods are improving geologicalmodels.Despitethisprogress,subsurfacemodelsstillrelyheavilyon interpolation and extrapolation techniques. Our approach will consist in combining these techniqueswithaprioriknowledgeofgeohistoryandgeologicalprocesses.That knowledgecanbeobtainedfromrecentandancientsubcropandoutcropanaloguesand from forward and inverse modeling of geological processes. 2)Literature Review Once an accumulation of petroleum has been discovered it is essential to characterize the reservoir as accurately as possible in order to calculate the reserves and to determine the mosteffectivewayofrecoveringasmuchofthepetroleumaseconomicallypossible (LuciaandFogg,1990;Lakeetal.,1991;Worthington,1991;HaldersenandDamsleth, 1993). Reservoir characterization first involves the integrations of a vast amount of data from seismic surveys, from geophysical well logs, and fromgeological samples (fig. 1). Note that the data come in a hierarchy of scales, from the megascopic and mesoscopic to themicroscopic.Itisimportanttoappreciateboththescaleandthereliabilityofthe differentdatasets,e.g.theproblemsofreconcilingporosityandpermeabilitydatafrom logs and rock samples. The first aim of reservoir characterization is to producea geological model that honours theavailabledataandcanbeusedtopredictthedistributionofporosity,permeability, andfluidsthroughoutthefield(GeehanandPearce,1994).Reservoirspossessawide rangeofdegreesofgeometriccomplexity(fig.2).Therare,butideal,layer-cake reservoiristheeasiesttomodelandtopredictfrom,butreservoirsrangefromlayer-cakeviajigsawpuzzletolabyrinthinetypes.Geologistsapplytheirknowledgeto produceapredictivemodelforthelayer-cakemodelwithease,andthejigsawvariety withsomedifficulty.Butthelabyrinthinereservoircanonlybeeffectivelymodeled statistically. Pressure build up and draw down tests provide an opportunity to obtain estimates of the following well and reservoir properties: Permeability to the produced phase (oil, gas, or water), which is an average value with in the radius of investigation achieved in the test Skin factor, which is a quantitative measure of damage or stimulation in the wellCurrent average pressure in the drainage area of the tested well Verificationofflowbarriers(suchasfault)andestimatesofdistancetothese barriers Well bore effects dominate early test data. The end of the well bore effects is found using log-log plots of test data, which are compared to pre-plotted type curves, as illustrated in figure 3. The shapes of test data plots are also used to identify the reservoir type, such as homogeneousacting,naturallyfractured,layered,orhydraulicallyfractured.Derivative typecurves(basicallytheslopeofaplotofpressureversusthelogarithmoftime)are particularlyhelpfulforidentifyingreservoirtypeandwellboreeffects(W.JohnLee, 1992), as shown in figures 4 (a) and (b). Thelocationofaparticularreservoironthelayer-cake-jigsaw-labyrinthinespectrum decideswhetheritcanbemodeleddeterministicallyusinggeology,orprobabilistically using statistics, such as the stochastic and fractal methods (Weber and van Guens, 1991). Whichever approach is used, the objective is to produce a three dimensional grid of the field, and to place a value for the porosity, permeability, and petroleum saturation with in each cell of the grid (fig. 5). Once this has been done the reserves may be calculated and the most effective method of producingthemmaybesimulatedonacomputer.Computersimulationenablesthe production characteristics of the field to be tested for different well spacings, production rates,enhancesrecoveryschemesandsoforth.Asthefieldisdrilledupandgoeson stream an iterative process constantly updates and revises the reservoir grid, and enables progressively more accurate production scenarios to be tested. Oilandgasfieldsinparalicsuccessionscommonlycompriseastackedseriesof reservoirs (e.g. Verdier et al., 1980; Jev et al., 1993). Each reservoir may reflect a distinct depositional environment or a range of sub environments. Sequence Stratigraphy can help tounravelthiscomplexityby:a)elucidatingthegeometryofstratigraphictraps; b)Outliningsealarchitecture;c)refiningthechoiceofanaloguedataforinputinto stochastic reservoir models; and d) delineating flow units. Sandbodydimensionsvarywithsequencesratigraphicsettings.Thereforesequence stratigraphy is helpful in choosing the correct analogue sand body dimensions with which to populate stochastic models (Emery and Myers, 1996). Formodelingpurposesreservoirsarecommonlydividedintoflowunits,i.e.units whichhaveadistinctandinternallyconsistenteffectonfluidflow(Ebanks,1987).In manycasesflowunitsareboundedbysequencestratigraphicsurfaces.Forexample, intra-formationalsealsabovefloodingsurfacesmarkflowunitboundaries.Inaddition, the grain-size and facies changes that occur across sequence boundaries typically result in permeability contrasts and therefore, in flow unit boundaries (fig.6). Sequencestratigraphyisnowalsowidelyappliedtocarbonatesequencesasameanof identifyingunconformities,andthuspredictinghorizonsofsolutionporosityand dolomitization(LoucksandSarg,1993;Salleretal.,1994).Petroleumreservoirsin secondary dolomite are very complex, and it may be difficult to assess their reserves and to characterize the reservoir. In the Lisburne field at Prudhoe Bay, Alaska, for example, a limestonebearsthedolomitizationoverprintsoftwosuperimposedunconformities (Jameson, 1994). Improved characterization of petroleum reservoirs must include better geologic models in ordertopredictquantitativeattributesofreservoirunits.Bynecessity,petroleum reservoir prediction and modeling must make both interpolation and extrapolations from limited data. Several approaches to modeling of sedimentary rocks include descriptive or conceptual(qualitative)geologicmodelsandgeostatisticalsimulation(process),and visualization(quantitative)models.Eachtypeofmodelhasitsadvantagesand limitations,includingtheappropriatescaleofapplication,datarequirements,and knowledgeastohowthereservoirwasformed.Allofthemodelscomplimentone another, providing views of complex reservoirs from different perspectives. Quantitative modelingpotentiallycancreateamorecoherent,integratedviewofthereservoirthan qualitativeconceptualmodels.Anoptimummodelprobablyincludesacombinationof approachesbasedontheextentandtypeofknowledgeaboutthereservoir.Sequence Stratigraphicmodelingisapowerfultoolthatcanimproveourabilitytounderstandthe complexitiesofpetroleumreservoirs,andtherebyassistintherecoveryofoilandgas fromthosereservoirs.Typesofmodelscanbeclassifiedintoa)conceptualmodelsa geologic interpretation or construct; b) correlation models a manual interpretation of the spatialassociationofgeologicunits;c)interpolationmodelsamachinegenerated associationofspatialdata,thatisavisualization;d)forwardsimulationsmachine generationofgeologybasedoninputofprocesses;ande)inversemodelsmachine derivation of process parameters from geologic data (W.L. Watney, J.A. French, and W.J Guy, 1996). Conventionalapproachforinterpreting3Disin2Dor2.5Denvironment.Balanced cross-sectionsarecurrentlyusedinpetroleumindustrytohelpconstructandvalidate structural traps, to understand faciesrelationships, and to examine the relative timing of hydrocarbongeneration,migration,andtrapformation.Newvisualizationtechnology allows3-Dseismicinterpreterstorapidlyanalyzeenormousdatavolumes.Alongwith thistechnologycomenewtechniquesthattakeadvantageofthevisualization breakthroughs.Workstationandpaper-sectiontechniquesusedbefore3-Dvisualization requiredlaborious,time-consumingline-bylineinterpretation.Thisisnolongercost effective. True volume interpretation, which does not rely on the creation of maps and / or cross sections, is the next logical step to find more reserves with fewer wells. The four main techniques of 3D Multi-Attribute Geo-Volume Visualization Interpretation (GVI)arerecognition,color,motion,andisolation(Sheffield,etal.,2000).Recognition