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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