Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach
Mazen Hittawe, Mohamad
Marsala, Alberto F.
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
King Abdullah University of Science and Technology
Physical Science and Engineering (PSE) Division
Online Publication Date2020-09-30
Print Publication Date2020-09-30
Permanent link to this recordhttp://hdl.handle.net/10754/665465
MetadataShow full item record
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.
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
PublisherSociety of Exploration Geophysicists