Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach

Yanhui Zhang, Mohamad Mazen Hittawe, Klemens Katterbauer, Alberto F. Marsala, Omar Knio, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2020
PublisherSociety of Exploration Geophysicists
DOIs
StatePublished - Oct 1 2020

Fingerprint

Dive into the research topics of 'Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach'. Together they form a unique fingerprint.

Cite this