Rocky Roden

Rocky Roden Square Medium 500 px

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
Unsupervised Machine Learning Techniques for Subtle Thumbnail

Unsupervised Machine Learning Techniques for Subtle Fault Detection

In this paper, authors suggest a workflow that enables interpreters to apply principal component analysis (PCA) and self- organizing maps ...
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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

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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identify reservoir rock

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

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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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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wiggle trace seismic data

Significant Advancements in Seismic Reservoir Characterization with Machine Learning

Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics ...
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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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Conventional Stacked Seismic Amplitude Display

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 figure 10

Seismic Interpretation Below Tuning with Multi-attribute Analysis

Seismic interpretation of thin beds below tuning has always been a challenge in the oil and gas industry. A multi-attribute ...
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Seismic Attributes for Attenuation

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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SOM results defining hydrocarbon contacts

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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