referstodeterminingthedistinguishingcharacteristicsofaneventtobemapped,then processingthedatatoenhancethosecharacteristicsforthepurposeofvisualizationand geo-body mapping. Color refers to the selection of optimum color scheme for visualizing thepropertyofinterest.MotionisoneofthemostcriticalaspectsofGVI;itismotion that taps the human subconscious and allows interpreter to see relationships between data in space and time. Isolation is the ability to separate the events of interest from other data, and is another key feature of GVI (Harvey et. al., 2000). TheseGeo-volumevisualizationandinterpretationin3Denvironmentwithmulti attributeanalysisenhancethestructuralboundariesaswellasinternalreservoir informationfor3Dseismicreflectiondata.Thefollowingattributesareusedfor structural and stratigraphic enhancement of data. InstantaneousPhaseenhancesthecontinuityofeventsbyignoringtheamplitude information in time samples. It is always a value between -180 and +180 and is primarily usedtovisualizestratigraphicrelationships(progradingreflections,onlaps,pinchouts etc.)onaregionalandlocalbasis.Insomecases,fluidcontactscanbeseenandphase reversalscanindicatewatercontacts.Instantaneousphaseandphasepicanbeusedto pickdiscontinuouslowamplitudeeventsandtoextendtheinterpretationofregional events into discontinuous areas (Harvey and Sheffield., 2000). Instantaneousamplitudemeasuresreflectionstrengthintime.Itisprimarilyusedto visualizeregionalcharacteristicssuchasstructure,sequenceboundaries,thickness,and lithologicalvariations.Insomecases,brightanddimspotscanbehydrocarbon indicators,andtuningcharacteristicscansometimesbeusedtoidentifyreservoirsona local basis (Harvey and Sheffield., 2000). InstantaneousFrequencyisrateofchangeoftheinstantaneousphasefromonetime sampletothenext.Itisprimarilyusedtovisualizeregionaldepositionalpatternsand sequenceboundaries.Insomecaseshighfrequencyabsorptioncancauseshadowzones beneath condensate and gas reservoirs, also frequency tuning can indicate changes in the thickness(possiblypinchouts).Spikesindicatenoiseand/orfractures.Instantaneous frequencycanbeusefulinrecognizingregionaleventsandmaybehelpfultoevaluate areas of interest for thickness variations (Harvey and Sheffield., 2000). Semblance is a measure of coherency for lateral changes in the seismic response caused by variation in structural events (faults, uplifts, subsidence and erosion), stratigraphic and sedimentary events (channels, onlaps, offlaps, and other lithological variations), porosity andthepresenceofhydrocarbons.Reflectiondiscontinuitiescanalsobecausedby seismicacquisition/processingandpoorimaging.Varioussimilartechniquesexistto bringoutthediscontinuityintheseismicdatavolumelike:semblance,continuity,and covariance. The cross-correlation techniques are also used to determine the local dip and azimuthattributes.Coherencyprocessingrequirescertainparameterstobespecified (e.g.timewindow,amountofdip,etc.)andthisshouldbecarefullycheckedtocontrol the quality of the output (cf Marsh et al.2005). Semblance is calculated over a vertical Window Length (in samples). Smaller window lengths generate sharper discontinuities, but also create more noise. Window lengths that are too long smear the discontinuities. It is recommended to start with a window length of12sampleson4msdata,andmodifyingthewindowlength,ifnecessary,insmall increments from the starting points. Using 3 traces is faster, and is often what we use for quickfirstlook,butthe9-tracecalculationismoreaccurateandispreferredfor interpretation purposes (Harvey and Sheffield., 2000). Linksarepossiblebetweentheinversionresultsandlithologiccrossplots.These crossplotssummarisetherelationshipbetweenvariouspetrophysicalandseismic parameters.Subsequent3Dclusteringprovidesameanstodopredictionofreservoir characteristicsinthestudieddataset(Guilbotetal.1996).Seismicattributesmaybe utilized to classify the seismic response in a certain interval. Instantaneous amplitude/square root (Frequency)is the attribute which is defined by dividing instantaneous amplitude to the square root of instantaneous frequency. It is sand toshaleindicatorforsiliciclasticenvironmentshighlightingthelowfrequency/high instantaneous amplitude events (Harvey and Sheffield., 2000). Conventionalmulti-attributemapping,likedensity,acousticandelasticimpedance, Poissons ratio, is certainly useful for lithology and fluid content identification (Walker et al.2005).Complexmulti-attributesormeta-attributescustomizedperstudycanbe computedtodiscriminatebettercertainreservoircharacteristicsinaN-dimensional space.ThePCAtechniqueisherebyofgreathelp.Theautomateddetectionof relationships hidden in the seismics is a non biased prediction tool, even more so because thecomputerisaveryrobustobserverofdetails(Veeken,2008).Multi-attribute autotrackingwillgraduallybecomemoremature(cfSternbach2002).Theamplitude coherencydisplayshowstheresolutionpowerofsuchdisplays(Figureno.7).The delineationofvoxsetsisthendoneonaroutinebasis.Thesevoxsetsareassembliesof voxelswithina3Dvolume,havingspecificmulti-attributecharacteristics.Theircareful selectionwillensureabetterdescriptionandrevealmoredetailsinindividualor composite geobodies i.e. flow units (Veeken, 2008). Lithologyinfluenceonamplitudescanoftenberecognizedbythepatternofamplitudes as observed on horizon slices and by understanding how different lithologies occur within adepositionalsystem.Byrelatinglithologiestodepositionalsystemsweoftenreferto these as lithofacies or facies. The link between amplitude characteristics and depositional patternsmakesiteasiertodistinguishlithofaciesvariationsandfluidchangesin amplitude maps. FigureNo.7.Multi-attributedisplaywiththecoherencyandamplitudecombined.It shows a meandering channel system in a time slice mode. Traditionalseismicfaciesinterpretationhasbeenpredominantlyqualitative,basedon seismic travel times. The traditional methodology consisted of purely visual inspection of geometricpatternsintheseismicreflections(e.g.,Mitchumetal.,1977;Weimerand Link,1991).Brownetal.(1981),byrecognizingburiedriverchannelsfromamplitude information,wereamongthefirsttointerpretdepositionalfaciesfrom3Dseismic amplitudes. More recent and increasingly quantitative work includes that of Ryseth et al. (1998)whousedacousticimpedanceinversionstoguidetheinterpretationofsand channels,andZengetal.(1996)whousedforwardmodellingtoimprovethe understanding of shallow marine facies from seismic amplitudes. Neri (1997) used neural networkstomapfaciesfromseismicpulseshape.Reliablequantitativelithofacies predictionfromseimicamplitudesdependsonestablishingalinkbetweenrockphysics properties and sedimentary facies. The subsurface is by nature a layered medium, where different lithologies or facies have beensuperimposedduringgeologicaldeposition.Seismicstratigraphicinterpretation seekstomapgeologicstratigraphyfromgeometricexpressionofseismicreflectionsin traveltimeandspace.Stratigraphicboundariescanbedefinedbydifferentlithologies (faciesboundaries)orbytime(timeboundaries).Theseoftencoincide,butnotalways. Exampleswherefaciesboundariesandtimeboundariesdonotcoincideareerosional surfacescuttingacrosslithostratigraphy,ortheprogradingfrontofadeltaalmost perpendicular to the lithologic surfaces with in the delta. Thereareseveralpitfallswheninterpretingstratigraphyfromtraveltimeinformation. First,theinterpretationisbasedonlayerboundariesorinterfaces,thatis,thecontrasts betweendifferentstrataorlayers,andnotthepropertiesofthelayersthemselves.Two layerswithdifferentlithologycanhavethesameseismicproperties;hence,a lithostratigraphic boundary may not be observed. Second, a seismic reflection may occur withoutalithologychange(e.g.,Hardage,1985).Forinstance,ahiatuswithno deposition within a shale interval cangive a strong seismic signature because the shales aboveandbelowthehiatushavedifferentcharacteristics.Similarly,amalgamatedsands can