MultiphaseflowandmonitoringofCO2inthesubsurface

AnnualReport2016

SallyBensonCharlotteGaring,HailunNi,ChrisZaharsky,ScottMcLaughlin,andDavidCameron

DepartmentofEnergyResourcesEngineeringSchoolofEarthSciencesStanfordUniversity

May2016

ContactsSallyM.Benson:smbenson@stanford.eduCharlotteGaring:cgaring@stanford.eduHailunNi:hni@stanford.eduChrisZaharsky:zahas002@stanford.eduScottMcLaughlin:scottmcl@stanford.eduDavidCameron:davidc2@stanford.edu

Abstract Carbon capture and storage (CCS) is a climate change technology inrelativeinfancywiththepotentialtogrowtoasignificantscale.Whiletheoilandgas industry has, however had centuries of field-scale operations researchguidingscientists’understandingofoil/watermultiphaseflow,CCShasnotbeenafforded the same luxury. The prohibitive cost of pilot studies and the generalurgencyoftheglobalwarmingcrisisforcesacomparativelyrusheddeployment.As such, the nuances of multiphase flow in CO2/brine systems can only beanticipated fromacombinationof theory,core-scaleexperimentsand fielddatafrom precious few pilot-projects. Moreover, unlike oil and gas reservoirs withproven seals that have withstood the test of time, CCS storage sites must becarefully characterized and monitored to assure that CO2 will achieve highretention rates. Our research focuses on determining the underpinning scienceuniquetomultiphaseflowinCCSandtoalesserextentinenhancedoilrecovery(EOR)operations,throughbothexperimentandsimulation.Forexample,weareapplying new rigor to the fundamental understanding of residual trappingphenomena. At the core-scale, we are using Xray-microtomography to studywhether theprocessofOstwald ripening (the respectivedissolution/exsolutionof small/large bubbles)might affect themobility of residually trapped CO2. Anupscalablepore-scalemodel forsimulatingOstwaldripeningisbeingdevelopedin conjunction with the experiments. At the field-scale we are conductingmacroscopic-percolation simulations to determine the previously overlookedimpactofcapillarypressureheterogeneityonresidualtrapping.Wearepushingtheboundariesofreal-timepore-scalemultiphaseflowimaging,indevelopingtheuseofmicroPETscanningofsamples.WhilePETscanningrequires theuseofaradioactive tracer, it has advantages over established Xray-CT methods withimprovedresolutionanddense temporal sampling. InEORapplications,weareusingnumericalsimulationtoinvestigatethepotentialforCO2exsolutionfromasaturatedinjectionfluidtoaccessotherwisetrappedoilstores.Finally,inkeepingwith our overall investigation of the nuances of CCS multiphase flow, we aredeveloping robustmonitoring techniques that are insensitive to suchuncertainphysicalphenomena.Weshowthatearly-timepressuremonitoringintheabove-zone,whereinitial leakageisprimarilysingle-phaseinnature,maybeusedinavariety of situations to locate a potential leak in the caprock. As a whole, ourresearch is contributing to an improved understanding of subsurface CO2multiphaseflow,throughexperimentationandsimulationofphysicalaffectsthatmayhavebeenoverlookedinothersubsurfaceapplications.OurresultscontinuetoguideapplicationsstudiesinCCSmonitoringandEOR.

Introduction

Carbon dioxide capture and sequestration (CCS) in deep geologicalformationshasemergedoverthepasttwentyyearsasanimportantcomponentof the portfolio of options for reducing greenhouse emissions. Our researchfocusesonthefundamentalscienceunderpinningsequestrationinsalineaquifersandmultiphase flow of CO2, brine and to a lesser degree, oil reservoirs. Salineaquifers have the largest sequestration capacity, as compared to oil and gasreservoirsordeepunmineablecoalbeds.Salineaquifersarealsomorebroadlydistributedand thus, closer tomoreemissionsources. However,unlikeoilandgas reservoirs with proven seals that have withstood the test of time, salineaquifersmust be carefully characterized andmonitored to assure that CO2willachieve high retention rates. Improved fundamental understanding of multi-phaseflowandtrappinginCO2-brinesystemswillbeneededtotakeadvantageofthis largestoragecapacityofsalineaquifers. Importantquestionsremaintobeanswered, suchas,what fractionof theporespacewillbe filledwithCO2,whatwillbethespatialextentoftheplumeofinjectedCO2,howmuchandhowquicklywillCO2dissolveinbrine,andhowmuchCO2willbetrappedbycapillaryforceswhenwaterimbibesbackintotheplumeandtowhatextentiscapillarytrappingpermanent? Here we are developing new experimental data, new monitoringmethods, and carrying out simulations to improve our ability to answer thesequestions.ModificationstocurrentlyacceptedmultiphaseflowtheoryarebeingdevelopedtoprovidereliablepredictionsofthefateandtransportofCO2inthesubsurface. A combination of laboratory experiments, numerical methods andanalyticalsolutionsarebeingdevelopedtoaddresstheseissues.Interactionanditerationbetweenthesefourapproachesimprovesourabilitytoquicklyidentifyand test new phenomena and approaches for accurately capturing them inquantitativemodels.Highlightsfromthepastyearareprovidedbelow.

Highlightsofthisyear’sprogressinclude:

• NewinsightsaboutthestabilityofresiduallytrappedCO2throughtheuseofsynchrotronX-raymicro-tomography;

• Investigationsof the relative importanceof small scaleheterogeneity oncapillarytrappingusinginvasionpercolationsimulation;

• Development of positron emission tomography for validating sub-corescaleheterogeneity;

• Investigation of a new method we have developed for enhancing oilrecovery–throughexsolutionofadissolvedCO2phase;and

• Furtherdevelopmentandoptimizationofabove-zonepressuremonitoringforquicklyandinexpensivelydetecting, locating,andquantifyingleakagefromCO2storagesites.

Thesearediscussedingreaterdetailinthesubsequentsectionsofthereport.

Long-termevolutionofresiduallytrappedCO2 Residual trapping is a key process for carbon dioxide storage efficiencyand securitybeyond theprimary stratigraphic seal: following injection, theCO2plumemigratesinthereservoirunderbuoyantandviscousforcesleavingbehinddropletsofCO2thataredisconnectedfromtheplumeandremaintrappedinthepore structure [1]. It is thengenerallyassumed that the residually trappedCO2will be permanently immobilized until it is dissolved ormineralized.However,there is little experimental data either from laboratory experiments or fromnatural analogues to support this assumption and the long-term evolution ofresidual trapping should be further investigated. One possiblemechanism thatcould threaten the stability of residual trapping is Ostwald ripening, a processthatwouldcausethegradualgrowthofCO2gangliawithlowcapillarypressures,at the expense of ganglia with higher capillary pressures [2]. The growth ofresiduallytrappedCO2gangliamaybecomeanissueifthegangliastartexpandingin the vertical direction until they eventually present a capillary pressure highenoughto inducegravitationalremobilizationupward.AsOstwaldripeningwillbedrivenbydifferencesincapillarypressurebetweenCO2ganglia,assessingtheimportance of this mechanism in the context of carbon storage requires toquantifycapillarypressuredistributionofresiduallytrappedCO2gangliatrappedinthemicrostructureofarock.Capillarypressure,whichdenotesthedifferenceinpressurebetweenanonwettingandawettingfluidisexpressedbytheYoung-Laplaceequation𝑃" = 𝑃$% − 𝑃% = 2𝜎𝜅,where𝜎istheinterfacialtensionand𝜅isthemeancurvatureoftheinterfacebetweenthetwofluids.However,duetothehighvarietyofcomplexgeometriesof thestructuredisplayedbyrealrocks, theinterfacebetween two fluids cannot bederived easily as for simple geometriessuch as a sphere or a cylinder [3], and pore-scale investigations using three-dimensionalimagingtechnicssuchasX-raymicrotomographybecomenecessary[4].We acquired a multi-scale high-resolution synchrotron-based X-raymicrotomography dataset of residually trapped gas (air) after water gravity-drivenimbibitionwithbrine(waterwithKI)inordertodevelopreliablemethodsforestimatingpore-scalecapillarypressureof individual residually trappedgasganglia. The experimentwas conducted on three different rock types (sinteredglassbeads,BoisesandstoneandFontainebleausandstone)andwithvoxelsizesvaryingfrom0.64to4.44µm,attheAdvancedLightSource,LawrenceBerkeleyNationalLaboratory.Anexampleofatwo-dimensionalcrosssectionisdisplayedinFigure1forthethreesamples.Eachsamplewasscannedat leasttwiceusingdifferent magnification lenses in order to investigate the effect of imageresolutionon theprecisionof interfacial curvaturecalculations.Foreach imageacquiredthenon-wetting(air)andwetting(brine)phaseswereidentified,aswellastheinterfacestheyshareonewitheachotherandalsowiththesolidsurface,andtheconnectivityandsizeofthetrappedgaswerequantified.Thecurvatureofallair/brineinterfaceswasthencalculated.Thedistributionofcurvaturevalues

andstandarderrorforsinglegasgangliawasfirstinvestigated.Ameancurvaturevaluewasthencalculatedforeachgasgangliaandthedistributionofcurvaturevalues,hencecapillarypressures,wasestimated.TheimageprocessingwasdoneusingacombinationofImageJ,AvizoandMATLABsoftwares.

