Multi Data Reservoir History Matching using the Ensemble Kalman Filter

Handle URI:
http://hdl.handle.net/10754/555580
Title:
Multi Data Reservoir History Matching using the Ensemble Kalman Filter
Authors:
Katterbauer, Klemens ( 0000-0003-0931-8843 )
Abstract:
Reservoir history matching is becoming increasingly important with the growing demand for higher quality formation characterization and forecasting and the increased complexity and expenses for modern hydrocarbon exploration projects. History matching has long been dominated by adjusting reservoir parameters based solely on well data whose spatial sparse sampling has been a challenge for characterizing the flow properties in areas away from the wells. Geophysical data are widely collected nowadays for reservoir monitoring purposes, but has not yet been fully integrated into history matching and forecasting fluid flow. In this thesis, I present a pioneering approach towards incorporating different time-lapse geophysical data together for enhancing reservoir history matching and uncertainty quantification. The thesis provides several approaches to efficiently integrate multiple geophysical data, analyze the sensitivity of the history matches to observation noise, and examine the framework’s performance in several settings, such as the Norne field in Norway. The results demonstrate the significant improvements in reservoir forecasting and characterization and the synergy effects encountered between the different geophysical data. In particular, the joint use of electromagnetic and seismic data improves the accuracy of forecasting fluid properties, and the usage of electromagnetic data has led to considerably better estimates of hydrocarbon fluid components. For volatile oil and gas reservoirs the joint integration of gravimetric and InSAR data has shown to be beneficial in detecting the influx of water and thereby improving the recovery rate. Summarizing, this thesis makes an important contribution towards integrated reservoir management and multiphysics integration for reservoir history matching.
Advisors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Sun, Shuyu ( 0000-0002-3078-864X )
Committee Member:
Schuster, Gerard T.; Jonsson, Sigurjon ( 0000-0001-5378-7079 ) ; Marsala, Alberto
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Program:
Earth Sciences and Engineering
Issue Date:
May-2015
Type:
Dissertation
Appears in Collections:
Dissertations; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoteit, Ibrahimen
dc.contributor.advisorSun, Shuyuen
dc.contributor.authorKatterbauer, Klemensen
dc.date.accessioned2015-05-24T06:56:49Zen
dc.date.available2015-05-24T06:56:49Zen
dc.date.issued2015-05en
dc.identifier.urihttp://hdl.handle.net/10754/555580en
dc.description.abstractReservoir history matching is becoming increasingly important with the growing demand for higher quality formation characterization and forecasting and the increased complexity and expenses for modern hydrocarbon exploration projects. History matching has long been dominated by adjusting reservoir parameters based solely on well data whose spatial sparse sampling has been a challenge for characterizing the flow properties in areas away from the wells. Geophysical data are widely collected nowadays for reservoir monitoring purposes, but has not yet been fully integrated into history matching and forecasting fluid flow. In this thesis, I present a pioneering approach towards incorporating different time-lapse geophysical data together for enhancing reservoir history matching and uncertainty quantification. The thesis provides several approaches to efficiently integrate multiple geophysical data, analyze the sensitivity of the history matches to observation noise, and examine the framework’s performance in several settings, such as the Norne field in Norway. The results demonstrate the significant improvements in reservoir forecasting and characterization and the synergy effects encountered between the different geophysical data. In particular, the joint use of electromagnetic and seismic data improves the accuracy of forecasting fluid properties, and the usage of electromagnetic data has led to considerably better estimates of hydrocarbon fluid components. For volatile oil and gas reservoirs the joint integration of gravimetric and InSAR data has shown to be beneficial in detecting the influx of water and thereby improving the recovery rate. Summarizing, this thesis makes an important contribution towards integrated reservoir management and multiphysics integration for reservoir history matching.en
dc.language.isoenen
dc.subjectHistory Matchingen
dc.subjectEnKFen
dc.subjectGeophysical Dataen
dc.subjectForecastingen
dc.subjectUncertainty Quantificationen
dc.titleMulti Data Reservoir History Matching using the Ensemble Kalman Filteren
dc.typeDissertationen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberSchuster, Gerard T.en
dc.contributor.committeememberJonsson, Sigurjonen
dc.contributor.committeememberMarsala, Albertoen
thesis.degree.disciplineEarth Sciences and Engineeringen
thesis.degree.nameDoctor of Philosophyen
dc.person.id118395en
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