yield internal stratigraphy within sandy intervals, reflecting different texture of sands fromdifferentdepositionalevents.Third,seismicresolutioncanbeapitfallinseismic interpretation,especiallywheninterpretingstratigraphiconlapsordownlaps.Theseare essentialcharacteristicsinseismicinterpretation,astheycangiveinformationaboutthe coastaldevelopmentrelatedtorelativesealevelchanges(e.g.,Vailetal.,1977). However,pseudo-onlapscanoccurifthethicknessofindividuallayersreducesbeneath theseismicresolution.Thelayercanstillexist,eveniftheseismicexpressionyieldsan onlap. Quantitative interpretation of amplitudes can add information about stratigraphic patterns, and help us to avoid some pitfalls. First, relating lithology to seismic properties can help us to understand the nature of reflections, and improve the geologic understanding of the seismicstratigraphy.Gutierrez(2001)showedhowstratigraphyguidedrockphysics analysisofwelllogdataimprovedthesequencestratigraphicinterpretationofafluvial systeminColombiausingimpedanceinversionof3Dseismicdata.Conducting impedanceinversionoftheseismicdatawillgiveuslayerpropertiesfrominterface properties, and an impedance cross-section can reveal stratigraphic features not observed ontheoriginalseismicsection.Impedanceinversionhasthepoetentialtoguidethe stratigraphic interpretation, because it is less oscillatory than theoriginal seismic data, it is more readily correlated to well log data, and it tends to average out random noise, there byimprovingthedetectabilityoflaterallyweakreflections(Glucketal.,1997). Moreover, frequency-band-limited impedance inversion can improve on the stratigraphic resolution,andtheseismicinterpretationcanbesignificantlymodifiediftheinversion results are included in the interpretation procedure. Forward seismic modeling is also an excellent tool to study the seismic signatures of geologic stratigraphy. GriffithsandHadler-Jacobsen(1993)andNordlundandGriffiths(1993)discussthe derivation of input parameters for forward models from seismic observations. A range of input parameters for simulation and points to consider prior to simulation are as follows: Intial Topography/ Bathymetry Thisneedstobedeterminedviabackstrippingandmustbecarriedoutinthree dimensions.Evenifthemodelingistobecarriedoutintwodimensions,weneedto know the three dimensional surface in order to estimate sediment transport directions. Nature of Depositional Surface Whatisthenatureofdepositionalsurfaceloosesand,clayorcrystallinerock?The answer determines the degree to which clastic input is supplemented by erosion products. Climate: rainfall Therunoffvolumeandsedimentconcentrationcanbeinputeitherdirectlyorpredicted from a climate model. Climate: wave and storm magnitude and frequency River discharge is extremely irregular, and most sediments are transported and deposited /re-depositedduringbriefextremeevents,hencethestatisticsofsuchbehaviourareof interest to modelers. It will not be possible to predict exactly when a flood or storm event willhappen,butmuchcanbesaidabouttheprobabilityofitshappeninginagiven climaticregime.Suchinformationcanbebuilteitherdirectlyorindirectlyintoforward models (Koltermann and Gorelick, 1992) Currents Isthemodelinanareawheremarinecurrentsmighthaveredistributedsediment?Ifso what were their characteristics? Subsidence rates Isorwas,subsidencebeingdrivenbysedimentloading,orwasitafunctionofplate stressorthermalrecoveryhappeningindependentlyofsedimentload.Thesediment geometriesproducedareverydifferentinthetwocasesassubsidenceratesmaybe different in different tectonic settings. Sea-Level Sea level is one of the most controversial variables, ever discussed. The simulation may usetheHaqetal.(1987)sealevelcurves,oralocalrelativesea-level(RSL)curve.If manysuchcurvesareavailablearoundabasinthenaregionalcurvemaybeextracted. One practical decision that has to be made in computer modeling is how to treat the RSL curve.Oneoptionistotreatitasthesumofallchangestoaccommodationspace, including global sea level, tectonic subsidence, compaction, thermal subsidence, etc. This maybeacceptableinaonedimensionalsimulationbutnotintwoorthreedimensions. Thethreedimensionalshapeofaccommodationspacecreationvarieswiththedifferent causes. An attempt should be made to break out the component parts. Sediment Supply Sedimentsupplycanbetreatedeitherasafunctionofclimaticprocess,orasan observation.Measurementofthevolumeofsedimentinputtothebasinduringagiven time period can be made. Integrating the sediment supply rate over this time period must give the observed sediment volume after decompaction. The sediment supply curve with in the simulated period can take a number of forms, such as constant, cyclic, or matched to chromosome areas. Post-depositional processes Inordertomatchthemodeledgeometriestoobservationstherehavetobesomepost-depositionalprocesses,suchascompaction,burial,faulting,etc.Thetimingofthese processes,inrelationtothedepositionaleventisoftencritical.Present-daysteadystate compaction concepts are not adequate when used in conjunction with forward modeling. Therateofcompactionlargelydeterminesthesuccessivelocationofdeltalobesand shale drapes, for example. Someworkersarenowalsointegratingstructuralandsedimentologicalattributeswithin basinformationmodels.Whichhasgivenrisetoquantitativedynamicsequence stratigraphy(QDS)inwhichquantitativetechniquesarebeingusedtoanalyzethe geodynamic,stratigraphic,lithostatic,diageneticandhydraulicattributesofsedimentary basins,treatingthemasfeaturesproducedbytheinteractionofdynamicprocesses operating at specific time and places. One of the characteristic features of fluvial systems is the presence of sinuous abandoned sand-filledchannelsembeddedinthebackgroundoffloodplainshale,whichis characterizedbyverylowpermeability/porositymaterial.Influvialreservoirs, hydrocarbonreservesaremostlycontainedinanumberofdistributedsandbodies isolatedbyfaultsorpartiallyconnectedtooneanotherviagoodpermeability/porosity materialbuttinysizepathway.Thisposesspecialchallengesforgeologicalmodeling becausetheexistenceofsuchconnectedpathwayandbarriersbetweenisolatedfluvial bodieshasgreatinfluenceonfluidtransportandthusproductionprofiles.Detailed knowledgeofsandchannelgeometry,spatialdistribution,andconnectednessare essentialtodevelopamodelthataccuratelydescribesfluidflow,reliablypredictthe futureperformance,andhelpindecisionmakingmanagementinfluvialsystem(V.Q. Phan, 2000). Theresponseofafluvialreservoirdependsonspatialdistributionofrockproperty (permeability/porosity),which,inturn,canbedeterminedbythesetofparameters describing channels. The procedure of computing such set of parameters from production andseismicdatausuallybeginswithaninversetechniquebecausethedataarenon-linearlyrelatedwithreservoirrockproperty,andthuschannelparameters,througha systemofmassbalanceequations.Duetothecomplexityoftheproblem,thereservoir responsemustbecomputednumericallymakinguseofanumericalsimulator(V.Q. Phan, 2000). Therecognitionoftheimportanceofsand/shaleheterogeneityinthefluvialreservoirs dates back to at least 1978 by Allen, L. Jr. One characteristic feature of fluvial reservoirs istheexistenceofsand-filledchannelcomplexesembeddedinthebackgroundoflow permeability/porosityshale.Severalresearchershaveaddressedtheproblemof characterizationoffluvialreservoirusinggeologicalintuition(twodecadesago)to simple interpretation of well log data and just recently to integration of data from various sources(geological,geophysical,petrophysical,andevenproductionhistory).The traditionalmethodsfordescribingcomplexreservoirgeologysuchascontouring parametersandhand-mademodelsarenotabletoadequatelyrepresentthe heterogeneitiesand,therefore,tocapturemostoftheaspectsthatimpactonfluid displacement. The Hand-Made models proposed by Johnson and Krol in 1984 rely on the geologicalinterpretationofwell-logdata.Thewelldataprovidedlocationswherethe channels certainly passing