Figure 1. 2D cross sections through the 3D reconstructed volumes for the Glass beads, Boise sandstone and Fontainebleau sandstone highlighting the significant difference in pore structure between the three samples.

The results shows that fluid phase distribution and morphology as well ascapillarypressureestimateshighlydependonbothi)theporesizeandshapeoftherocksampleandii)theimageresolutionofthescans.Poroussampleshavinga pore structure consisting ofwell-connected flow paths of large diameter andlow ormoderate pore bodies pore throats ratio, represented here by the glassbeads sample, display large air and brine features with interfaces that can beunambiguously identified using the lower resolution (2X magnification - voxelsizeof3.28µm).Forthistypeofsample,theresidualgasconsistsinasinglewell-connected cluster spanning through most of the sample. The interfaces of thelargegasclusterandthebrinephasehavesimilarcurvaturevalues(lowstandarddeviation)thatarelowerthanforthetwosandstonesamples.Sincetheinterfacesare large they are representedby ahighnumberof point, evenwith the lowerresolution, and the standard error of the mean, SEM, is low (1.2E-4 µm-1).Whereas this resolution is sufficient to accurately probe the pore networkdisplayed by the Glass beads sample, higher resolution is needed for a moreprecise characterizationof themicrostructure for the sandstone samples.MICPmeasurements conductedon sister samples show indeed that20%and30%ofthe pore space of the Boise and Fontainebleau sandstone respectively havefeaturespresentingadiameterbelowthevoxelsizeforthe2Ximages.Both sandstone samples present a high number of disconnected gas ganglia ofvarying sizes,mostof thembeing located inoneor fewpores.Boise sandstonethat have larger pores than Fontainebleau sandstone also present larger gasganglia. Themost significant differencebetween the two sandstones lies in thesizeandshapeoftheinterfacesthatgasgangliasharewiththebrinephase.Thevery small throat diameter and high residual gas saturation for Fontainebleau

sandstone leads tomuch smaller air/brine interfaces than forBoise sandstone,andwhilethemediumresolution(5Xmagnification–voxelsizeof1.8µm)allowsboth confident interface identification and curvature calculation for Boise, thehigher resolution (10X magnification – voxel size of 0.64 µm) is required forFontainebleau. Theparticularmorphology of Fontainebleaupore structure alsoresults in a high amount of flat interfaces corresponding to thin brine filmsbetweenthetrappedgasandthequartzgrainsthathavetoberemovedfor thecurvature calculations. In average, the SEM of curvature calculation for singleganglia is 1.8E-3 µm-1 forBoise 5X image and is 3.1E-3 µm-1 for Fontainebleau10Ximage.Theanalysisofbothsampleimagedwithdifferentresolutionsuggeststhat sincemore smaller gas gangliamay be visible with the higher resolution,especially fora sandstonepresentingsignificantly smallpore features,differentcapillarypressuredistributionandmeanvaluesmaybeestimatedusingoneortheotherresolution.Ingeneral,sincetheuseofahigherresolutionleadstomoreconfidenceininterfacesegmentationandlesserrorincurvaturecalculationsdueto the higher number of points describing the interface, the use of a highresolutionwhenpossibleisrecommended.TheresultsofthecurvatureanalysisforthethreerocksamplesarepresentedinFigure2anddetailedinTableI.

Rock φHe dHg voxelsize Nair κ (µm-1) Pc(Pa)sample (-) (µm) (µm) ganglia mean std mean stdGlassbeads 0.39 101 3.28 5 0.008 0.002 1,107 308Boise 0.28 31 1.80 49 0.032 0.020 4,617 2,889Fontainebleau 0.10 14 0.64 26 0.147 0.093 21,110 13,401

Table I. Characteristics of i) the three rock types: porosity 𝜙+, measured with Helium pycnometry and mean pore-throat diameter 𝑑+. [µm] estimated using MICP and ii) the analyzed sub-samples presented in Figure 2: voxel size [µm], number of separated air ganglia, mean curvature 𝜅 [µm-1] (mean value, standard deviation) and corresponding capillary pressure 𝑃" [Pa] (mean value, standard deviation)

Figure 2. Interfacial curvature analysis for the glass beads (left), Boise sandstone (middle) and Fontainebleau sandstone (right) samples: for each analyzed sub-volume, the results are organized in column where the first image is a visualization of the disconnected gas ganglia, the second is a visualization of the air/brine interfaces (red), the third image is a visualization of all the interfacial curvature values displayed by the interfaces taken into account for the mean calculation, and the last image is a graph of the curvature values distribution pdf for each air ganglion presenting interfaces large enough for the curvature calculation.

Themeancurvaturebetweenresidualgasgangliaandthebrineis lowerfortheglassbeadssamplethanforthetwosandstonesamplesandgasgangliatrappedinFontainebleausandstonepresentahighercurvaturethantheonestrappedinBoisesandstone.Forallcases,thecorrespondingmeancapillarypressuresareinthe sameorderofmagnitude than theentrypressureestimated from theMICP

curves. There is a distribution of capillary pressures between the different gasganglia trapped in both sandstones, and in a higher extent for Fontainebleausandstone,asdisplayedinFigure3.

Figure 3. Capillary pressure distribution for the Boise and Fontainebleau analyzed sub-volumes presented in Figure 2 and Table I.

This suggests thatOstwald ripeningmechanismcould result in transferringgasfromthegangliapresentinghighercapillarypressuretosurroundinggangliawithlowercapillarypressures.HoweverforBoisesandstoneitwouldonlyconcernavery small fraction of gas and for Fontainebleau sandstone it would besignificantlylimitedbytheverysmalldiameterandlonglengthofdiffusionpathsbetweentwogasganglia.Inconclusion,thestudysuggeststhatforhomogeneousrocks Ostwald ripening may not be an important mechanism for remobilizingresidually trapped CO2 and that equilibrium could be possible. However, ourstudies have shown that the capillary pressure in trapped ganglia are nearlyequal to the capillary entry pressure of the rock. Since many systems have asignificantdegreeofcapillaryheterogeneity[6]thiswillcreateadrivingforceforOstwaldRipening,albeitovermuchlargerspatialdomainsandmuchlongertimescales than investigatedhere.Wearenow initiatingresearch to investigate thispossibility.References

1. Benson,S.M.,andCole,D.R.,2008.CO2sequestrationindeepsedimentaryformations,Elements,vol.4,p325-331

2. Epstein,P.S.,andPlesset,M.S.,1950.OntheStabilityofGasBubblesinLiquid-GasSolutions,J.Chem.Phys.,vol.18,nº11,pp.1505-1509

3. Voorhees,P.W.,1985.ThetheoryofOstwaldRipening,J.Stat.Phys.,vol.38,nº1-2,p.231-252

4. Bear,J.,1972.Dynamicsoffluidsinporousmedia,Elsevier,New-York5. Wildenschild,D.,andSheppard,A.P.,2013.X-rayimagingandanalysistechniquesfor

quantifyingpore-scalestructureandprocessesinsubsurfaceporousmediumsystems,AdvancesinWaterresources,vol.51,p.217-246

6. Krause,M.H.,Perrin,J.C.,&Benson,S.M.(2011).Modelingpermeabilitydistributionsinasandstonecoreforhistorymatchingcorefloodexperiments.SPEJournal,16(04),768-777.

