Webinar- BigDataAnalyticsforPetroleumEngineering:HypeorPanacea?

Presenters:

Dr.MichaelPyrcz,PetroleumandGeosystemsEngineering(moderator)Dr.LarryLake,PetroleumandGeosystemsEngineeringDr.JoydeepGhosh,ElectricalandComputerEngineering

TheProblemSettingSwimminginDataandInformationStarved!• diversedatatypes:completions,production,geological,petrophysical,geophysical• diversedatascale:poreimagingtodrainagearea• only1trillionthofthesubsurfaceislikelydirectlysampled• indirectmeasuresimprovecoverage,butatthecostofresolutionandaccuracy

ASeismicLinefortheTorokFormationClinoforms oftheLowerCretaceous,USA(seismiclinesareshotbytheUSGSandareavailableinpublicdomain,imagefromSEPM,Kendall).

TheProblemSettingComplicatedProblemsandImportantDecisions• heterogeneousrocksystem,multiphasefluids,coupledfluidflowand

geomechanical response• forecastproductionratesandprotectenvironment,healthandsafety• deliverreliableenergyandoptimalextractionofnationalresources

Fluvialreservoirmodelingwithconnectivityapproximatedbyafastmarchingsolution.Timeofflightandfractionalflowareshown.

TheProblemSettingStrategic:• Gettingmoremeaningfulinformationfromourdata?• LittleData+SimpleModel=BigData?• Improveobjectivitygiventhelargenumberofexperience-baseddecisions?• Whatistheroleofstatisticalvs.deterministicmodeling?• Howtomaximizeprofitabilityandsupportbigdataanalytics?

Tactical:• Modelingmultivariate,multiscale,spatialphenomenon.• Accountingforsamplingbiasandmissingdata.• Improvingaccuracyandflexibilityofproxymodels.• Modelingallimportantsourcesofuncertainty.• Makingoptimumdecisionsandthepresenceofuncertainty.

Model Selection

Integrating Model Uncertainties in Probabilistic Decline Curve Analysis

for Unconventional Hydrocarbon Production Forecast

5

Differenttypesofmodelshavebeenproposedforunconventionaloilproductionforecast.

Forexample

Arpsmodel:• Empirical.• Maynotbeidealforunconventional

production.

Stretchedexponentialmodel:• Empirical.• BasedontheanalysisoftheBarnett

shalewells.

PanCRM:• Analytical.• CRMcombinedwithanalytical

solutionof linearflowintofracturedwells

Logisticgrowthmodel:• Empirical.• Usedtoforecastgrowth inmany

applications.

Possiblesolution:• Anymodelisregardedasapotentiallygoodmodelwhosegoodnessisdescribedbyaprobabilityrepresentation.

• Theprobabilityofamodelisinterpretedasameasureoftherelativetruthfulnessofthemodeltotheothermodels.

• Theprobabilityisfurtherusedtoweightthemodelforecast.

Howtoassessthemodelprobability?• MaximumLikelihoodEstimation• Bayes’Theorem• MonteCarloSimulation

BayesTheorem

Themodelparametersaredeterminedbymatchingproductiondata.But,…

• Whichmodelshouldweuse?• Whatiftheycanmatchthedataalmostequallywell?

Rate

Time

Model1

Model2Model3

Model1

Model2

Model3

Aggregatedwithequalweights

Aggregatedwithupdatedweights

SchematicExample1

Probability

MeanCu

m.O

ilProd.