through, but the channels are positioned arbitrarily in the inter-wellareasassandstonesareinsufficientlylaterallyextensivetobecorrelatedbetween wells (V. Q. Phan, 2000). Alternativemethodsarerandom-objectdistributionofthesandbodiesor sedimentologicalprocessrelatedmodels.Thesemethodsmadeuseofstochastic simulations to describe the complex geological architecture honored to the well data and conditionedtotheprobabilitydistributionsobtainedfromoutcroporotherstudiesof analogdepositionalenvironments.Thisstochastictechniquewasfirstcreditedto Haldorsen and Lake in 1984 for development. Henriquez et al. applied the method to first buildastochasticmodelconditioningtosand-bodythicknessandpositioninwellbores andthenthegeologicalmodelwastransferredtoareservoirsimulationgridbyuseof transmissibilitymultipliersandanNGRvalueforeachblock.Theauthorsreportedthat thetransferofdatasmoothesoutmuchofthedetailedgeologicalinformation,andthe calculatedrecoveryfactorsareinsensitivetothecontinuitymeasuredinthegeological model and therefore proposed an improvement to the interface between geological model and reservoir simulation model (V.Q Phan, 2000). Smith and Morgan developed conceptual and stochastic models for both faulted reservoir sandsandfluvialsands.Thesemodelsenabledthecalculationsofthepermeability reductionacrossfaultsectionsandtherecoveryfactorsinfluvialreservoirsthatcanbe used in aconventional numerical simulator. Stochastic simulation is an excellent tool to integratestaticdatafromvarioussources(welllog,seismic,etc.).Itcanbeusedto describe/generatethecomplexgeologicalarchitectureandthespatialdistributionsof facies, sand-bodies, and the rock properties within each compartmented unit (V. Q. Phan, 2000). Although these simulated scenarios are conditioned to hard data at the well locations and subject to secondary constraints, the simulated models still incur large uncertainty in the inter-well areas and predictions of reservoir performance. There are a number of recently developedtechniquesforcharacterizinginter-wellheterogeneitiesthatwerereportedto beefficientandusefultostudythesensitivityofproductionperformancetothe uncertaintyingeologicalmodeling.Beggetetal.developedanew,quantitative,object-basedmodelofthedistributionoflithotypeswithineachmajorfaciesandconverted these models to spatial distributions of porosity/permeability and these are then up scaled foruseinflowsimulation.Theauthorsreportedthatthequantitativelithotypemodels contain a number of variable parameters which enable sensitivities to uncertainties in the geological description to be studied (V.Q. Phan, 2000). Yet,someotherresearchershavelatelyproposedasystematicaltechniquethatallows describingthegeologicalfeaturesinfluvialsystemevenmoreefficientandrealistic. AmongtheseareDeutschandWang.Theirtechniqueisbasedonahierarchicalsetof coordinatetransformationsinvolvingrelativestratigraphiccoordinates,translations, rotationsandstraighteningfunctions.Allmethodsmentionedsofarmadeuseofstatic information(timeindependentdata).Forreservoirscharacterizedbysimple configurationsorinsomepurposes,themodelsobtainedbythesemethodsprovidea goodreservoirdescriptionandareadequatelyreliableforuseinpredictingfuture reservoir performance. But other cases, particularly in most fluvial systems, often require amorereliablemodelthemodelthathonorsmostavailableinformation(V.Q.Phan, 2000).

Theintroductionofdynamicdata(timedependentdata)intheproblemof characterizationoffluvialreservoirsisusefulintermsofobtainingdetailedmodel configurationsandsignificantlyreducingtheuncertaintyinmodelingandinreservoir predictions.Branaganetal.performedathree-wellinterferencetestingofanaturally fractured,tightfluvialreservoirlocatednearRifle,Coloradoinincorporationwith geologicandgeophysicaldatainaseriesofsimulationsusinganaturallyfractured reservoirmodel.Themodelparameterswerethensystematicallyalteredinorderto provide the best history match of the well test data (V.Q. Phan, 2000). Theworkconcludedthatthereservoirsimulationhistorymatchingofwelltestand interferencedatafromthethreewellsproducedaverydetailedmodelofthefluvialEI reservoir.Themodelprovidedsignificantinsightintothecomplexproduction characteristicsofthistight,anisotropicnaturallyfracturedreservoir.Lordetal.have demonstratedthatthehistorymatchingofwellperformancewithasimple multicompartmentmaterialbalancemodelprovidesexcellentestimatesfordirectly drained pore volumes and inter-compartment transmissibilities in fluvial reservoirs. They alsoreportedthatasuccessfulhistorymatchfortheinter-channelmodelwasachieved usingonlyratehistoryandsinglepressureobservationattheendofproduction. However, the limitations of the compartment model showed in case of dual permeability reservoir where the real pressure is not uniformly distributed as in a tank, and therefore, is not accurately described by compartmental models (V.Q. Phan, 2000). StewartandWhaballahaveshownthatamaterialbalancesimulatorcanbeusedwith pressurehistoriesfromwelltestsincompartmentedoilreservoirstoidentifygeological configurations.HowerandCollinscompartmentalizedafluvialgasreservoirintotwo compartmentscoupledbyalowpermeabilitybarrierandalsointroducedatwo compartment material balance model. A further study by Lord and Collins extended this approachtoanynumberofpermeablecompartmentswithcommunicationbetween compartment pairs through low permeability barriers. This numerical method provided a two-way of history matching allowing the users to match pressure history specifying rate data or to match rate history specifying pressure data (V.Q. Phan, 2000). In1996,Zhengetal.studiedtheimpactofvariableformationthicknessonpressure transientbehaviorandwelltestpermeabilityinfluvialmeanderloopreservoirsusinga commercialsimulator.Themodelingofthemeanderloopreservoirwasbasedona simplelinear-channelmodelwithparaboliccross-sectionalprofile.Anumericaland analyticalrepresentationofchannelcomplexesinfluvialreservoirshasbeenstudiedfor severaldecadesbuttheuseofnumericalsimulatorstodynamicallyincorporatefluvial channelsintotheinterpretationofseismicandproductiondatahasnotyetbecome common in a complete or systematical process (V.Q. Phan, 2000). Earlypaperswereconcernedmorewithnumericalthangeologicalconsiderations. Missing scale betweengeological and simulation models results in a loss of information whichcouldenhancethedifficultyinintergratingwelltestand/orproductiondata. Sometimesinpractice,thesameproblemcouldalsoariseduetoanothermissingscale betweena simulation model used for long termforecast andan inversion model used in thehistorymatchingprocess.MassonnatandBandiziolreviewtheinterdependence betweengeologyandwelltestinterpretation.Theauthorsinvestigatedtheeffectsthe geologicalmodelhasuponthewelltestinterpretation.Theauthorsinvestigatedthe effectsthegeologicalmodelhasuponthewelltestresponseandhowthewelltestsare used to confirm a geological model through several field examples (V.Q. Phan, 2000). Time-lapseseismicdatamonitoringisusefultodocumentthechangeinseismic response due to production and injection in the wells (cf Oldenziel 2003). The production ofhydrocarbonschangestheacousticimpedancecontrast.Itresultsinatimeand amplitudedifferenceinandbelowthereservoirsequence.Thedifferencesaredueto changeinpressureandwatersaturationforthereservoirrocks.Sweptareasare conveniently visualized and bypassed zones are easily recognized. The flow pattern in the reservoir is better resolved. The prediction of water breakthrough is feasible and a better estimation of the well production figures is obtained (Veeken, 2008). Thefracturedensitycanbeassessedbyanisotropicbehaviouroftheseismicvelocities (Thomsen parameters). For this purpose the degree of non-hyperbolic Move Out (residual moveout)isdetermined.Geostatisticaldecompositioncanbeusefultoquantifythe anisotropyeffectinthedatagatherswithseparationofacommonpartinallazimuth gathers,theanisotropicsignalandthenoise(Coleouetal.2002).Thefractureintensity