InfluenceofCapillaryHeterogeneityonResidualGasTrappingThisresearch focusesonhowtopredict theamountofCO2 thatcanbe trappedafter spontaneous imbibition of the formation brine through characterizing theheterogeneity of a reservoir rock. A simulator has been built, based onmacroscopic percolation theory to model how different kinds of rockheterogeneity affect capillary heterogeneity trapping of CO2 after imbibitionunderstronglycapillary-dominatedconditions.ThesimulationresultsshowthatCO2capillaryheterogeneitytrappingafterspontaneousimbibitionincreaseswithincreasing degree of rock heterogeneity, decreasing correlation length, andincreasingdegreeofanisotropy.Methodology

A macroscopic percolation simulation was run using a 100 by 100 two-dimensional grid in the x-y plane. Fluid enters the grid from the top edge andexitsthegridfromthebottomedgeataconstantpressure.Thetwoedgesontheleft- and right-hand side are no-flow boundaries. Each grid block is above therepresentative-elementary-volume (REV) scale and has individually definedlocal-scale drainage capillary pressure curves and permeability values. TheLeverett J function is used for scaling so that each grid block has a differentcapillarypressurecurve.Wenotethatbothdrainageandimbibitionusethesamedrainage grid-block-level capillary pressure curves for simplicity. Thepermeabilityvaluesaredrawnfromalognormaldistribution.Thevaluesforthevarious input parameters aremodified from theBerea sandstone experimentaldata published by Krevor et al. [1]. For simplicity, a single porosity valuewasassignedtoallgridblocksandsetirreduciblewatersaturationaswellasresidualCO2saturationvaluesto0.The macroscopic percolation technique is used to simulate CO2-waterdisplacementprocessesunderstronglycapillary-dominatedconditions.Wethusassume that the flow rate is small enough, such that the system is always incapillarypressureequilibrium.Gravityforcesareignored.Themodelstartswith100%water saturation throughout thegrid.An initialnonzeropressureon thenonwettingphase,initiatesthedisplacementprocess.Becausethereisessentiallynoviscouspressuregradient,theexternallyimposedpressuregradientisdirectlyequaltothecapillarypressureofthegridasawhole[2].Tobeginthesimulation,theimposedpressureisincreasedfrom0Pato102Patomodel thedrainageprocess inwhichCO2 invadesthe fullywater-saturatedgridfrom the top edge. During drainage, CO2 invades the grid in an invasionpercolationstyle.Ateachpressurestep,thesimulatorscansthewholegridtoseewhichgridblocksontheCO2invasionfronthavecapillaryentrypressureslowerthanthecurrentimposedpressure,andwillinvadethosegridblocksifthatisthecase. The CO2 saturation is then computed by inverting the capillary pressurefunctions of each grid block [3]. After the drainage process is complete, theimposedpressureisreducedbackfrom102Pabackto0Patomodelimbibition

inanordinarypercolation style from the topedgeof thegrid.Considering thatCO2snap-off trapping isapossibility,weassume thatwater flowoccurs in thinfilms on all grain surfaces in strongly-water-wet porous media. Therefore,ordinary percolation style displacement is the most suitable to model suchimbibitionbehaviorsbecause it allowswater to get access to thewhole grid atany capillary pressure [2]. The saturation of each grid block is calculated byinverting the individually defined drainage capillary pressure functions at eachpressure step. During imbibition a trapping rule is imposed, which states thatwhenagridblockissurroundedby100%watersaturatedgridblocks,thenthatgrid block is determined to be trapped and its CO2 saturation can no longerchange with decreasing capillary pressure. After the imbibition process iscompleted,theaverageCO2saturationateachpressurestepisusedtoobtainthelarge-scale or “effective capillary pressure” curves for both drainage andspontaneousimbibition[2].ResultsWe ran a set of four simulations to explore the effect of subcore-scaleheterogeneity on capillary heterogeneity trapping. The degree of subcore-scaleheterogeneitywasmeasuredwith variability in the permeability field. A largerstandard deviation in the log-transformed lognormal permeability distribution(𝜎34 5 ) represents a larger degree of subcore-scale heterogeneity. For𝜎34 5 values of 0.5, 1.0, 1.5, and 2.0, we computed the effective capillary pressurecurves frommacroscopic percolation. An example of one of the simulations isprovidedinFigure1. ThisstudydemonstratedthattheamountofCO2capillaryheterogeneitytrappingincreaseswith𝜎34 5 asshowninFigure2.Acasewithanisotropicpermeabilityfieldwithcorrelationlengthinbothxandydirectionof5and10gridblockswasanalyzed,withmultiplerealizationsplannedfor futurework. Preliminary results on the single case show that CO2 capillaryheterogeneity trappingdecreaseswith increasingcorrelation length in isotropicpermeabilityfields.Furthermore,caseswithanisotropicpermeabilityfieldswithx-direction correlation length of 5 and 10 grid blocks and no y-directioncorrelationwere also investigated. Preliminary results show that CO2 capillaryheterogeneity trapping increases slightly with increasing degree of anisotropywhenwehavehorizontallaminations.Ongoingworkincludesexperimentingwithnewtrappingrulesresultinginlarge-scalehysteresisincapillarypressurecurvesanduniquescanningcurves,aswellas incorporating local-scale hysteresis to model both residual trapping andcapillary heterogeneity trapping. Future endeavors to further current researchwillincludecarryingoutstochasticprocesstoquantifyuncertaintyinsimulationresultsandvalidatingthemacroscopicpercolationsimulationresultswithcore-flooding experiments as well as simulation results from conventional finite-differencesimulator.

Figure 1: Illustration of grid-block-scale capillary pressure curves and the computed effective capillary pressure curves for both drainage (a) and imbibition (b) for uncorrelated permeability field with 𝝈𝐥𝐧 𝒌 =1.0; (c) shows the combined effective capillary pressure curves for both drainage and imbibition for uncorrelated permeability field with 𝝈𝐥𝐧 𝒌 =1.0.

Figure2:InfluenceofcapillaryheterogeneityonresidualCO2trapping.

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References[1] S.C.M.Krevor,R.Pini,L.Zuo,andS.M.Benson,“RelativepermeabilityandtrappingofCO2

andwaterinsandstonerocksatreservoirconditions,”WaterResour.Res.,vol.48,no.2,pp.1–16,2012.

[2] M.A.Ioannidis,I.Chatzis,andF.A.L.Dullien,“MacroscopicPercolationModelofImmiscibleDisplacement:EffectsofBuoyancyandSpatialStructure,”WaterResour.Res.,vol.32,no.11,pp.3297–3310,1996.

[3] B.H.KueperandD.B.McWhorter,“TheUseofMacroscopicPercolationTheorytoConstructLarge-ScaleCapillaryPressureCurves,”WaterResour.Res.,vol.28,no.9,pp.2425–2436,1992.

UseofMicro-PositronEmissionTomographyforValidatingSub-CoreScalePermeabilityMaps

Quantificationandmeasurementofcorescaleheterogeneityisofspecialinterestbecause it isknowntohaveasignificant impactonmultiphasedisplacement inporous media. Laboratory characterization of fluid flow behavior throughheterogeneous sandstone cores is key for understanding and informingsimulation models on realistic capillary trapping and continuum-scale relativepermeability curves. To date, most of the studies pursuing this line of inquiryhave relied on X-Ray Computed Tomography (CT) scanning for measuring thesub-core scale rock properties [1]. Here we describe the development of arelatively new experimental platform for these investigations, namely, microPositronEmissionTomography(microPET).PositronEmissionTomographyisanon-destructive, fourdimensional, reproducible imaging technique that enablesdirectvisualizationofdynamicsinglephaseandmultiphasefluidflowinporousmediaat the continuumscale.ThemicroPETscanneremployed in this study isdifferentfromtraditionalPETscannersinthatitisdesignedforpre-clinical,smallanimal imaging and thus has amuch smaller system diameter than traditionalclinical PET scanners. This smaller system size improves the fundamentalresolutionofPETimagingbyaboutafactorofthree.WhilePETimaginghasbeenutilized inahandfulof studies for singleandmultiphase flowanalysis incores,we believe that with additional development, micro-PET could provide animportantcomplementtoCTbasedimagingtechniques.In this studywe present comparisons of fluid saturationsmeasurements usingmicroPET and X-ray CT measurements. We use these data to develop sub-corescalepermeabilitymapswith the techniquesdeveloped inKrauseet al, [1]andPinietal,[2].Finally,weconductasinglephasetracertransportexperimenttovalidatethesub-corescalepermeabilityandporositymaps.Recent advances in laboratory and theoretical techniques have led to thedevelopment of methods for quantifying capillary heterogeneity during coreflooding experiments with X-ray CT [3,4] and using voxel based capillarypressure scaling to quantify the permeability field of entire core samples [2].While Computed Tomography is the most widely used imaging technique forobserving experimental multiphase flow behavior, it has a few importantlimitations.First,thetemporalresolutionforimagingdynamicflowbehaviorcanbe severely limited depending on the scanning and cool down speed of theparticularscanner.PETprovidescontinuous3-dimensional imagesof theentirecorethroughoutthedurationoftheexperiment.Duringimagereconstructionitispossible todiscretize thePETscan into time stepsas small as a fewseconds—depending on radioactivity levels in the scanner. Second, when imaging singlephase flow or flow of two fluids with similar attenuation coefficients it isnecessarytoadddopantstooneorbothofthefluidswhichmayalterthedensityandchemicalbehaviorofthefluids.EmissiontomographytechniquessuchasPETrely on very small (< X ppm) concentrations of positron-emitting tracer. These