MidlandWellNo.29:matchoilproductionrate

31MidlandWells:matchoilproductionrate

Simple Models for Isothermal EOR Displacements

Koval model:

fsolvent =1

1+1− Csolvent( )KvCsolvent Kv = HkE

E = effective viscosity ratio Hk Heterogeneity factor

Hk = 1(homogeneous) E = 1(tracer)

Koval Model

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

FLow

cap

acity

, F

Storage capacity, C

Hk=5Hk=10Hk=20

FlowCapacityCurvesatDifferentHeterogeneityFactors

Final Bank Initial

Flow

c a&b

c

cb

a

Time

Rate

SoF SoB SoI

vΔS voB

97% oil recovery0.009 final oil saturation

Pore Volumes

Frac

tion

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Cumulative ResidualOil Recovery

Oil Fraction

Pope, et al., 2007

FractionalFlowSolution(TwoFronts)

c

b

a

Time

Rate

EL

Flow

c b a

Final

Initial

SoF SoB SoI

c

cb

a

Time

Rate

Final Bank Initial

Flow

c a&b

SoF SoB SoI

vΔS voB

FlowC=1

FinalInitial

SoFSoI

C=0

SoIInaccessible

SoB

FieldOilBankFormation

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

OilCut,fo

InjectedPV,tD

Lost Soldier Field

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.0 0.2 0.4 0.6 0.8 1.0 1.2

OilCut,fo

InjectedPV,tD

Rangely Field

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.0 0.5 1.0 1.5 2.0 2.5 3.0

OilCut,fo

InjectedPV,tD

Slaughter Pilot

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

OilCut,fo

InjectedPV,tD

Twofreds Field

CO2 ProjectResults

EstimatedVolumetricSweep

Capacitance Resistance Modeling

CRMDevelopment

• Bruce(1943)- Analogytoresistanceandcapacitance

• SPE414(1962)-Saudireservoirsimulator• CPARM

• UTPGEdozensofresearchersoveradecade

• Technicalandpeer-reviewedpapers• Casestudies (25+)• CRPOT

• Consultingprojects• LicensedtoBP

• Currentlyused• BPdevelopedwork-flowpatentsaroundthe

technology

Hypothesis

Characteristics of a reservoir can be inferred from analyzing production and

injection data only.

Howitworks…▪ Analogous toaresistancecapacitor–electricpotential(input)isconvertedtocurrentorvoltage(output)

▪ Convertsinputsignals(injectionrates)tooutputsignals(productionrates)afteratimelagandattenuation

▪ Usesthebasiccontinuityequation▪ Characterizesrelationshipsbetweenwells▪ Optimizesbasedontheeconomics

ReservoirSimulation in1962(SPE-414)

Capacitance-ResistanceModeling

τ

q(t)I(t)

t pc VJ

τ = Time constant

Solution:

( ) ( ) ( ) ( ) ( )( )1 11Δ Δ Δ

− − −τ τ τ

− −

⎛ ⎞⎜ ⎟= + − − −⎜ ⎟⎝ ⎠

t t t

k k k wf k wf kq t q t e e I t J p t p t e

t pdpc V i(t) q(t)dt

= −

wfdq(t) 1 1 dpq(t) i(t) Jdt dt

+ = −τ τ

Ordinary Differential Equation:

Continuity:( )wfq(t) J p p= −

Production Rate: “Pressure is a reservoir engineer’s best friend”Fokert Brons

CRMforaSingleInjector-ProducerPair

t pc VJ

τ =

PressureInjectionProduction

( ) ( ) ( ) ( ) ( )( )1 11t t t

k k k wf k wf kq t q t e e I t J p t p t eΔ Δ Δ

− − −τ τ τ

− −

⎛ ⎞⎜ ⎟= + − − −⎜ ⎟⎝ ⎠

11

pn

ijjf

=

≤∑

f2jf6j

f4j

f3j

f5j

f1jf11 f12

f13

I6

I1

I2

I3

I4I5 qj(t)

Drainage volume around a producer

( ) ( ) ( )11

1i

j jnt t

j k j k ij i ki

q t q t e e f I tΔ Δ− −τ τ

−=

⎛ ⎞= + −⎜ ⎟

⎝ ⎠∑

Time Constant:

where

For multiple injectors and neglecting pressure: Gain:

