Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach
dc.contributor.author | Zhang, Yanhui | |
dc.contributor.author | Mazen Hittawe, Mohamad | |
dc.contributor.author | Katterbauer, Klemens | |
dc.contributor.author | Marsala, Alberto F. | |
dc.contributor.author | Knio, Omar | |
dc.contributor.author | Hoteit, Ibrahim | |
dc.date.accessioned | 2020-10-06T13:27:39Z | |
dc.date.available | 2020-10-06T13:27:39Z | |
dc.date.issued | 2020-09-30 | |
dc.identifier.citation | Zhang, 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.doi | 10.1190/segam2020-3425261.1 | |
dc.identifier.uri | http://hdl.handle.net/10754/665465 | |
dc.description.abstract | As 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.publisher | Society of Exploration Geophysicists | |
dc.relation.url | https://library.seg.org/doi/10.1190/segam2020-3425261.1 | |
dc.rights | Archived with thanks to Society of Exploration Geophysicists | |
dc.title | Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach | |
dc.type | Conference Paper | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Earth Fluid Modeling and Prediction Group | |
dc.contributor.department | Earth Science and Engineering Program | |
dc.contributor.department | King Abdullah University of Science and Technology | |
dc.contributor.department | Physical Science and Engineering (PSE) Division | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Saudi Aramco | |
kaust.person | Zhang, Yanhui | |
kaust.person | Mazen Hittawe, Mohamad | |
kaust.person | Knio, Omar | |
kaust.person | Hoteit, Ibrahim | |
refterms.dateFOA | 2020-10-07T05:46:33Z | |
dc.date.published-online | 2020-09-30 | |
dc.date.published-print | 2020-09-30 |
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