lowconcentrationsdonotalterthedensityorchemicalreactivityoftheinjectedfluids.TheflexibilityofthedynamicimageconstructioncombinedwiththehighersignaltonoiseratioenablesPETimagingtocomplimentCTimagingforstudyingvariousmultiphaseflowprocesses.DuringaPETscan,positronsareemittedasconsequenceofaβ+decayfromtheinjected radiotracer. As they travel through the surroundingmaterial they loseenergy and slow down due mostly through electromagnetic collisions withsurroundingatomsandmolecules.Asthevelocityofthepositrondecreasesthereis an increasing probability that it combines with an electron and annihilates.This annihilation event produces two gamma rays emitted in nearly oppositedirections,eachwithanenergyof511keV.Thesepairsofcoincidentgammaraysare thendetectedwithanarrayofphotondetectors thatsurround thematerialcontainingtheradiotracer(Figure1).Aneventisregisteredbythesystemonlyiftwo coincident gamma rays strike two separate photon detectors within thecoincidencewindow(4.5ns).Positron Emission Tomography relies on the artificial production of positronemittingradionuclides.Radionuclidesaretypicallychosenbaseddesiredhalf-life,radioactivedecayemissions,radionuclidefacilitygenerationcapabilities,andthechemical properties of tracer fluid. The radiotracer used in this study isFludeoxyglucose(FDG)whichincorporatesthe18Fpositronemittingisotope.Theradiotracer is provided by the Stanford Radiochemistry Facility. Thisradioisotope was chosen because its half-life of 110 minutes is favorable formulti-hour scans. Simple pulse tests indicate there is little or no chemicalinteraction between the radiotracer and the porous media however chemicalrockanalysisisongoing.PET imaging techniques have only been utilized in a handful of reservoirengineeringandhydrologyapplications.RecentlyPinietal,[5]usedPETimagingof a 11C radiotracer to quantify single phase advection and dispersion in arelatively heterogeneous Berea core. Loggia et al, [6] used positron emissionprojection imaging(PEPI) to image flowthrougha fracture ina large limestoneblock(36cmx26cmx60cm).Theyuseda 64Cu tracer toanalyzegeometricaldispersion,calculatefractureaperture,andquantifyfluidchannelingthroughoutthe fracture. Spatial correlation analysis of the resulting fracture aperturewascalculated. The correlation distance was found to be on the order of 10 cm.Kulenkampff et al, [7] used two different radiotracers [18F]KF and [124I]KI toimagealarge(roughly0.5mmaperture)axialfractureinagranitecore.Apeak-finding function was developed to identify the fracture location from the PETdata, the results were compared with aperture measurements from a highresolution, micro CT scan of the same fracture. The only examples of highpressureexperimentsdonewithPETimagingwereperformedbyFernoetal,[8]andMaucecetal,[9]. InMaucecetal,[9],F18wasdilutedinwaterandusedtovisualize flow through small (1 inch diameter) sandstone and fractured shalecores.Fernoetal,[8]performedsingleandmultiphaseexperimentsatreservoir

conditions.ThiswasthefirststudytodescribeamethodforusingPETimagesforcalculatingphase saturations,however thesemeasurementswerenot validatedwithothersaturationquantificationtechniques.

Figure1:Schematicofpositronannihilationeventthatcreatestwogammaraysthattheoreticallytravel exactly 180o from each other, striking the photon detectors in PET scanner within 4.5nanosecondsofeachother.

In this study, awater-nitrogen drainage experiment is performed in a 3.5 inchdiameter Berea sandstone core first using CT to measure porosity and phasesaturations and then using a preclinical microPET scanner to measure phasesaturations in a second experiment. ThemicroPET experiment had two stages.Thefirstwasatracerpulsetestinwhichavolumeoftracerapproximatelyequalto10%ofthecoreporevolumewasinjectedandthendisplacedbywateratthesameflowrate.Thesecondstageoftheexperimentwasatracersaturationanddrainage experiment in which approximately 2 PV of radiotracer was injectedintothecoreinordertofullysaturatethewettingfluid(water)withtracer.Oncethe core was fully saturated with tracer, a simple drainage experiment wasperformed in which nitrogen gas was injected at 10 mL/min and then 25mL/min. The reduction in radiotracer in the core scales with the reduction inwetting phase saturation enabling phase saturation throughout the core.Whilethe final PET saturation maps have a lower spatial resolution than the CTsaturation maps, the saturation values between the experiments are in goodagreementandthusprovidevalidationthatPETimagingcanbeusedtoprovidequantitativesaturationinformation.UsingmethodssuchasthosedevelopedbyKrauseetal[4]itispossibletousetheresulting core saturation maps in combination with MICP data to constructuniquepermeabilitymapsdescribingthepermeabilityineveryvoxelinthecore.The in-situ tracer migration data collected from the PET scan is then used to

validate thesepermeabilitymapsby comparing thePETdatawith results fromanalyticalADEtracertransportcalculationsrelyingonthepermeabilitymapstocharacterize the core-scale model. Future work will further validate the PETtransportthroughtheuseofnumericalsimulation.MethodsA schematic of the experimental setup used for these experiments is shown inFigure2.Thesetupenablesbothsingleandmultiphasefluidinjection,radiotracerremovalandshielding,andcontinuouscollectionofinletandoutletpressureandradiotracer concentration data. Continuous water injection is achieved with apairofTeledyneISCOModel500Dsyringepumpsandcoreoutletbackpressure(typically around 100 psi) is maintained with an ISCO Model 1000D syringepump.GasisinjectedusingaSierraC100Lgasmassflowcontrollerratedto500psi.ConfiningpressureisappliedwithanISCO500Dpumpandissetto400-450psi forall experiments.Tracer is injected into thecore fromanadditional ISCO500DpumpandisloadedusinganNE-1000programmablesinglesyringepump.Injectedfluidpassesthroughaninletradioactivitydetectorpriortoenteringthesample holder. The effluent fluid then passes through an outlet radioactivitydetector prior to being discharged into a waste container. Both differential(Omegadyne150-DIFF-W/W-USBH) and absolute (Omegadyne100-USBH) fluidpressurearemeasuredat the inletandoutletof thesampleholder.Leadbricksand machined lead shielding is placed around several of the experimentcomponents including the radioactivity detectors, injection syringe, tracerinjection pump, backpressure pump, and the radioactivity waste reservoir.ShieldingisusedtoreducetheradiationdosereceivedbytheexperimenterandtoreducethebackgroundradiationthatcanincreasenoiseduringthemicroPETscans.ThecoreholderusedinthisstudywascustombuilttofitinthemicroPETscannerandutilizedalowattenuationcastnylonmaterialfortheouterconfiningshell.The inletandoutletcapswheremachined fromaluminum.Thereare twoconfining pressure ports on the inlet of the coreholder to enable temperaturecontrolviacontinuousfluidcirculationbetweenthecoreholderandtheconfiningpressure pump which contained a temperature control sleeve. The confiningpressurewaterwas circulatedwithanEldexReciProSeries2000 reciprocatingpistonpump.ThePETscansareperformedusingaSiemenspre-clinicalInveonDPETscannerattheStanfordCenterforInnovationinIn-VivoImaging(SCI3).Priortostartingexperiments, the core sample is loaded in the coreholder and scanned on aseparateSiemenspre-clinicalInveonCTscanner.ThisCTscanisusedtogeneratetheattenuationcorrectionmapwhichisusedforthescattercorrectionduringthePETreconstruction.A separate experiment was performed using only a GE Lightspeed clinical CTscannerintheStanfordDepartmentofEnergyResourcesEngineeringtoacquire

porosity and phase saturation maps in order to validate saturationmeasurementsmadeduring thePET experiment. The sameexperimental setupwasusedfortheCTandPETexperiments.

