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dc.contributor.authorZhang, Yanhui
dc.contributor.authorMazen Hittawe, Mohamad
dc.contributor.authorKatterbauer, Klemens
dc.contributor.authorMarsala, Alberto F.
dc.contributor.authorKnio, Omar
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2020-10-06T13:27:39Z
dc.date.available2020-10-06T13:27:39Z
dc.date.issued2020-09-30
dc.identifier.citationZhang, Y., Mazen Hittawe, M., Katterbauer, K., Marsala, A. F., Knio, O. M., & Hoteit, I. (2020). Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425261.1
dc.identifier.doi10.1190/segam2020-3425261.1
dc.identifier.urihttp://hdl.handle.net/10754/665465
dc.description.abstractAs more and more types of geophysical measurements informing about different characteristics of subsurface formations are available, effectively synergizing the information from these measurements becomes critical to enhance deep reservoir characterization, determine interwell fluid distribution and ultimately maximize oil recovery. In this study, we develop a feature-based model calibration workflow by combining the power of ensemble methods in data integration and deep learning techniques in feature segmentation. The performance of the developed workflow is demonstrated with a synthetic channelized reservoir model, in which crosswell seismic and electromagnetic (EM) data are jointly inverted.
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/segam2020-3425261.1
dc.rightsArchived with thanks to Society of Exploration Geophysicists
dc.titleJoint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentKing Abdullah University of Science and Technology
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.eprint.versionPost-print
dc.contributor.institutionSaudi Aramco
kaust.personZhang, Yanhui
kaust.personMazen Hittawe, Mohamad
kaust.personKnio, Omar
kaust.personHoteit, Ibrahim
refterms.dateFOA2020-10-07T05:46:33Z
dc.date.published-online2020-09-30
dc.date.published-print2020-09-30


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