mapformsinputforDiscreteFractureNetworkmodelingtoderivevariousflow propertieslikefracturepermeabilitytensors,matrix-fractureinteractionparameters, which can be used as input for flow simulators (Wong and Boerner 2004). Recent work on fault systems has emphasized the importance of the segmented nature of fault geometries in matters of fault evolution (Peacock and Sanderson, 1994; Trudgill and Cartwright, 1994; Cartwright et al. 1995; Dawers and Anders, 1995; Childs et al., 1996; Marchaletal.,1998),sedimentarybasindevelopment(AndersandSchlische,1994; DawersandUnderhill,2000),geothermalfluidmigration(Coussementetal.,1994; Martinez,1998),andseismologicalbehavior(CroneandHaller,1991;dePoloetal., 1991; Machette et al., 1991; Wells and Coppersmith, 1994). A widely recognized impact of fault systems is with regard to fault controlled hydrocarbon traps in oil and gas fields. Wellplacementandrecoveryeffortsinmanyoilandgasfieldshavebenefited significantly from highly detailed characterization of segmented fault systems (Bouvier et al.,1989;Morleyetal.,1990;PegrumandSpencer,1999;Knipeetal.,1998;Ottesen Ellevestetal.,1998;Maerten,1999).Breaksinfaultcontinuityprovidepotentialflow zonesthroughwhichhydrocarboncanmigrateacrossafaultedregion,therefore,a thorough methodology for the analysis of segmented fault systems is needed to enhance faultinterpretationsandthusrecognizepotentialwaterbreakthroughsandhydrocarbon escape points with in fault compartmentalized reservoirs (Kattenhorn, S.A., and Pollard, D.D, 2001). Although distinct fault segments may be clearly visible at the surface of the Earth, some largefaultsystemshaveevolvedtoapointwhereevidenceofinitialsegmentationhas beeneradicatedthroughfaultsegmentslinkingtogether,allowingtheaccumulationof largeamountsofslipoveraresultantcompositefaultsurface(Wesnousky,1988; PeacockandSanderson,1991).SlipprofilesalongfaulttracesattheEarthssurface commonlyexhibitheterogeneitiesinslipdistributions(CartwrightandMansfield,1998; Morley,1999)thatmayimplyarelictsegmentednature.Thisprocessofinitial segmentationandsubsequentlinkageischaracteristicoffaultsystemevolution (Cartwrightetal.,1995;DawersandAnders,1995)andmaybeassociatedwith geometricirregularitiesalongfaultstrikeatthepointsoflinkage(Peacockand Sanderson, 1994). FaulttracesattheEarthssurfaceprovidedlimitedinformationonthe2-Devolutionof faultswheretheyintersectahorizontalplanebutcannotbeusedtoelucidatethethree dimensional (3-D) evolution of the fault system. The 3-D characteristics of fault systems thatcanbedeterminedfrom3-Dseismicreflectiondata(Childsetal.,1995;Mansfield andCartwright,1996;OttesenEllevsetetal.,1998;Yieldingetal.,1999;Dawersand Underhill,2000)arecrucialforaccuratereservoircharacterizationwherefaultsactas barrierstohydrocarbonmigrationandmaythuspotentiallycompartmentalizethe reservoir.Insightsinto3-Dfaultgeometriesarealsoimportantforformulating mechanical models that examine fault geometries, tip-line shapes, slip distributions, fault scalinglaws,andmechanicalinteractioneffectswithinsegmentedfaultsystems (Kattenhorn, S. A., and Pollard, D.D, 2001). Withinanyfaultedreservoirtherearelargenumbersoffaultsthatarebelowthe resolutionofseismicsurveys.Someofthesefaultsareencounteredinwells,butvast majorityofthemremainundetected.Suchsub-seismicfaultscansignificantlyinfluence theflowofhydrocarbonsduringproduction.Theeffectoffaultingontransmissibility with in the reservoir is evidenced by high-pressure differentials across faults with in the oilandgasfields(SmithandHogg,1997).Suchdifferencesinpressuresometimes indicate fault sealing effects, which may be related to clay smearing along the faults (R. Knipe, 1994). Faultinterpretationsfromonly3-Dseismicdata,however,arelimitedbyinterpretation subjectivity,structuralcomplexity,processingartifacts,seismicresolution,and insufficientuseofprincipleoffracturemechanicsthatcanaidtheinterpretation.This mayresultinerroneousfaultinterpretationsthatincorporateunrealisticfaultgeometries andoverlookimportantgeometricfeaturessuchassegmentationorfaultlinkagezones. Thesize-distributionofsub-seismicfaultscanbepredictedbyextrapolatingthesize distributionmeasuredattheseismicscaledowntothesub-seismicscale.However,the positionsandorientationofthesub-seismicfaultsaremoredifficulttodetermine.For sub-seismicfaultmodelingatreservoirscaleanewmethodisdevelopedbyStanford UniversityTeamforRockFractureStudies,whichwillbediscussedinourresearch methodology section. Naturallyfracturedandfaultedsystemscanhaveadramaticimpactonreservoir performance they may act both as highly permeable flow conduits or baffles and seals. The complexity of a fracture network typically leads to an extremely heterogeneous and anisotropicpermeabilitydistributionwithinthereservoir.Successfulmanagementof thesereservoirsisimpossiblewithoutsubstantialknowledgeofthenaturaltensileand shearfracturesystems.Itisessentialtoknowtheirspatialdistributionandhydraulic propertiesonaninter-wellscaletoproperlysimulatethefield-widerecoveryprocesses (BOURNE, S. J., RIJKELS, L., STEPHENSON, B.J., AND WILLEMSE, E.J.M., 2001). Itisneitherpossiblenorgenerallynecessarytoaccuratelypredictindividualfractures within a reservoir. Rather we should restrict our attention to predicting just the properties of those tensile and shear fracture networks that are hydraulically conductive. We should calculatethestressfieldresponsibleforreservoirfracturingusinggeomechanics.Brittle fractures form where this stress field exceeds the local material strength as characterized bythebrittlefailureenvelopeforbothtensileandshearfractures(BOURNE,S.J., RIJKELS L., STEPHENSON, B.J., AND WILLEMSE, E.J.M., 2001). AworkflowofanewmethodbyBournein2001forpredictingnaturalfracture distributions and their effect on reservoir simulations is shown in the figure no.8. Figureno.8.Integratedmodelfornaturallyfracturedreservoirsbasedon:(i) geomechanical models of rock deformation, (ii) fracture mechanics, and (iii) multi-phase flow simulation. This work flow incorporates all the available static and dynamic data in ordertoconstrainthemodelandminimizeuncertaintyinfracturepredictionandflow forecasting. In this method the first step uses geo-mechanical models of rock deformation to calculate thefieldscaledistributionofstressresponsibleforfracturingfromobservedstructural GeometryStressFracturesFlow iii Data Sources Seismic Well logs Outcrop Constraints Material Properties Regional Stress Constraints Matrix Properties Fluid Properties Model Validation Core & BHI Outcrop Model Validation Well test Production history MaficOil MoReS Poly 3D DIANA iii geometryofthefield.Fracturenetworkgeometriesarethenobtainedbysimulatingthe initiation,growth,andterminationoffracturewithinthecalculatedstressfield.These predictednetworkgeometriesarepartiallyconstrainedandvalidatedbycore,borehole image, mud loss, and outcrop data. Thereafter, multi-phase, well scale or field scale flow simulationsofthefracturemodelarevalidatedandcalibratedagainstwelltestand productiondata(BOURNE,S.J.,RIJKELS,L.,STEPHENSON,B.J.,AND WILLEMSE, E.J.M., 2001). Close integration of fracture prediction and flow simulation enables significant reductions in uncertainty by using all the available static and flow data to constrain a single model. In this way, for instance, standard ambiguities in borehole fracture data due to sampling bias can be overcome by the use of well inflow data. Moreover, as the fracture model is field-scale,thegreaterthenumberofwellsavailablethesmallertheuncertaintyin fracturepredictionbecomesacrossthewholefieldandnotjustaroundthewells.Such reductioninuncertaintyallowsimprovedfielddevelopmentthrough:(i)better assessment of therecoverymechanism, (ii) morereliable production forecasts, (iii) well placementforoptimaldrainage,(iv)minimalwater-cutand(v)recognitionofdrilling hazardsassociatedwithfractures(BOURNE,S.J.,RIJKELS,L.,STEPHENSON,B.J., AND