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Automated fault detection without seismic processing

Mauricio Araya-Polo, Taylor Dahlke, Charlie Frogner, Chiyuan Zhang, Tomaso Poggio and Detlef Hohl
Automated fault detection without seismic processing (in Data analytics and machine learning, Mike Davidson (editor))
Leading Edge (Tulsa, OK) (March 2017) 36 (3): 208-214

Abstract

For hydrocarbon exploration, large volumes of data are acquired and used in physical modeling-based workflows to identify geologic features of interest such as fault networks, salt bodies, or, in general, elements of petroleum systems. The adjoint modeling step, which transforms the data into the model space, and subsequent interpretation can be very expensive, both in terms of computing resources and domain-expert time. We propose and implement a unique approach that bypasses these demanding steps, directly assisting interpretation. We do this by training a deep neural network to learn a mapping relationship between the data space and the final output (particularly, spatial points indicating fault presence). The key to obtaining accurate predictions is the use of the Wasserstein loss function, which properly handles the structured output - in our case, by exploiting fault surface continuity. The promising results shown here for synthetic data demonstrate a new way of using seismic data and suggest more direct methods to identify key elements in the subsurface.


ISSN: 1070-485X
EISSN: 1938-3789
Serial Title: Leading Edge (Tulsa, OK)
Serial Volume: 36
Serial Issue: 3
Title: Automated fault detection without seismic processing
Title: Data analytics and machine learning
Affiliation: Shell International Exploration and Production, International
Affiliation: ConocoPhillips, International
Pages: 208-214
Published: 201703
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 17
Accession Number: 2018-085173
Categories: Structural geologyApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table
Secondary Affiliation: Massachusetts Institute of Technology, USA, United States
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201847
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