Figure2:SchematicofexperimentalsetupofsingleandmultiphasePETexperiments

The3.5 inch diameter by 5.5 inch longBerea corewas first dried in a vacuumovenforseveraldaysbeforebeingloadedintothecoreholder.FollowingthedryCTscans, thecorewassaturatedwithgaseousCO2 todisplaceall theair in thecore.Nexttapwaterwasruncontinuouslythroughthesampleforover24hoursat a flow rates of 10-20mL/min and pressures between ambient and 150 psi.WaterwasremovedfromthesystemandreplenishedwithfreshtapwaterthreetimesinorderbesurethealltheCO2originallyinthecorewaseitherdisplaceordissolved into the water. Once the core was fully saturated with water fiverepeatedwetCTscansweretakenofthecore.Gas injection started at 5 mL/min and was injected continuously for over 12hours (approximately 20 PV), with water injection completely shutoff. FiverepeatedCTscanswereagaintaken.Thisprocesswasrepeatedatgasflowrates10,15,20,25, and30mL/minexcept at each flowrate thevolumeofnitrogeninjectedwas only 5-8 PV. Before increasing the gas flow rate five repeated CTscansweretakenoftheentirecore.Following the completion of the CT experiment CO2was again injected at veryhigh flow rates (~100 mL/min) and at pressures up to 100 psi in order todisplacethenitrogeninthecore.Waterwastheninjectedforapproximately48hoursat10-20mL/min.Waterwasremovedfromthesystemandfreshtapwater

wasaddedtothesystemthreetimesinorderbesurethealltheCO2inthecorewaseitherdisplacedordissolvedintothewater.Oncethecorewasre-saturatedwithwaterashorttracerinjectionfollowedbyadrainageexperimentwasperformedduringafourhourmicroPETscan.PriortostartingthemicroPETscan,4.2millicurieofFDGwasinjectedintotheISCOtracerpumpcontaining500mLofwater.InordertoensurepropermixingofthetracerandresidentwatertheFDGsolutionwasfirstdilutedwithapproximately50mLofcoldwater.ThisdilutedFDGsolutionwastheninjectedintothewarmerwaterintheISCOpumpandrepeatedlyinjectedandproducedfromtheISCOpump(settoconstantpressuremodea10psi)six times.OncetheFDGwaswellmixed inthe ISCO pump, themicroPET scanwas started and the freshwater pumpwasshutoff and tracerwas injected for150secondsat10mL/min.After25mLoftracer was injected the tracer pump was shutoff and freshwater injectionimmediately resumed at 10 mL/min. Approximately 2.5 PV of freshwater wasinjected, and the tracer pulse was completely displaced through the core asdeterminedbytheoutletradioactivitydetector(firstpulseofredcurveinFigure3). Following this stage of the experiment, 475mL of radiotracerwas injectedcontinuously into the core until the corewas fully saturated, again verified byinletandoutletactivitycurves(Figure3).Tracer injection is thenshutoffand6PVofnitrogenisinjectedat10mL/min.Duringthelaststageoftheexperimentthe nitrogen flow rate is increased to 25 mL/min and approximately 6 PV ofnitrogenisinjected.

Figure3:DecaycorrectedinletandoutlettracercurvesofmultistagePETexperiment.VerticalblacklinesindicatedynamicframelocationsofPETscanreconstructionforphasesaturation

analysis.

Following the completion of themicroPET scan the recorded coincident eventsarebinnedintodesiredtimesteps(blackverticallinesinFigure3).Thesebinsofcoincidenteventsarethenusedtoreconstructdynamic3Dimagesof theentiremicroPET scan. As a result of this process, each frame of the reconstructedimagesshowstheaveragetracerlocationbetweenthebeginningandendtimeofthat frame. In general, longer timesteps provide slightly better images becausemore decay events improve reconstruction accuracy and statistics. One of themajorbenefitsofPETimagingisthatscanscanberepeatedlyreconstructedwithdifferenttimestepspecificationssothatdifferentstagesoftheexperimentcanbeanalyzed in detail long after the experiment is complete. The reconstruction

methodusedforthisstudyisthethree-dimensionalOrderedSubsetExpectationMaximization using Maximum A Priori (OSEM-OP MAP), an iterativereconstructionmethod[10].Thenominalresolutionofthereconstructedimagesis0.77mmx0.77mmx0.79mmhowever the imagesare coarsenedup to3.8mmx3.8mmx3.8mm.Forthesakeofcomparison,theCTscanswerecoarsenedtosimilarvoxelsize.Coarseningwasdonebytakingthearithmeticmeanof thesmallervoxelslocatedintheresultingcoarsevoxels.ResultsOncethemicroPETimageisreconstructed,watersaturation(Sw)iscalculatedasthelinearinterpolationoftime-averageactivityconcentrationatsteadystategasinjection (PETdrainage) over the time-average activity concentration of the samevoxelatfullwater/tracersaturation(PETft)asshowninEquation1.

Iftheinitialvoxeltracerconcentration(PETpt)iszerothenthissimplifiestothesaturation equation described in [8]. To get the time-averaged activityconcentrationatfullwatersaturationthethreeframesinwhichtracerisflowinthe inlet is equal to the tracer flow at the outlet are averaged (three frameshighlighted in green in Figure 3). This frame averaging is done to improve thevoxelstatistics,similartoimprovementsgainedbyaveragingrepeatedCTscans.The framesusedtocalculate the timeaverageconcentrationof thesteadystatenitrogen injection at 10mL/min arehighlighted inpink and at 25mL/min arehighlightedinorangeinFigure3.TocalculatenitrogensaturationsforeachvoxelfromtheCTexperimentwerelyonthelinearinterpolationbetweenpurestatesasdescribedbyEquation2[11].

Where CTdrainage is calculated from the average of the five scans taken at givennitrogen flow rate, CTgas is calculated from the average of the five scans takenprior tosaturating thecorewithwater,CTwater is taken fromtheaverageof thefivescanswhenthecoreisfullysaturatedwithwater.AcomparisonofthesliceaveragesaturationsfromthemicroPETandCTdrainageexperimentsisshowninFigure4.Thesaturationsattheinletsliceofthecoreandslicestowardthecenteragreeverywell.Thereissomediscrepancybetweenthesaturationaround10mmfromtheinletofthecore.Webelievethisisnotduetoerrors in either the CT or microPET scans but the fact that initially the CTdrainage experiment started by flowing roughly 20 PV of nitrogen through thecoreat5mL/min (flowinggasovernight)before increasing the flowrate to10mL/minwhereasthePETscanexperimentflowedonly6PVofnitrogenthroughthecoreat10mL/minbeforeincreasingtheflowrateto25mL/min.

Figure4:Sliceaveragenitrogenphasesaturationsalongthelengthofthecorefordifferentflowrates. Solid lines are measured during a drainage experiment using CT to calclate saturations,dashedlinesaresliceaveragevaluesmeasuredduringaseparatedrainageexperimentusingPETtomeasuresaturations.StreamtubepermeabilityandporositycalculationWiththemeasurementofthe inletslicesaturationfromboththemicroPETandCTexperiments, thecoreaveragecapillarypressurecanbeestimatedusing themethoddescribedin[3].Fourdifferentplugsampleswerealsousedtomeasurecapillary pressure using the Mercury Injection Capillary Pressure (MICP)technique with a Micromeritics Autopore IV. The results of these four curveswerefitwithaBrooks-Coreycapillarypressurefunction(Equation3).Thefittingparametersusedwereλ=1.6,Swir=0.21,andPcentry=1.2psi.

Using the fitted capillarypressure function and thephase saturationmaps it ispossibletodescribethepermeabilityofeveryvoxelinthecoreusingasimplifiedversionofthemethoddevelopedbyKrauseetal[4]anddescribedinEquation4and5.

Wherekiisthevoxelpermeability,φiisthevoxelporosity(measuredwithCT),Pcbaristhecapillarypressureoftheslicecontainingvoxeli,usingthesliceaveragesaturation. Sigma and theta are surface tension and contact angle respectivelyandJ(Sw,i)isthedimensionlessJ-functiondescribedingreaterdetailinEquation5.

Wherekc is the coreaveragepermeability,φc is the coreaverageporosity, andPc(Sw,i) is the capillary pressure of the voxel i, using the voxel saturationmeasuredduringtheCTexperimentat10mL/min.