WILLEMSE, E.J.M., 2001). To characterize reservoirs at pore levels, we should have enough information about CO2 reactionsinsiliciclasticreservoirs,effectofprovenanceonreservoirquality,porosity depthtrends,timingofillitegrowthanduseofimageanalysistodefinereservoir heterogeneity (Kaldi and Tingate, 2003). Reservoirsandstoneexhibitporosityenhancementbydissolutionofcalciteandquartz overgrowthcement,moreover,presenceofchloriteinhibitquartzovergrowth cementation, while, ductile clays not only reduce the pore and pore throat sizes, but also cancauseseveredamageduringproductionanddevelopmentbyretainingwateror collectingfines.Oxidationthroughinvasionofmeteoricwatermodifiestheclaysand cementsmicro-poreswithgoethite.Glauconycementedsandstones;showtheeffectof swellingclaysonthedistributionofporesandporethroats,thesetypesofexpandable clayscancauseseveredamagetoreservoirporosityandgreatlyreducepermeability. Illite replaces grains but illite and kaolinite together fill porosity, fibrous and bladed illite greatlyincreasessurfaceareaandirreduciblewater,anditcanalsoactasafilterto collectmudandfines,thereforeclaydistributionpatternsgreatlyinfluencepermeability (Kaldi and Tingate, 2003). Table1summarizespotentialrockfluidreactionsbasedonknowledgeofclayspresent, damageprevention,andcorrectiveprocedures(Kersey,1986).Preventionispreferred and, when possible, is likely to cost less than correction. 3)Objectives of the study This research work aims to achieve better quantitative dynamic 3-D geological models of thesedimentaryarchitectureofthesubsurfaceonreservoirscalebeyondseismic resolution.Thegeologicalmodelingtechniquesdevelopedinconsequenceofthis researchworkwillenableustodistinguishflowunitsandtoquantifythegeometryand connectivity of the subsurface as precisely as possible. The goal of reservoir-scale research is (1) to assess and reduce uncertainties in reservoir modeling, (2) to predict production behavior of hydrocarbon reservoirs, and (3) to locate by-passedhydrocarbonandincreasetheefficiencyofhydrocarbonproduction.In harmony with the last objective, specific production-geological research approaches will be included in the programme. The purpose of this study is to establish the key concepts andmethodologiesrequiredtodevelopandbuildcomplexreservoirmodels.Specific emphasiswillbegiventotheintegrationofdataarisingfromdifferentoriginsandata variety of scales (geology, seismics, well logging, cores, PVT, production, etc.). The use ofdataanalysis,geostatisticsandupscalingtechniqueswillbemadewithreferenceto case studies. The study aims to develop a common language and a shared understanding ofconceptsbetweenthevariousdisciplines,therebypromotingcollaborationandteam workingwithwellknownresearchersandprofessionalsfrombothacademicinstitutions and the oil industry. 4)Significance of the study A multi-disciplinary approach is important, enabling an accurate evaluation to be made of reservoirgeometry,internalstructuresandheterogeneities,aswellastheirimpacton fluid flow. The process is critical during all phases in the life of a field, as it determines thesizeofreserves,reservoirproducingmechanisms,fielddevelopmentstrategyand costs.Therefore,reservoircharacterizationandmodelingisvitalforoptimum exploitation of reserves and utilization of company financial resources. Theneedforanaccuratecharacterizationofthereservoirs(e.g.bybetterunderstanding of reservoir connectivity or accurate modeling of fracture networks) is a key to optimize the reservoir management plans and then to reduce the development costs by: Better control on the well planning (number, location and trajectory) Betterdecisionsonthedevelopmentstrategy(reservesestimation,recovery process, drilling schedule, etc.) Helpinginrelatingfundamentalunderstandingofreservoirpropertiesto improvements in business practice and decision making processes throughout the exploration and production process of oil and gas fields.Beingawareoftherangeofuncertaintiesintheprocessofexplorationand production and their impact on business decisions. 5)Research Planning & Schedule Thesearchfornewhydrocarbonandwaterreservoirsandtheneedtoenhancerecovery fromexistingreservoirsrequireconcertedactionofgeologists,geophysicistsand engineers.Theroleofthegeologistsinthisistoimprovethepredictivepoweroftheir models.Theobjectiveofthisresearchprogrammeistoprovideaspatialdescriptionof petrophysicalpropertiesinheterogeneousreservoirs.Itisdonebyintegratinggeology (geologicalrulesandexperience),geophysics,petrophysics,reservoirandproduction engineering.Theresearchprogrammeaimstoderivestaticproperties(porosityand permeability)inwellsandinter-wellregionsatlogscale,oratgrid-blockscale.When coupledwithdynamicpropertiesatgrid-blockscale,theresultisareliablesimulation model which can be used to improve performance prediction in relatively new fields and to rejuvenate old fields by locating by-passed and undrained hydrocarbons for improved oil&gasrecovery.Thisstudywillfocusonfollowingmajorresearchphaseseachof which may take 3 to 4 months in its completion: a)Introductoryfieldsessiontofamiliarizewitharangeofreservoirfacies; structuralset-up;relationshipbetweenreservoirfaciesandtheirrespective sedimentary environments; and the impact of such environments on petrophysical properties. b)Filedworkonoutcrops,whichhavebeenthesubjectofextensivereservoir analoguestudies.Examinationoftherelationshipbetweenreservoirfacies, internal structures and heterogeneities at different scales. c)Descriptionandmappingofreservoirstructuresandheterogeneitiesand assessment of their impact on fluid flow and reservoir development strategy under various reservoir configurations, fluid types and distribution patterns. d)Seismic interpretation of key surfaces required for reservoir characterization and modeling.Afterloadingofalltheavailableseismicdatatoaworkstation,all available ZVSP, VSI, Check shot and or any other velocity data will be used to tie thestudywellstotheseismicdata.Thechronostratigraphicandsequence stratigraphicframeworkestablishedfrombiostratigraphic,isotopicand sedimentologicalanalysisandinterpretationwillbeusedtopickkeyhorizons. AfterdatasynthesisanditsintegrationintoauserfriendlyGISdatabasewecan getbenefitfrom3DBasinmodelingbasedoncomplexrealcasestudiesand DiffusionOrientedNormalandInverseSimulationofSedimentationfor3D multi-lithological stratigraphic modeling to understand Basin architecture and for subsequentreservoircharacterizationandmodeling.TectonoStratigraphic surfaceswillalsobemappedregionallytounderstanduplifting,erosion,and compactiontoassistrestorationofpaleo-topography.Thiswillthenbeusedto establishgeometryofthereservoirhorizonsandsealintervalsbyforwardand inversemodeling.Faultdistributionmapswillalsobepreparedtogetherwithan understanding of fault movement through time and fault seal analysis will also be carried out. e)Knowledge-ChargedReservoirModelling:when wells are fewand sparse, the statisticalinterpretationofthewelldataisunlikelytoresultinarepresentative pictureofthereservoir.Wewillovercomethisproblembydeveloping knowledge-chargedreservoirmodelsbasedongeologicalrulesandprevious experience.Themosttangibleformsofgeologicalknowledgearehand-drawn geologicalmapsandcrosssections.Thesemapsandcrosssectionsindicatethe complexityofstructural,sedimentaryanddiageneticpatterns.Wewillusesoft computingandgeostatisticstoextracthigher-orderinformationhiddeninsuch maps. In this regard we will also use restoration softwares that enable the restoration for balancedcrosssectionsdirectlytakenfromthestructuralmodelorthe interpretation,seismicimage,andmaps.Therestorationwillinitiallycarriedout in 2.5D and constitutes a major step during the validation of seismic interpretation and structural modeling processes. For this purpose the restoration process should befastandcanbeperformedonasinglehorizonormapaswellasmultiple horizonsormaps.Thefunctionalitiesofthismoduleshouldbeembeddedintoa