CRMforMultipleInjectors

-0.5

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

P001

45P0

2454

P039

08P0

3939

P039

70P0

4287

P043

23P0

4343

P043

64P0

4372

P043

80P0

4432

P044

93P0

4528

P045

53P0

4581

P046

04P0

4625

P046

55P0

4665

P047

07P0

4740

P047

80P0

4800

P048

19P0

4833

P048

46P0

4883

P049

03P0

4967

P049

95P0

5018

P050

53P0

5083

P051

00P0

5121

P051

41P0

5167

P051

81P0

5200

P052

28P0

5243

P052

53P0

5270

P052

87P0

5305

P053

20P0

5336

P053

49P0

5361

P053

83P0

5398

P054

14P0

6201

P200

98P2

0276

P205

74P2

1195

P213

47P2

1366

P214

19P2

1455

P215

60P2

1610

P216

27P2

1841

P219

03P2

1919

P219

73P2

2014

P220

23P2

2029

P220

39P2

2056

P220

66P2

2112

P221

94P2

2270

ProducerR-Squared

AverageR-Squared=0.71

VenturaField—HistoryMatchIndividualWellMatches

VenturaField,CA—ConnectivityMaps

Connectivity>10%

Connectivity>50%

Optimization—MaximizeNetPresentValue

Subjectto• CRM• Fractionalflowmodel• Upperlimitontotalinjectionrate• Upperlimitsonrateofeachinjector• Priceofoilandcostofwater• Discountrate

240

260

280

300

320

NPV Base NPV Opt

MM

$

240

260

280

300

320

Inj Base Inj Opt

MM

BBL

10.511

11.512

12.513

Oil Base Oil Opt

MM

BBL

NPV

INJ

Oil

NPV =poqoj(tk;fij,τ j)

1+ Df( )kΔt

k=1

nt∑

j=1

np∑ −

pw

1+ Df( )kIi(tk )Δt

k=1

nt∑

i=1

ni∑

12/12/17 JoydeepGhoshUT-ECE

OnData-IntensiveApproachestoComplexEngineeringandBusinessProblems

Prof.JoydeepGhosh

SchlumbergerCentennialChairedProfessorDept ofECE

Director,IDEAL(IntelligentDataExplorationandAnalysisLab)

UniversityofTexasatAustin

TheFourthParadigm(2009)

MajorParadigmsforScientificExploration&Discovery

• Empirical• Theoretical• Computational• Data-Intensive ScientificDiscovery

(butnotinKuhn’ssense)

30

• 2017:AIRules

JoydeepGhosh UT-ECE

GoingDeep

31

FromFacebook’sDeepfacepaperhttps://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf

Ourmethodreachesanaccuracyof97.35%ontheLabeledFacesintheWild(LFW)dataset,reducing theerrorofthecurrentstate oftheartbymorethan27%,closelyapproachinghuman-levelperformance.

BigDataAllows

• Complexmodelswithmanyvariables• “Automated”featureengineering,(somewhat)compensatingforlackofdomainknowledge

• Reduce”noisecomponent”• Flexible,GeneralModels

• AdaptableComplexity• Reduceuncertainty

• NewInsights

32

• Digitalexhaustà retrospectiveanalysis• Theory-freeanalysisofcorrelationsisfragile• Biasindata

• AlfredLandonvs.FDR,1936• Iftheterm“doctor”ismoreassociatedwithmenthanwomen,thenanalgorithmmightprioritizemalejobapplicantsoverfemalejobapplicantsforopenphysicianpositions.

• DifficulttoComprehend• NewemphasisonExplainableAI

• TheParableofGoogleFluTrends33

5TribesofML(FromDomingos)

MySpeciality:Multi-learnersystemsUsemultiple,complementaryapproachesformorerobustmodelingofcomplexengineeringproblems

Custommodels,where“cannedsolutions” areinadequate.

Multi-viewLearning

Multi-Task & Transfer Learning

Multitask

Transfer

Active & Semi-supervised Scenariosalso possible

Active+Multi-taskLearning(+semi-supervised+knowledgetransfer)

JoydeepGhoshUT-ECE

Active: get to optimality quicker with minimal human involvement

JoydeepGhoshUT-ECE

Field-wide Distributed Monitoring and Prediction using DXS Technology (DTS, DAS, DPS,..)