Withthe3Dpermeabilitymap,thepermeabilityofeachvoxelalongtheaxisofthecoreisthenharmonicallyaveragedtocalculatethepermeabilityofeach1Dstreamtube.ThestreamtubepermeabilityandporosityofthecoreareshowninFigure5.Theporosityandpermeabilitymapshighlightheterogeneityinthecoreintheformofbeddingplaneswhichrunparalleltotheaxisofthecore.Thesebeddingplanesvisuallyhavevariablegrainsizeandleadtolayerswithpermeabilityvaluesashighas40mDandaslowas17mD.

Figure5:Permeabilityandporosityvaluesforeachstreamtubeinthecore.

PETTracerPulseAnalysisOneofthegreateststrengthsofmicroPETimagingistheabilitytoquantitativelyvisualize tracer flow inside the core during single or multiphase experiments.Usingthefirst30minutesofthemicroPETexperimentdescribedabove,weareabletoimagethetracerpulsemigrationandpulsespreadingduetodiffusionanddispersion as the pulse of tracer travels through the core (colored circles inFigure7).Results of the PET pulse experiment can then be fit with the discrete pulsesolutiontotheAdvection-DispersionEquation[12].

WhereC0istheinjectedconcentration,vzistheaveragelinearvelocityalongeachstreamtube,zisthedistancefromtheinletofthecore,t0isthestarttimeoftracerinjection, ts is the stop time of tracer injection, and Dz is the longitudinaldispersioncoefficient.ThelongitudinaldispersionisdescribedbyDz=αvz,whereαisthetransversedispersivity.TheADEisfittothePETdatabydeterminingthedispersivity of the entire core and by determining the dispersivity of thestreamtubes.Hereweassumethedispersivityinallofthestreamtubesisequalhoweverthedispersioncoefficientsaredifferentduetodifferentlinearvelocitiesarising from permeability and porosity heterogeneity. Figure 6 shows the

solutionfortheentirecorewithadispersivityof0.27cm(dashedlines),andthesolutionforthestreamtubemodelwithadispersivityof0.11cm(solidlines).Theresults provided here are a similar to those of Pini et al [5] showing that thestreamtubemodelyieldssignificantlylowerdispersivitythanthe1-Dmodel.Thediscrepancyforboththe1-Dandstreamtubemodelfittingatearlytimeislikelydue to tracermixing in thecoreholder inletdeadvolumewhichcreatesamoredispersetracerinjectionpulse.

Figure6:Sliceaverageactivitylevelsalongthelengthofthecoreatdifferenttimesduringthepulseinjectionexperiment.PETdata,1Dadvectiondispersionsolution,andthestreamtubeadvectiondispersionsolutionareindicatedbythecircles,dashedlines,andsolidlines

respectively.

TheADEsolutionscanalsobecomparedatthevoxellevelwithresultsfromthePETtracerdata.Figure7comparestheradiotracerconcentrationinthePETwiththeADEstreamtubeandcore-averagesolutionsdownthecenterofthecore.Thevoxel-based analysis indicates that the heterogeneity in the core createssignificant transverse dispersivity that is not captured in the 1D streamtubeanalytical approximation. Fully 3D numerical simulation work is ongoing tobettervalidatethesub-corescaleporosity,permeability,anddispersivity.

Figure 7: Comparison of PET tracer migration (top), analytical streamtube tracer migration(lower-left),core-average1Danalyticaltracermigration(lowerright)after0.41PVofwaterhavebeeninjectedfollowingtracerinjection.Thetracerisinjectedfromlefttoright.Thecolorbarsofallthreeplotsrangefrom0to0.006µCi.FurtherResearchThisstudyisoneofthefirstinstancesofusingmicroPETimagingtoquantifyflowinporousmediaand is the firststudy inwhichphasesaturationmeasurementsmade using PET have been validated through repeated experiments using thewell-established X-ray CT methods for saturation measurement. One of thegreatestbenefitsofusingPETimagingistheabilitytoquantifytheflowbehaviorof tracers thatdonotmeasurablychange thepropertiesof the tracer fluid.Thedynamic imagingand increased signal tonoise ratiosofmicroPETallow in-situtracer visualization, and more importantly quantification, of both single andmultiphase flows at the continuum scale. In future studies microPET scans,complementedbyCTdata,willbeusedtofurthervalidatecorewidepermeabilitymaps,dispersionanddiffusionanalysis,andbetterunderstandthedynamicandsteadystateflowbehaviorduringdrainageandimbibitionexperiments.References

1. Levin, C.S. and Hoffman, E.J., “Calculation of positron range and its effect on thefundamentallimitofpositronemissiontomographysystemspatialresolution,”Physicsinmedicineandbiology,(1999)44,3,781-799.

2. Krause, M. H., Perrin, J. C., & Benson, S. M., “Modeling permeability distributions in asandstonecore forhistorymatchingcorefloodexperiments,”SPEJournal,(2011)16,04,768-777.

3. Pini, R., Krevor, S.C.M., Benson, S.M., “Capillary pressure and heterogeneity for theCO2/water system in sandstone rocks at reservoir conditions,” Advances in WaterResources,(2012)38,48-59.

4. Krause,M.,Krevor,S.,Benson,S.M.,“AProcedurefortheAccurateDeterminationofSub-Core Scale Permeability Distributions with Error Quantification,” Transport in PorousMedia,(2013)98,565-588.

5. Pini, R., Vandehey, N.T., Druhand, J., O’Neil, J.P., Benson, S.M., “Quantifying solutespreading and mixing in reservoir rocks using 3D PET imaging,” Journal of FluidMechanics,(2016),796,558-587.

6. Loggia, D., Gouze, P., Greswell, R., and Parker, D.J., “Investigation of the geometricaldispersion regime in a single fracture using positron emission projection imaging,”TransportinPorousMedia,(2004)55,1-20.

7. Kulenkamp,J.,Grundig,M.,Richter,M.,andEnzmann,F,“Evaluationofpositron-emission-tomography for visualisation of migration processes in geomaterials,” Physics andChemistryoftheEarth,(2008)33,14-16,937-942.

8. Ferno,M.A.,Gauteplass, J.,Hauge,L.P.,Abell,G.E.,Adamsen,T.C.H.,Graue,A., “CombinedPETandCTtovisualizedandquantifyfluidflowinsedimentaryrocks,”AdvancesinWaterResources,(2015)51,7811-7819.

9. Maucec,M.,Dusterhoft,R.,Rickman,R.,Gibson,R.,Buffler,A.,andHeerden,M.V.,“ImagingofFluidMobility inFracturedCoresUsingTime-lapsePositronEmissionTomography,”SPE166402,(2013)1-12.

10. Hudson,H.M.,Larkin,R.S., “Accelerated ImageReconstructionUsingOrderedSubsetsofProjectionData,”IEEETransactionsonMedicalImaging,(1994)13,4,601-609.

11. Akin, S. Kovscek, A.R., “Computed tomography in petroleum engineering research,”GeologicalSociety,London,SpecialPublications,(2003)215,25-38.

12. Bedient, P.B., Rifai, H.S., Newell, C.J., “Ground Water Contamination: Transport andRemediation,2ndEdition,”PrenticeHall,(1999)

SimulationofCO2ExsolutionforEnhancedOilRecoveryandCO2StorageExsolutionofCO2fromcarbonatedbrineoccurswhenporepressuresdeclineandCO2solubilitydecreases,formingafreegasphase.ThepresenceofexsolvedCO2has been shown experimentally to significantly reducewetting phasemobility.Numerical simulations of CO2 exsolution were performed to assess thepracticalityofusingCO2exsolutionasanoveloilrecoverymethod.Ingeneral,theapproachinvolvescarbonatedwaterinjectionfollowedbyCO2liberationthroughdepressurization,whichreducestherelativepermeabilityofwaterwhereCO2hasexsolved such that the sweep efficiency of subsequent water flooding isimproved.Three-phase flowat thecore-scalewassimulatedusingECLIPSE300with theCO2SOLoption inorder tocompareresults to laboratoryexperiments.The simulation results match well with experimental results, showing anincrementalrecoveryfactorof15%forexsolutioncomparedtocarbonatedwaterflood alone, which is at the upper end of the range for experimental results.Simulations of two-phase flow using TOUGH2-ECO2N were performed toevaluatewhetherfluidproductioncouldachieveexsolvedgasphasesaturationshighenoughtosignificantlyreducewettingphasemobilityatthereservoirscale.Theseresultsshowthatsufficientexsolutioncanbeachievedprovidedaconfinedreservoirorconfiningwellpatternsuchthatreservoirpressurecanbereducedthroughout the reservoir. Simulation of three-phase flow in a heterogeneousreservoir was also performed to evaluate the effect of CO2 exsolution on oilrecoveryforseveraldifferenttypesofoilunderimmiscibleconditions.Foreachcase investigated, carbonated water injection led to an enhancement in oilrecoverycomparedtowaterfloodingaloneandcarbonatedwaterinjectionwithCO2exsolutionprovidedafurtherimprovementinrecovery.Dependingontheoiltype, CO2 exsolution EOR led to an incremental recovery factor of 4 to 12%compared towater flood alone and an incremental recovery factor of 2 to 8%compared to carbonated water flood without depressurization under theconditions simulated.The resultsalso show thatparticularly forheavieroil theimmobile disconnected exsolved CO2 phase provides a useful wetting phasemobility control mechanism. Overall the simulation results indicate thatexsolutionEORcanbeaneffectiverecoverytechniquethatprovidesconcurrentCO2storage.Simulation of exsolution enhanced oil recovery was performed for awater/oil/CO2systematthereservoirscaleusingECLIPSE300withtheCO2SOLoption.Carbonatedwater injection followedbydepressurization toexsolveCO2has been shown in the laboratory to improve oil recovery. Simulations ofcarbonatedwaterinjectionandoilrecoverywereperformedusingfourdifferentoil typesunder similar injection scenarios.Theoil types arebasedon10°, 20°,30°,and40°APIoil.TheinjectionstrategyemployedforCO2exsolutionEORwasprimary recovery fromapressureof 100bar to50bar,water injection for0.4porevolume injected (PVI), carbonatedwater injection for1PVI, exsolutionbydepressurizationto35bar,andfinallycarbonatedwaterinjectionfor1PVI.