workflow that reduces the learning curve, and enables reporting and repeatability. Wewillalsodo3Drestorationusingfull3Dsolidmodelingcapabilitiesanda finiteelementrestorationengine.Throughanintuitiveworkflowthis3D restorationshouldhelptocompute3Ddeformationparametersandrefinethe geometryatthebasinscalelevelaswellasreservoirscale.Forbasinscale,this should allow the computation of the paleo-geometry that leads to the computation of the source rock burial as well as the geometry of carrier beds and of structures. Atthereservoirscale,thisshouldcomputethedeformationparametersthatlead to the prediction of fractures density and directions. We will also incorporate this information with conventional data (log-derived properties and seismic attributes) to improve stochastic modeling practices. f)Lithofacies Recognition from Well Logs: Lithofacies constitute the fundamental buildingblocksofareservoirmodel.Theyprovideanindicationofreservoir qualityandimprovewell-to-wellcorrelationoflitho-hydraulicunits.Thisphase willuseconventionalandmodernwelllogstoidentifylithofaciesandtheir verticalordering.Wewilldothisbyimprovingourunderstandingofelectro-facies and regional geology through the use of advanced classification algorithms in soft computing (neuro-computing, fuzzy systems, evolutionarycomputing and probabilisticreasoning).Thesetechnologieshaveshowngreatpromiseinmany formation evaluation studies. g)Predicting Reservoir Quality: This phase aims to predict flow-related properties (porosityandpermeability)fromwelllogsthroughtheuseofsoftcomputing.It providespracticalsolutionstomanycore-logintegrationproblems.Wewilluse advanceddatasamplingstrategiesandneuralarchitecturetoproducemultiple predictionsoradistributionofpredictions,ratherthanasingledeterministic prediction.Theaccuracyandprecisionofthepredictionsmaybeusedto determinethepredictionconfidence.Thiswillparticularlyusefulforquantifying reservoirs with rapid facies changes and thin beddings. Four example images are shown below: 2D Lithofacies Model 2D Porosity Model 3D Porosity Model 3D Porosity Model h) FormalizingGeologicalKnowledge:Theobjectiveofthisphaseistodevelop scenario-based geological models in uncertain depositional environment. We will considermultipleinterpretationsderivedfromgeologicalknowledge (perceptions), rather than the more commonly used equally-probable realizations. Thelattereffectivelyresultfromanalyzingthesensitivityofasingle interpretation. In contrast, we will use granular computing to formalize geological perceptionsandmodelimprecise,qualitativeandlinguisticgeologicalevents. Thesecanbeusedtosimulatescenario-basedreservoirarchitecture.Thisallows us to assess the true uncertainty of reservoir modeling beyond the observed data. i)DevelopmentofTeam-workinthereservoir-geologicalandpetrophyscical modelingusingdeterministicandstochasticmethodswithintegrationofseismic and dynamic constraints by identification of key heterogeneities, quantification of uncertaintiesandup-scalingofpetrophysicalmodelstoappropriatefluidflow models for specific reservoir simulations. j)4Dseismictimelapsemonitoringandproductionhistorymatchingtoremove the constraints of static reservoir models and the dynamic flow simulations. 6)Research Methodology We will integrate information from four complementary data sources: 1.Subsurfacedata:deep3Dseismicreflectiondata,cores,welllogs,datafrom production history, data from borehole imaging, nuclear magnetic resonance and spectroscopicmethods,realtimedownholeseismicimages,andsubsurface information come from sophisticated directionaldrilling methods in combination with a priori knowledge of geo-history and geological processes obtained from recent&ancientsubcropandoutcropanaloguesandfromforwardandinverse modeling of geological processes. 2.Recent&ancientsubcropandoutcropanalogues:withdetailedvertical information by ground penetrating radar and shallow seismics. 3.Complete reservoir analogues: provided by including field petrophysics, deep and shallow seismics, ground penetrating radar, and borehole data in the outcrop and subcrop analogues with excellent information on the vertical and lateral extent of sediment bodies, which ideally suit to match core data with sequence stratigraphy of rock sequences. 4.Process-responsemodelingofsedimentarysystemsaimstosimulatereservoir architecturebydevelopingsuitablemathematicalformulationsfortheneteffects oferosion,transportanddepositionofsediments,asafunctionofinitial topography,tectonics,climateandsealevel,onspatialandtemporalscales relevant to the formation of hydrocarbon and groundwater reservoirs. Moststandardgeologicalmodelsofsedimentaryenvironmentsarederivedfromrecent analogues. Examples include tidal deposits from the Wadden Sea, fluvio-deltaic deposits fromtheMississippiandtheBrahmaputra,deepwaterturbiditesystemsandsubmarine fansfromAmazonandMississippi,andeoliandepositsfromtheSaudi-Arabiandesert. Suchrecentanaloguesgivegoodhorizontal(Plan-view)2-Dgeometry,butlimited vertical information. Weaimtoovercometheselimitationswithground-penetratingradarandshallow seismics.Anotherlimitationisthatmostpresent-daycoastalandshallowmarine environmentsarehighstandfeatures,andthatitisdifficulttoassesstheirpreservation potential.Furthermore,thelargerandmorecomplexthesystems,themoreuniquethey are,andthemoredifficultitbecomestoextractfeaturestheyhaveincommonwith subsurface examples. Geological remote-sensingresearch is particularly useful to obtain abetterunderstandingofthesurfaceexpressionofhydrocarbonreservoirsandto integrate surface and subsurface data. This refers not only to spatial information, but also tospectralinformation.Hyper-spectralimagingisanimportantcomponentinthis respect.Outcropanaloguescangiveexcellentinformationontheverticalandlateral extentofsedimentarybodies,andareideallysuitedtomatchcoredatawithsequence stratigraphyofrocksequences.Outcropsusuallygivelimitedhorizontal(plan-view) informationandmaynotalwaysreflecttheproperpreservationpotentialofarock sequence. By using a variety of datasets, including satellite imagery, aerial photographs, surficialsedimentmapping,coringandaugering,andC14andOSL/TLdating,2Dand 3Dgeometryandstratigraphicarchitectureofreservoirandsealelementscanbe determined. By carefully selecting the types of outcrops studied, and the methods employed to do so, we will obtain considerable insights into their reservoir characteristics. We will do this by includingfieldpetrophysics,shallowseismics,ground-penetratingradar,andborehole data in the outcrop study itself to provide complete reservoir analogues. Best-fitstratigraphiclayeringandcompartmentalizationofreservoirswillbegenerated using3DSeismicMulti-AttributeGeo-VolumeVisualizationInterpretation(GVI) techniques. 