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Identify Reservoirs by Combining Machine Learning, Petrophysics, and Bi-variate Statistics

Identify Reservoirs by Combining Machine Learning, Petrophysics, and Bi-variate Statistics

Geophysical Insights
The tools of machine learning, petrophysics, well logs, and bi-variate statistics are applied in an integrated methodology to identify and ...
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A workflow to skeletonize faults and stratigraphic features Square

A workflow to skeletonize faults and stratigraphic features

Geophysics
In this paper, we introduce a 3D fault directional skeletonization workflow (Figure 1) that uses the dip magnitude and azimuth ...
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Machine Learning - New Discoveries & Reservoir Optimization

Machine Learning – New Discoveries & Reservoir Optimization

AAPG Energy Insights
Deborah Sacrey, owner of Auburn Energy, presented at the the AAPG Deep Learning TIG for a free lunch and learn ...
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Location map of the Taranaki basin, offshore west North Island, New Zealan

Which seismic attributes are best for subtle fault detection?

Interpretation Journal
By Marwa Hussein, Robert R. Stewart and Jonny Wu | Published with permission: Interpretation Journal | May 2021Download PDF Table of ...
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The Scientific Universe From Square One to SOM – April 27-28 2021

The Scientific Universe From Square One to SOM – April 27-28 2021

Geophysical Insights
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GSH-SEG Spring Symposium-Data Science and Geophysics: How Machine Learning and AI will Change Our Industry - Apr 27-28 This year ...
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2020_comparison-CNN-and-fault-enhancement

Comparing convolutional neural networking and image processing seismic fault detection methods

SEG 2020
By Jie Qi, Bin Lyu, Xinming Wu and Kurt Marfurt, | Published with permission: SEG| Oct 2020Download PDF Table of ...
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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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