Resource Library
Comparing convolutional neural networking and image processing seismic fault detection methods
AAPG Energy Insights
By Jie Qi, Bin Lyu, Xinming Wu and Kurt Marfurt, | Published with permission: Society of Exploration Geophysicists (SEG)| October ...
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Unsupervised Machine Learning Applied to Direct-P and Converted-P Data – a free webinar, 17/18 February 2021
Geophysical Insights
This webinar features a 45-minute presentation by Dr. Bob Hardage (CV below), a researcher and proponent of the application of ...
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Fabric and internal architecture of Permian Basin turbidites indicated by unsupervised machine learning analysis of P-P and SV-P images
Interpretation Journal
By Bob Hardage, Tom Smith, Diana Sava, Yi Wang, Rocky Roden, Gary Jones, and Sarah Stanley | Published with permission: Interpretation ...
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A Tale of Two Reservoirs: How Machine Learning can Help Define “Sweet Spots” in Conventional and Unconventional Reservoirs
2020 Oil & Gas Machine Learning Symposium
In the past, SOM has been used on only one attribute at a time using the seismic wavelet as the ...
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A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description – Wisting case study Presentation
2020 Oil & Gas Machine Learning Symposium
A multidisciplinary approach that is maximizing information extraction from seismic to predict lithofacies and reservoir properties, based on the following steps is presented: Multi-attribute ...
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Application of Unsupervised Machine Learning for 3D Seismic, Pliocene Turbidities, Offshore Nile Delta
2020 Oil & Gas Machine Learning Symposium
Application of the unsupervised Machine learning using SOM clearly demonstrate the strike and geomorphology of the Pliocene marine turbidities. The ...
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Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine Learning
SEG 2020
Examination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric ...
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Finding Hydrocarbons using SOM Classifications
SEG 2020
The key to this presentation is showing examples of how the SOM classification process has led to hydrocarbon discoveries in ...
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Calibrating SOM Results to Wells – Improving Stratigraphic Resolution in the Niobrara
SEG 2020
By using statistical tools such as Attribute Selection, which uses Principal Component Analysis (PCA), and Multi-Attribute Classification using Self Organizing ...
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