By Rocky Roden | May 2020 Introduction to Machine Learning for Interpreters ● Why Machine Learning now? ● Address terminology confusion ● Types of Machine Learning ● Case studies ● Machine Learning and the “Black Box” connotation ● Machine Learning and compute power ● Future trends   How does Machine Learning Relate to Finding Oil …

What Interpreters Should Know About Machine Learning Read More »

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Abstract: Interpreters face two main challenges in computer-assisted seismic facies analysis. The first challenge is to define, or “label”, the facies of interest. The second challenge is to select a suite of attributes that can differentiate target facies from each other and from the background reflectivity. Accurately defining the seismic expression of a given seismic …

Finding the best attribute combination for seismic facies classification Read More »

Abstract: Every day our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning.  Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how does this technology relate to …

Will Machine Learning “Profoundly” Change Geoscience Interpretation? An Interpreter’s Perspective Read More »

Dr. Tom Smith shares the “Holy Grail” of Machine Learning in Seismic Interpretation with the Geophysical Society of Houston.

Dr. Tom Smith presents “Geobodies in Paradise: a Machine Learning Application” at the 2018 SEG Convention in Anaheim, California. Dr. Smith explains four geobody examples including Golden 3D Survey Geobodies, Eagle Ford Sweet Spot Predictions, Niobrara Sweet Spot Predictions and Stratton Field Strategraphic Fabric by Geobody Shape Classification.

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Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning technologies is helping solve problems.

Compare traditional seismic interpretation results with SOM (Self-Organizing Maps) classification achieved with machine learning in Paradise software.

Dr. Kurt Marfurt, Principal Investigator at the AASPI Consortium at the University of Oklahoma, shares insights on “Attribute Selection: Machine Learning vs. Interactive Interpretation.”

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