The results of the three-phase simulations show that for eachof the four casesconsidered, carbonated water injection leads to recovery enhancement ascompared to water flood alone and that carbonated water injection withdepressurization results in additional recovery enhancement. The increase inincremental recovery factors by CO2 exsolution EOR compared to water floodalone ranges from 4% for 40° API oil to 12% for 10° API oil. The incrementalrecoverywithCO2exsolutioncomparedtocarbonatedwater floodalonerangesfrom 2% for 40° API oil to 8% for 10° API oil. The finding that recoveryenhancementbyCO2exsolutionEORisgreaterwhenrecoverypriortoexsolutionislowerisconsistentwiththeexperimentalresultsdescribedpreviously.Figure1showstherecoveryfactorsofforinsituoilswithvariousAPISinfourdifferentinjectionscenarios.The main mechanisms of recovery enhancement by CO2 exsolution EOR areviscosity reduction and mobility control. Viscosity reduction during CO2exsolution EOR occurs for all of the cases considered. In the regions whereexsolution occurs and creates a free CO2 phase, oil viscosity is reduced fromapproximately1000cPto650cPfor10°APIoil,from165cPto80cPfor20°APIoil,from27cPfor16cPfor30°APIoil,andfrom4.5cPto2.8cPfor40°APIoil.Thisviscosityreductionimprovesthemobilityratioandincreasesdisplacementefficiencyof thewaterdisplacingoil. It shouldbenoted that thepressuredropalsoreducesviscosity,butforthedropusedfrom50barto35barthiseffect issmallcomparedtotheeffectfromdissolutionofCO2.The influence of mobility control on the recovery factor for each oil type wasevaluatedby comparing the recoverybyexsolutionEOR to the recoverywithamobilegasphaseresultingafterexsolution.Forthisalternativescenario,thegasphase has typical Brooks-Corey relative permeability curves for a drainageprocess. These results indicate that for heavier oil the mobility controlmechanismistheprimaryreasonforrecoveryenhancementbyexsolutionEOR,whileforlighteroilitisrelativelyinsignificant.Theabilityofthemobilitycontrolby CO2 exsolution to improve sweep efficiency is dependent on CO2 exsolutionoccurringinregionsofthereservoirwithhighwatersaturation.Whenexsolutionoccurs predominately in regions of high water saturation it forces water flowpaths to deviate from these regions to portions of the reservoir with high oilsaturation.This is observed tooccurmorenoticeably forheavier oilwhichhashigherwatercutsthanlighteroil,forwhichthesweepbywaterisalreadymoreefficient. Additionally, the mobility of CO2 is dependent on the formation of adisconnected gas phase which has been shown to occur for high nucleationfractions associated with high depressurization rates [3]. Simulations withvaryingrelativepermeabilitywereperformedforthe20°APIoilwhichshowedadecrease in incremental recovery of 9% for very high water and gas relativepermeabilitycomparedtorecoveryusingexsolutionrelativepermeabilitycurves.

Figure 1. Recovery factor versus pore volume injected for various API oil indifferent injection scenarios: exsolution EOR, exsolution of mobile CO2,carbonatedwater,andwaterflood.References

1. Zuo,Lin,andSallyM.Benson."ExsolutionenhancedoilrecoverywithconcurrentCO2sequestration."EnergyProcedia37(2013):6957-6963.

2. Zuo,L.,2014.“CO2exsolution–challengesandopportunitiesinsubsurfaceflowmanagement.”DissertationforStanfordUniversity(2014).http://purl.stanford.edu/gm445fd0429.

3. Alizadeh,A.H.,etal."Multi-scaleexperimentalstudyofcarbonatedwaterinjection:Aneffectiveprocessformobilizationandrecoveryoftrappedoil."Fuel132(2014):219-235.

API10o API20o

API40oAPI30o

Useofabove-zonepressuremonitoringdatatolocateleaksinthecaprockWhile efforts should be made to reduce the risk of leakage in carbon storageoperations; if there is a leak it will be important to quickly detect, locate andcharacterizeitinordertomaximizetheeffectivenessofremediatestrategies.Oneofthemostpromisingmethodsforearlydetectioninvolvestheuseofabove-zonepressure monitoring data. Because pressure responses in the subsurfacepropagate quickly, pressure data frommonitoringwellsmay be used to detectandcharacterizeleaksbeforeCO2hasevenreachedthem.Thegoalofthisworkisto provide an assessment for the predictability of leak locations and leakagevolumes,givendifferentnumbersofabove-zonemonitoringwells,overdifferentmonitoringtime-periods,inanuncertainheterogeneousenvironment. Several authors have analyzed the detectability of leaks from pressuremonitoring data. Notably, the works of Sun & Nicot (2012) and Sun & Nicot(2013)provideguidelineson theconditionsatwhich leaksmightbedetectablefrompressuredata inenvironmentswithdifferent levelsofmeasurementerrorand geological heterogeneity. Since locating and characterizing leaks frompressuredata is amoredifficultproblem, requiring inversion techniques, thereare fewer studies in that area. An analytical method for approximating thelocationofaleakfromabove-zonepressuredatawasproposedbyJavandeletal.(1988),thoughitpertainsonlytoleakageintoahorizontal,homogeneous,infiniteaquifer. The reliability of solutions obtained using similar methods wereinvestigated by Zeidouni & Pooladi-Darvish (2012a, 2012b) and Jung et al.(2015),thoughallthesestudiesassumedahomogeneousgeology.Expandinguponthesepreviousstudies,ourworkprovidesanassessmentforthepredictabilityof leak locationsand leakagevolumes,givendifferentnumbersofabove-zone monitoring wells, over different monitoring time-periods, in anuncertain heterogeneous environment. To perform this research, severalsyntheticleakagemodelswerecreatedtoprovide‘true’monitoringdata.Figure1illustrates thesizeof thesemodelsaswell as theconfigurationof injectionandabove-zone monitoring wells. Data assimilation (also known as inversion orhistorymatching)wasusedtopredictleakagelocationsandunderlyinggeologicparameters thatwouldmatch the observed pressure data in the ‘true’models.The efficacy of the methodology is determined by how closely the methodpredictsthelocationofandlong-termleakagevolumesthroughtheleaks.