• TBofdata/day• Largenumberof(potential)applications

• 2pagelistbyDria (2012)ofjustDTS.• gasliftmonitoring/optimization,injectionprofiling,wellintegrityandmonitoring,real-timestimulationmonitoring,etc.

• Somevisualanalysisordescriptivestatistics;butlittlepredictivemodelingsofar

Revisitingexistingdata-drivenE&Papplications• "Predictingequipmentfailures"

• rockpermeabilityanditsspatialdistribution

• hydraulicfracturedesignandpost-fracturewellperformanceprediction.

• virtualsensors,e.g.syntheticMRlogs(Mohaghegh)

• SurrogateReservoirModeling(SRM),tomodelandanalyzeanenhancedcoalbedmethaneproject.

• groupofhorizontalwellplacementattributesaredefinedtocapturethelocationofhorizontalwellsinaheterogeneousreservoir.

• Seismicinversion

• ReservoirSurveillance

• WellStimulation,e.g.predictjobpausetime

• monitoringofsubseastructures,pipelines,NGpipes(size,locationofleaks)

• Shaleexplorationandproduction

CurrentPractice:LargelyBI(dashboarding),anduseofoff-the-shelfpredictionmodels)Possible:SpecializedRegression/Classification:

• 1000+variables,sparsedata,structuredoutputs, lowrank,networked info,……• Integrateheterogeneous datatypes• -

JoydeepGhoshUT-ECE

BigDatainClimate

• SatelliteData• SpectralReflectance• ElevationModels• NighttimeLights• Aerosols

• OceanographicData• Temperature• Salinity• Circulation

• Climate Models• Reanalysis Data• River Discharge• Agricultural Statistics• Population Data• Air Quality• …

Source:NASA

2016NSFBIGDATAPIMEETING 40

PatternMining:Monitoring OceanEddies• Spatio-temporal pattern miningusingnovel

multiple object trackingalgorithms• Created anopensourcedatabaseof20+yearsof

eddiesandeddytracks

ExtremesandUncertainty:Heatwaves,heavyrainfall• Extremevaluetheory inspace-timeand

dependence ofextremes oncovariates• Spatiotemporal trends inextremes and

physics-guideduncertaintyquantification

Relationshipmining:Seasonalhurricaneactivity• Statisticalmethod forautomaticinference of

modulating networks• Discoveryofkeyfactorsandmechanisms

modulating hurricane variability

SparsePredictiveModeling:Precipitation Downscaling• Hierarchical sparseregression andmulti-task

learningwith spatialsmoothing• Regionalclimatepredictions fromglobal

observations

NetworkAnalysis:Climate Teleconnections• Scalablemethod fordiscoveringrelated graph

regions• Discoveryofnovelclimateteleconnections• Alsoapplicable inanalyzingbrainfMRIdata

ChangeDetection:Monitoring EcosystemDistrubances• Robust scoringtechniques foridentifyingdiverse

changesinspatio-temporal data• Created acomprehensivecatalogueofglobalchangesin

surfacewaterandvegetation, e.g.firesanddeforestation.

Five Year, $ 10m NSF Expeditions in Computing Project (1029711, PI: Vipin Kumar, U. Minnesota)Understanding Climate Change: A Data-driven ApproachResearch Highlights

http://climatechange.cs.umn.edu/ 41

PatternMining:Monitoring OceanEddies• Spatio-temporal pattern miningusingnovel

multiple object trackingalgorithms• Created anopensourcedatabaseof20+yearsof

eddiesandeddytracks

ExtremesandUncertainty:Heatwaves,heavyrainfall• Extremevaluetheory inspace-timeand

dependence ofextremes oncovariates• Spatiotemporal trends inextremes and

physics-guideduncertaintyquantification

Relationshipmining:Seasonalhurricaneactivity• Statisticalmethod forautomaticinference of

modulating networks• Discoveryofkeyfactorsandmechanisms

modulating hurricane variability

SparsePredictiveModeling:Precipitation Downscaling• Hierarchical sparseregression andmulti-task

learningwith spatialsmoothing• Regionalclimatepredictions fromglobal

observations

NetworkAnalysis:Climate Teleconnections• Scalablemethod fordiscoveringrelated graph

regions• Discoveryofnovelclimateteleconnections• Alsoapplicable inanalyzingbrainfMRIdata

