Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification

Handle URI:
http://hdl.handle.net/10754/565998
Title:
Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification
Authors:
Katterbauer, Klemens; Arango, Santiago; Sun, Shuyu ( 0000-0002-3078-864X ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Reservoir simulations and history matching are critical for fine-tuning reservoir production strategies, improving understanding of the subsurface formation, and forecasting remaining reserves. Production data have long been incorporated for adjusting reservoir parameters. However, the sparse spatial sampling of this data set has posed a significant challenge for efficiently reducing uncertainty of reservoir parameters. Seismic, electromagnetic, gravity and InSAR techniques have found widespread applications in enhancing exploration for oil and gas and monitoring reservoirs. These data have however been interpreted and analyzed mostly separately, rarely exploiting the synergy effects that could result from combining them. We present a multi-data ensemble Kalman filter-based history matching framework for the simultaneous incorporation of various reservoir data such as seismic, electromagnetics, gravimetry and InSAR for best possible characterization of the reservoir formation. We apply an ensemble-based sensitivity method to evaluate the impact of each observation on the estimated reservoir parameters. Numerical experiments for different test cases demonstrate considerable matching enhancements when integrating all data sets in the history matching process. Results from the sensitivity analysis further suggest that electromagnetic data exhibit the strongest impact on the matching enhancements due to their strong differentiation between water fronts and hydrocarbons in the test cases.
KAUST Department:
Earth Science and Engineering Program
Publisher:
Elsevier BV
Journal:
Journal of Petroleum Science and Engineering
Issue Date:
Apr-2015
DOI:
10.1016/j.petrol.2015.02.016
Type:
Article
ISSN:
09204105
Sponsors:
The work presented in this paper has been supported in part by the project entitled Simulation of Subsurface Geochemical Transport and Carbon Sequestration, funded by the GRP-AEA Program at King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Articles; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorKatterbauer, Klemensen
dc.contributor.authorArango, Santiagoen
dc.contributor.authorSun, Shuyuen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-12T08:58:44Zen
dc.date.available2015-08-12T08:58:44Zen
dc.date.issued2015-04en
dc.identifier.issn09204105en
dc.identifier.doi10.1016/j.petrol.2015.02.016en
dc.identifier.urihttp://hdl.handle.net/10754/565998en
dc.description.abstractReservoir simulations and history matching are critical for fine-tuning reservoir production strategies, improving understanding of the subsurface formation, and forecasting remaining reserves. Production data have long been incorporated for adjusting reservoir parameters. However, the sparse spatial sampling of this data set has posed a significant challenge for efficiently reducing uncertainty of reservoir parameters. Seismic, electromagnetic, gravity and InSAR techniques have found widespread applications in enhancing exploration for oil and gas and monitoring reservoirs. These data have however been interpreted and analyzed mostly separately, rarely exploiting the synergy effects that could result from combining them. We present a multi-data ensemble Kalman filter-based history matching framework for the simultaneous incorporation of various reservoir data such as seismic, electromagnetics, gravimetry and InSAR for best possible characterization of the reservoir formation. We apply an ensemble-based sensitivity method to evaluate the impact of each observation on the estimated reservoir parameters. Numerical experiments for different test cases demonstrate considerable matching enhancements when integrating all data sets in the history matching process. Results from the sensitivity analysis further suggest that electromagnetic data exhibit the strongest impact on the matching enhancements due to their strong differentiation between water fronts and hydrocarbons in the test cases.en
dc.description.sponsorshipThe work presented in this paper has been supported in part by the project entitled Simulation of Subsurface Geochemical Transport and Carbon Sequestration, funded by the GRP-AEA Program at King Abdullah University of Science and Technology (KAUST).en
dc.publisherElsevier BVen
dc.subject4D reservoir monitoringen
dc.subjectEnsemble Kalman Filteren
dc.subjectHistory matchingen
dc.subjectSensitivity analysisen
dc.titleMulti-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantificationen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalJournal of Petroleum Science and Engineeringen
kaust.authorKatterbauer, Klemensen
kaust.authorSun, Shuyuen
kaust.authorHoteit, Ibrahimen
kaust.authorArango, Santiagoen
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