Dr. Tom Smith presenting on Machine Learning at the 3D Seismic Symposium on March 6th in Denver
What is the "holy grail" of Machine Learning in seismic interpretation? by Dr. Tom Smith, GSH Luncheon 2018
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning

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Advances in Machine Learning for Reservoir Characterization by Rocky Roden

Webinar: Advances in Machine Learning for Reservoir Characterization

By: Rocky Roden, Sr. Consulting Geophysicist for Geophysical Insights

 

Webinar Outline: 
   - Welcome Note - Klaus Soffried, Moderator
   - Advances of Machine Learning in Reservoir Characterization - Rocky Roden, Consulting Geophysicist
        - Introduction to Paradise
        - Overview of Machine Learning interpretation workflow
        - Case Studies highlighting:
             - Thin beds below conventional tuning
             - Detailed facies analysis
             - Direct Hydrocarbon Indicators (DHIs) related to low probability anomalies
        - Summary and Conclusions


 Rocky Roden talks about the impact of Machine Learning and Multi-Attribute Analysis on Reservoir Characterization.

Rocky Roden
Senior Consulting Geophysicist | Geophysical Insights

Rocky R. Roden has extensive knowledge of modern geoscience technical approaches (past Chairman-The Leading Edge Editorial Board).  As former Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia.  He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East.  Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco.  He holds a B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University.

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Interpretation Below Seismic Tuning Using Multi-attribute Analysis

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Interpretation Below Seismic Tuning Using Multi-attribute Analysis

Earlier this week, Rocky Roden's presentation on "Interpreting Below Seismic Tuning Using Multi-Attribute Analysis" garnered quite the buzz.  Geosicentists across the globe learned of new processes in visualizing thin beds and facies with machine learning technology.  In the presentation, Mr. Roden covered topics such as: 

  • Rayleigh's Criterion and the classical basis of seismic tuning

  • Work by Brown et al. (1984,1986) and Connolly (2007) on thin bed calculations

  • Phenomena at or below tuning

  • Applications of attributes to the wedge model

  • How multi-attribute classification techniques that use machine learning enable visualization below tuning

  • Case studies in the application of this new technique in conventional and unconventional geologic settings


Rocky R. Roden has extensive knowledge of modern geoscience technical approaches (past Chairman-The Leading Edge Editorial Board).  As former Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia.  He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East.  Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco.  He holds a B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University.

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Eagle Ford Formation Case Study

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Eagle Ford Formation Case Study

Latest Technology for Seismic Interpretation: Direct detection & delineation of facies architecture in the Eagel Ford Group or How did the Eagle Ford GP get Made? A presentation by Patricia Santogrossi at the 2016 SEG Annual Convention.

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An introduction to Paradise 3.0

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An introduction to Paradise 3.0

Paradise is multi-attribute analysis software that uses machine learning processes to extract more information from the seismic response, even below seismic resolution. Using Paradise, interpreters are able to analyze multiple attributes simultaneously and calibrate results to wells quickly.

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