ChangeDetection:Monitoring EcosystemDistrubances• Robust scoringtechniques foridentifyingdiverse

changesinspatio-temporal data• Created acomprehensivecatalogueofglobalchangesin

surfacewaterandvegetation, e.g.firesanddeforestation.

Five Year, $ 10m NSF Expeditions in Computing Project (1029711, PI: Vipin Kumar, U. Minnesota)Understanding Climate Change: A Data-driven ApproachResearch Highlights

http://climatechange.cs.umn.edu/

Challenges• Multi-resolution, multi-scale data• High temporal variability• Spatio-temporal auto-correlation• Spatial and temporal heterogeneity• Large amount of noise and missing values• Lack of representative ground truth• Class imbalance (changes are rare events)

BigData&EarthSciences,UCSD 42

MachineIntelligenceandDecisionSystems

UT-MINDS

43

Prof. Joydeep Ghosh, UT-MINDS directorhttp://data.ece.utexas.edu

Overview§ UT-MINDSfacultyperformR&Dindatascienceandmachinelearning§Theoryandalgorithms§DeployableApplicationsbasedonreal,complexdata

§Robust,Scalable,Well-Engineeredsolutionsfor designofreliablefull-stacksystems

Data/sensors MLcore Applications

• Images/video• Signals(wireless, sensors,..)• TextandSpeech• Networkeddatasources• Databases/streams…

• DeepLearningandGANs• ReinforcementLearning• ExplainableML/AI• OptimizationandRobustML• Parallel/DistributedImplementations• Lifelong/continual learning• ModelLifecycleManagement• …

• Human-Machine interaction• Context-AwarePersonalization• MLforHealthcare• Security• Hardware/SoftwareVerification• Infrastructure:Transportation,Energy• NetworkModels• …

ThankYou!

46

JoydeepGhoshUT-ECE

What we do

• Data-DrivenModeling&KnowledgeDiscovery“BigDataPredictiveandPrescriptiveAnalytics”

• DataTypes:• relationaldatabases,distributedsensors,signals,images,web-logs,key-value….

• data (continuous+symbolic)+domainknowledge• Tools:

• Datamining/stats;webmining;machinelearning,Neuralnets,signal/imageprocessing….

• LargeScaleSystemissues• Speciality:Multi-learnersystems

• Usemultiple,complementaryapproachesformorerobustmodelingofcomplexengineeringproblems

• Custommodels,where“cannedsolutions” areinadequate.

Joydeep Ghosh UT-ECE

Neuro-Symbolic Hybrids for Knowledge Refinement

• Decision Support for LCRA Dams near Austin:• initial domain knowledge + follow-on data• Can extract refined domain knowledge!

JoydeepGhoshUT-ECE

• ML+SPfor• semi-automatingtheprocessofdetectingandcharacterizingvariouseventsofinterest

• determiningthe(spatio-temporal)resolutionofdatathatisnecessaryandsufficient

• predictingfield-widedevelopmentsandpotentialproblem-spots(withalertingmechanism)basedondetectedsignals/eventsandtheirspatio-temporalcorrelations.

• PotentialBenefits• moreaccuratemonitoringwithlesspersonnel,reducedatarequirements,andleadto(near)-realtimealertingsystemsandpost-jobdiagnostics.Maysuggesttimelyinterventions.

SampleProjectonDASAnalysis

Knowledge Pipeline:

http://www.cpge.utexas.edu/

This webinar will be posted to our website.

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