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Machine learning for detailed reservoir — Wisting case study Featured

A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description – Wisting case study

First Break
Sharareh Manouchehri, Nam Pham, Terje A. Hellem and Rocky Roden predict lithofacies and reservoir properties using multi-attribute seismic analysis based ...
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From E to P Using Machine Learning - A case study in columbia

From E to P Using Machine Learning: A Case Study in Columbia – A free webinar, May 19 2021

Geophysical Insights
Machine Learning is a disruptive technology that holds great promise, and this webinar is an interpreter’s perspective, not a data ...
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Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification

First Break
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A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal ...
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Finding the best attribute combination for seismic facies classification

2019 Oil & Gas Machine Learning Symposium
Abstract: Interpreters face two main challenges in computer-assisted seismic facies analysis. The first challenge is to define, or “label”, the ...
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Will Machine Learning “Profoundly” Change Geoscience Interpretation? An Interpreter’s Perspective

2019 Oil & Gas Machine Learning Symposium
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Abstract: Every day our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or ...
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Advantages of Machine Learning with Multi-Attribute Seismic Surveys

Geophysical Insights
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Dr. Tom Smith explains the advantages of using machine learning to analyze multiple attributes of seismic surveys simultaneously, including specific ...
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identify reservoir rock

Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

First Break
Published in the special Machine Learning edition of First Break, this paper lays out results from multi-attribute analysis using Paradise, ...
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Machine Learning Essentials Course

NEW e-Course by Dr. Tom Smith: Machine Learning Essentials for Seismic Interpretation

Geophysical Insights
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Dr. Tom Smith presents an e-course on Machine Learning Essentials for Seismic Interpretation originally hosted by the Geophysical Society of ...
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3D view of neurons

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

Unconventional Resources Technology Conference
In a paper presented at URTeC 2019, Geophysical Insights uses Paradise machine learning software to improve resolution the reservoir intervals ...
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