Resource Library

2020_comparison-CNN-and-fault-enhancement

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 webinar web banner

Unsupervised Machine Learning Applied to Direct-P and Converted-P Data – a free webinar, 17/18 February 2021

Geophysical Insights
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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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Internal architecture of Permian Basin turbidites Thumbnail

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 Squared Thumbnail

A Tale of Two Reservoirs: How Machine Learning can Help Define “Sweet Spots” in Conventional and Unconventional Reservoirs

2020 Oil & Gas Machine Learning Symposium
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In the past, SOM has been used on only one attribute at a time using the seismic wavelet as the ...
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Wisting Case study Presentation Square Thumbnail

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 Squared Thumbnail

Application of Unsupervised Machine Learning for 3D Seismic, Pliocene Turbidities, Offshore Nile Delta

2020 Oil & Gas Machine Learning Symposium
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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 Square Thumbnail

Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine Learning

SEG 2020
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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 Square Thumbnail

Finding Hydrocarbons using SOM Classifications

SEG 2020
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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 Square Thumbnail

Calibrating SOM Results to Wells – Improving Stratigraphic Resolution in the Niobrara

SEG 2020
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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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