
Sr. Consulting Geophysicist
Rocky R. Roden owns his own consulting company, Rocky Ridge Resources Inc., and works with several oil companies on technical and prospect evaluation issues. He also is a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. He is a proven oil finder (36 years in the industry) with extensive knowledge of modern geoscience technical approaches (past Chairman – The Leading Edge Editorial Board). As 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 U.S.A., 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 BS in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES.
Published Work by Rocky Roden
Advanced Trends in Machine Learning for Seismic Fault Delineation
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Unsupervised Machine Learning Techniques for Subtle Fault Detection
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What Interpreters Should Know About Machine Learning
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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
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Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale
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Making Sense of Machine Learning
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning
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Seismic Interpretation of DHI Characteristics with Machine Learning
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Interpretation of DHI Characteristics with Machine Learning
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Seismic Interpretation Below Tuning with Multi-attribute Analysis
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