Figure 3. Spatial model showing the size and relative locations of the CO2 injectors in

the storage formation versus monitoring wells in the overlying aquifer

Fivedifferent ‘true’modelswereconsidered inorder toevaluate theefficacyofdataassimilationusingvaryingamountsofspatialandtemporalmonitoringdata.By efficacy, we refer to the accuracy in predicting leak location, leakage fluidquantity during the (30-year) injection stage, and long-term (500-year) CO2leakagequantity.QuantityoftemporalmonitoringdataAbove-zonepressuremonitoringdatawas collected from9 verticalmonitoringwells, arranged in a 3x3 grid in the overlying aquifer (as in Figure 1). Theobservationdatawascollectedfromthe5designated‘true’modelsoveraperiodof1,2,3,6,9,12and18months.Dataassimilationwasthenusedtopredicttheleak location, among other variables, that would match the data. The distancebetween the predicted and true leak locations was averaged over the 5 truemodelsforeachleveloftemporalsampling,andisplottedinFigure2.Whenthereis no noise in the observation data, solutions that use only onemonth of datapredict leakage locations that are, on average, 1.4 gridblocks (around 600 m)fromthe true location.Additional temporalmonitoringdatadoesnotappear toimprove the data assimilation efficacy in terms of locating the leak in caseswithout noise. However, when noise is considered, the efficacy of dataassimilationimproveswiththeamountoftemporalmonitoringdata.Specifically,weseealargedecreaseinleakagelocationerror,goingfrom5.5gridblocksusingonemonthofmonitoringdata,to1.9gridblockswhenusingtwomonthsofdata,toaround1.0gridblock(436m)using12monthsofdata.

Figure 4. Average errors in leak location. Gridblocks are 436 m long.

NumberofmonitoringwellsAsimilar testwasalsoconducted todetermine theefficacyofdataassimilationwithrespecttothenumberandconfigurationofmonitoringwells.Forthesetests,we used 12 months of monitoring data from the same ‘true' models, but wevariedthenumberofmonitoringwellsusedbetween1,2,3,4and9wells.Caseswith andwithout noisewere tested, but for the purposes of the summary,wepresent the caseswith noise because those cases provided better estimates ofboth leakage location and long-term leakage volumes. Results only for leakagelocationarepresented.Figure3showsthepredictedleaklocations,‘true’leaklocations,monitoringwelllocations, and solutionuncertainty indications (grayer areas represent ahigherlikelihoodoftheleakbeinglocatedthere,basedonthepotentialdata-assimilationmatch).Acleardecreaseinerrorwithincreasingnumbersofmonitoringwellsisobserved in all cases. Our best prediction (using 9 wells with noise), has anaverageerrorinleaklocationof1.0gridblocks,whiletheaverageerrorusingonemonitoring well is close to 7 gridblocks. It appears that it might bemost costeffectivetouse3or4monitoring,aserrorsaretypicallywithinonegridblockofthebest(9well)case.Thecircularpatternsofgrayinthesinglemonitoringwellcasetendingtobeequidistantfromthemonitoringwellandroughlythedistanceofthetrueleak.Thisindicatesthatasinglewellmaybeusedtodeterminehowfarawayaleakis,thoughitcannotdeterminethedirection.Relatedresearchhasshown that additional monitoring wells (i.e., more than one) do not provideadditional improvement in predicting long-term fluid leakage volumes. Theconclusionbeingthatmultiplemonitoringwelllocationsareonlyrequiredifitisdesiredtodeterminethelocationoftheleak,andthatitspotentialseveritymaybegaugedbyasinglemonitoringwell.

Figure 5. Top view of monitoring wells and leak locations for history-matches to all five ‘true’ models using 12 months of monitoring data (with noise). Gray points indicate leak locations corresponding to the best 20% of the PSO solutions (in terms of RMSE value).

Journalarticlesandpeerreviewedbooks(publishedandinpress)Yang,F.,Hingerl,F.F.,Xiao,X.,Liu,Y.,Wu,Z.,Benson,S.M.,&Toney,M.F.(2015).

Extractionofpore-morphologyandcapillarypressurecurvesofporousmediafromsynchrotron-basedtomographydata.Scientificreports,5.

Li,B.,&Benson,S.M.(2015).Influenceofsmall-scaleheterogeneityonupwardCO2plumemigrationinstorageaquifers.AdvancesinWaterResources,83,389-404.

Krause,M.H.,&Benson,S.M.(2015).Accuratedeterminationofcharacteristicrelativepermeabilitycurves.AdvancesinWaterResources,83,376-388.

Krevor,S.,Blunt,M.J.,Benson,S.M.,Pentland,C.H.,Reynolds,C.,Al-Menhali,A.,&Niu,B.(2015).Capillarytrappingforgeologiccarbondioxidestorage–Fromporescalephysicstofieldscaleimplications.InternationalJournalofGreenhouseGasControl,40,221-237.

Niemi,A.,Bensabat,J.,Shtivelman,V.,Edlmann,K.,Gouze,P.,Luquot,L.,Benson,S.M.&Liang,T.(2016).Heletzexperimentalsiteoverview,characterizationanddataanalysisforCO2injectionandgeologicalstorage.InternationalJournalofGreenhouseGasControl.

Hingerl,F.F.,Yang,F.,Pini,R.,Xiao,X.,Toney,M.F.,Liu,Y.,&Benson,S.M.(2016).CharacterizationofheterogeneityintheHeletzsandstonefromcoretoporescaleandquantificationofitsimpactonmulti-phaseflow.InternationalJournalofGreenhouseGasControl.

Huo,D.,Pini,R.,&Benson,S.M.(2016).Acalibration-freeapproachformeasuringfractureaperturedistributionsusingX-raycomputedtomography.Geosphere,12(2),558-571.

Zahasky,C.,&Benson,S.M.(2016).Evaluationofhydrauliccontrolsforleakageinterventionincarbonstoragereservoirs.InternationalJournalofGreenhouseGasControl,47,86-100.

Huo,D.,&Benson,S.M.(2016).ExperimentalInvestigationofStress-DependencyofRelativePermeabilityinRockFractures,TransportinPorousMedia,accepted

Huo,D.,&Benson,S.M.(2015).Anexperimentalinvestigationofstress‐dependentpermeabilityandpermeabilityhysteresisbehaviorinrockfractures.DynamicsofFluidsandTransportinComplexFractured-PorousSystems,99-114.

Pini,R.,&Benson,S.M.(2015).QuantifyingHydrogeologicalHeterogeneityofRocksusingCore-Floods.InPoreScalePhenomena:FrontiersinEnergyandEnvironment(pp.243-261).WorldScientific.

ConferencePresentationsandPostersCO2StorageCapacityAssessmentsandUncertainty,BPResearchWorkshopon

CO2Storage,Sunbury,UnitedKingdomStatusandOpportunitiesinCO2CaptureandStorage,AnnualMeeting,Global

FamilyOffices,Monterey,CaliforniaImpactofOstwaldRipeningontheStabilityofTrappedCO2,Departmentof

Energy,Germantown,Maryland

InfluenceofFineScaleHeterogeneityandOstwaldRipeningonResidualGasTrapping,AnnualMeeting,Nano-ScaleControlofGeologicalCO2Storage,Berkeley,California

IdentifyingandManagingLeaksfromSubsurfaceSources,AmericanGeophysicalUnion,SanFrancisco,California

TechnologiesandPoliciesforSafeandEffectiveCO2Storage,InitiativefortheCoolEarthForum,TokyoJapan

PressureMonitoring,ContingencyPlanning,andMitigationofLeakagefromCO2StorageProjects,ResearchInstitutefortheEarth,Tokyo,Japan

CO2StorageResearch,ExxonMobil,Clinton,NewJerseyHighlightsofCO2StorageResearch,Schlumberger,Boston,Massachusetts“CO2Capture,Reuse,andStorage.”MRSEnergySummerSchool,ColoradoSchool

ofMines,ColoradoFateandTransportofCO2inDeepGeologicalSystems:ADecadeofLearning,

GordonConferenceonCO2CaptureandStorageMcLaughlin,Scott.SimulationofCO2ExsolutionforEnhancedOilRecoveryand

CO2Storage.InvitedtalkattheStanfordCenterforCarbonStorageAnnualMeeting,Stanford,May2016

Cameron,David.Detectingleaklocationsfrompressuremonitoringdataassimilation.IEAGHG10thmonitoringnetworkmeeting,LawrenceBerkeleyNationalLabs,California(May2015)

Garing,C.,DeChalendar,J.,Voltolini,M,Ajo-Franklin,J.andBenson,S.X-raymicrotomographyimagingofimmisciblefluidsafterimbibition:amulti-resolutiondataset,8thAnnualInterporeConference,Cincinnati,OH(2016)

Garing,C.,DeChalendar,J.,Voltolini,M,Ajo-Franklin,J.andBenson,S.Multi-scaleX-raymicrotomographyimagingofimmisciblefluidsafterimbibition,AGUFallMeeting,SanFrancisco(2015)

Zahasky,C.andBenson,S.Phasesaturation,capillaryheterogeneity,andtracertransportquantificationusingmicroPETinaheterogeneoussandstonecore.InternationalSymposiumoftheSocietyofCoreAnalystsheldinSnowMass,Colorado,USA,21-26(August2016)