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A scalable deep learning platform for identifying geologic features from seismic attributes

Lei Huang, Xishuang Dong and T. Edward Clee
A scalable deep learning platform for identifying geologic features from seismic attributes (in Data analytics and machine learning, Mike Davidson (editor))
Leading Edge (Tulsa, OK) (March 2017) 36 (3): 249-256

Abstract

The modern requirement for analyzing and interpreting ever-larger volumes of seismic data to identify prospective hydrocarbon prospects within stringent time deadlines represents an ongoing challenge in petroleum exploration. To provide a computer-based aid in addressing this challenge, we have developed a "big data" platform to facilitate the work of geophysicists in interpreting and analyzing large volumes of seismic data with scalable performance. We have constructed this platform on a modern distributed-memory infrastructure, providing a customized seismic analytics software development toolkit, and a Web-based graphical workflow interface along with a remote 3D visualization capability. These support the management of seismic data volumes, attributes processing, seismic analytics model development, workflow execution, and 3D volume visualization on a scalable, distributed computing platform. Early experiences show that computationally demanding deep learning methods such as convolutional neural networks (CNN) provide improved results over traditional methods such as support vector machines (SVMs) and logistic regression for identifying geologic faults in 3D seismic volumes. Our experiments show encouraging accuracy in identifying faults by combining CNN and traditional machine learning models with a variety of seismic attributes, and the platform is able to deliver scalable performance.


ISSN: 1070-485X
EISSN: 1938-3789
Serial Title: Leading Edge (Tulsa, OK)
Serial Volume: 36
Serial Issue: 3
Title: A scalable deep learning platform for identifying geologic features from seismic attributes
Title: Data analytics and machine learning
Author(s): Huang, LeiDong, XishuangClee, T. Edward
Author(s): Davidson, Mikeeditor
Affiliation: Prairie View A&M University, Department of Computer Science, Prairie View, TX, United States
Affiliation: ConocoPhillips, International
Pages: 249-256
Published: 201703
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 14
Accession Number: 2018-085179
Categories: Structural geologyApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 3 tables, sects.
N18°00'00" - N30°04'00", W98°00'00" - W80°30'00"
Secondary Affiliation: TEC Applications Analysis, 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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