3D Seismic cube with the combination of inversion, lithologic crossplot cut-offsandcoherencywillbeestablishedtovisualizetherelationshipbetweenvarious petrophysicalandseismicparameters.4Dseismictimelapsemonitoring andproduction historymatchingwillbecarriedouttoremovetheconstraintsofstaticreservoirmodels and the dynamic flow simulations Channel parameterization Onecriticalissuetobeaddressedinfindingtheinverseresultsbyintegratingdynamic datainfluvialsystemsisthedevelopmentofmathematicalfoundationsfordynamically describingmultiplechannelsconsistentwithgeologicalinformationandefficientinthe history matching process. It is obviously contrary to outcrop and subsurface observations to simply model channel by a set of simple geometry rectangular shapes. Among outcrop observationsarethechannelsinuosityandthediscontinuityofrockmaterialacrossthe channel boundary.In his work, V.Q. Phan (2000) defined thegeometry of eachchannel by the following set of parameters: Channelingdirection(globalorientation)byfiveparameters,threeofwhichare channel coordinates and the remaining two are azimuth and dip. Deviation of channel centerline from global channeling by three parameters. Width of channel by one parameter.Length of channel by one parameter. The rock properties (porosity and permeability) within channel are uniformly distributed and thus defined by two parameters. Therefore, a channel is completely described by 12 parameters, ten of which are for geometry and the remaining two are for flow properties. Grid Cell Properties and their Derivatives as a function of Channel Parameters Calculationofgridcellpropertiesasafunctionofchannelparametersforthesingle channelcaseisrelativelysimpleandthemethodhasbeendescribedbynumberof researchers, among which are Landa et al. One limitation of this method is that it requires the computations of the intersections between object boundaries and the simulation mesh grid, which is feasible only when the channel is described by simple geometric functions. Anotherlimitationofthismethodisthatdespitelargeeffortsincomputingsuch intersectionpoints,theareaineachcelloccupiedbyachannelisstillfarfrombeing accurate as the channel boundaries are arbitrary (V.Q. Phan, 2000). Thereforeasubcelltechniqueisproposedinthisresearchworkformodelinggeometry andspatialdistributionofchannelsinfluvialsystembyhistorymatchingofproduction andseismicdatainwhichtherockpropertiesandtheirderivativesasafunctionof channelparametersarecomputed.Thismethodiscapableofaccuratelyprocessingany numberofgeologicalobjectswitharbitrarygeometricshapesandresultsinafastand feasibleinversion.Theapplicationoftheproposedtechniquewillbepresentedinthis researchworkthroughseveraltwodimensionalexamplesinatwo-phasewater-oil/ water-gas case. The results of this work will also show that the integration of production andseismicdatacangiveusabetterunderstandingofthereservoirgeologyand geophysics to confirm a geological model (V.Q. Phan, 2000). Theimpactonhydrocarbonflowbythefaultsintheoilandgasfieldsnecessitates accuratecharacterizationofthe3-Dfaultgeometry,includingtheidentificationof potentialleakagepointsalongfaultsinthereservoir.Tothatend,ourgoalistounravel thefaultgeometries,andfaultgrowthhistoriesintheoilandgasfieldsthroughthe developmentofthefollowinggenerallyapplicableinterpretationmethodology.First, emphasisisplacedontheuseofseismicattributecharacteristicstodevelopandinitial faultinterpretation.Second,weuseoutcrop-scaleanalogsofreservoir-scalefaultsto describethenatureoffaultgeometryincrosssection,linkagetendencies,andother deformationcharacteristicspertinenttohoningtheseismicinterpretations.Finally,we finetunetheinitialinterpretationbyintegratingresultsofnumericalmodels,which examinetherelationshipsbetweenfaultgeometries,slipdistributions,andhorizon displacements.Thecombinationofthesetoolsallowsustoaccuratelyconstrainfault geometries in 3-D and to develop a hypothesis for fault evolution in oil and gas fields that augmentspreviousreservoircharacterizations(Kattenhorn,S.A.,andPollard,D.D, 2001). A workflow for fault linkage modeling for reservoir compartmentalization is presented in figure no. 9, where Poly3D is a C language computer program written by Andy Thomas (1993)tocalculatethequasi-staticdisplacement,strainandstressfieldsinalinear-elastic, homogeneous and isotropic whole-or half-space using planar, polygonal elements of displacement discontinuity and the boundary element method (BEM). StanfordUniversityTeamforRockFractureStudieshasdevelopedamethodbasedon mechanicalmodelingtoconstrainthepositionsandorientationsofsub-seismicfaults. Thelargeseismically-resolvablefaultsarebroughtintoa3Dnumericalmechanical model in order to determine the stress conditions near these faults at the time of faulting. TheStressfieldisthencombinedwithafailurecriterioninordertopredictthe orientations and densities of the smaller faults. This information is represented on a pair ofgrids(i.e.adensityandstrikegrid).Thegridsarethenusedtocondition2Dor3D stochasticmodelsoffaulting,whichuseapower-lawdistributionand/orstochastic growthprocessestosimulatesub-seismicfaults.Chartofworkflowforcharacterizing fractured reservoirs of oil and gas fields using Poly3D is shown in figure no.10. Figure No. 9 Fault linkage modeling for reservoir compartmentalization Tooptimizerecoveryinnaturallyfracturedreservoirs,thefield-scaledistributionof fracturepropertiesmustbeunderstoodandquantified.Incaseoflimiteddatawhere stochastic models typically fail a semi-deterministic method to systematically predict the spatialdistributionofnaturalfracturesandtheireffectonflowsimulations,isrobustly used.Thisapproachenablesthecalculationoffield-scalefracturemodels.Theseare calibrated by geological, well test and field production data to constrain the distributions of fractures with in the inter-well space (BOURNE, S. J., RIJKELS, L., STEPHENSON, B. J., AND WILLEMSE, E.J.M., 2001). Seismic data analysis 3D seismic data Interpretation (faults) Fault analysis (geometry, slip ) Poly 3D analysis Input parameters (fault geometry, rock properties, boundary condition) Output (computed slip) Is comparison between observed and computed slip satisfactory? NOYES Edit fault geometry Keep fault geometry Figure no.10 Subseismic Fault Modeling at reservoir scale First,wecalculatethestressdistributionatthetimeoffracturingusingthepresent-day structural reservoir geometry. This calculation is based on geomechanical models of rock deformation such as elastic faulting. Second, the calculated stress filed is used to govern thesimulatedgrowthoffracturenetworks.Finally,thefracturesareupscaled dynamicallybysimulatingflowthroughthediscretefracturenetworkpergridblock, enabling field-scale multi-phase reservoir simulation. Uncertainties associated with these predictionsareconsiderablyreducedbyconstrainingandvalidatingthemodelswith seismic,borehole,welltestandproductiondata.Thisapproachisabletopredict physicallyandgeologicallyrealisticfracturenetworks.Itssuccessfulapplicationto outcropsandreservoirsdemonstrates,thereisahighdegreeofpredictabilityinthe properties of natural fracture networks. Several examples have shown the success of this method in single and multi-phase fields (BOURNE, S. J., RIJKELS, L., STEPHENSON, B. J., AND WILLEMSE, E.J.M., 2001). Formationdamagetakesplaceatthelevelofporesandporethroats,therefore,for reservoircharacterizationatporelevelafullsuiteofpetrographicandpetrophysical analysis should be conducted, including: Thin section microscopy,including transmitted light,cathodoluminescence,fluorescene,highresolutionscanningelectronmicroscopy, electronmicroprobe,XRDfordetailedclayanalysis,labanalysisforclayseparation, imageanalysis,porecasting,isotopeanalysis,quantitativeanalysisofreservoir chemistry,analysisofcapillarypressurebymercuryinjection,labanalysisofporosity andpermeability,NMRmeasurementstodefineeffectiveporosityandrelative permeability,predictionofpermeabilityandeffectiveporosityunderdifferentclay distribution patterns, and CEC. 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Myers, 1996:EditedforBPExploration,StockleyParkUxbridge,London,Black Well Science Publishers Limited, 297 Pages. 10)DianaMorton-Thompson,andArnoldM.Woods,1997:Development Geology Reference Manual, AAPG Methods in Exploration, no. 10, Three-ring binding, 548 pages. 11)EvelinaParaschivoiu,2002:AustralianSchoolofPetroleum,Universityof Adelaide,Australia,AbstractofherPhDsynopsisonTheuseofforward stratigraphic modeling for reservoir characterization. 12)2004:WebsiteofSchoolofEarthScience,StanfordUniversity,California USA. 13)2003:WebsiteofImperialCollegeofScience,Technology,andMedicine, London. 14)2003:WebsiteofSchoolofPetroleumEngineering,UniversityofSouth Wales Australia. 15)2003: Website of Schlumberger Oilfield Services. 16)2004:WebsiteofAustralianSchoolofPetroleum,UniversityofAdelaide, Australia 17)PaulC.H.VEEKEN,2008:SEISMICSTRATIGRAPHY,BASIN ANALYSISANDRESERVOIRCHARACTERIZATIONAHandBook ofGeophysicalExploration(SeismicExploration,Volume37),509pages, edited by Klaus H