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Rocky Roden

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:

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Making Sense of Machine Learning

Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning

The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir ...
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Seismic Interpretation of DHI Characteristics with Machine Learning

The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this ...
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Interpretation of DHI Characteristics with Machine Learning

Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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Seismic Interpretation Below Tuning with Multiattribute Analysis

Seismic Interpretation Below Tuning with Multiattribute Analysis

Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of ...
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Seismic Interpretation Below Tuning with Multi-Attribute Analysis

This international webinar describes how multi-attribute seismic analysis is applied using the Paradise software to visualize thin beds and facies ...
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Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with ...
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Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with ...
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