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Exploring for Wolfcamp Reservoir in the Permian Basin, Using a Machine Learning Approach

July 19, 2018
Broadcast time: 12:00 Noon CT
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Presented by:  Peter Wang, Geophysical Technical Advisor
Featured Domain:  Interpretation
Featured Technologies:  SeisEarth

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For comments or questions, please contact Peter Wang.

Abstract

One of the leading challenges in hydrocarbon E&P is predicting rock types and fluid content distribution throughout the reservoir away from the boreholes. In this presentation, we will demonstrate the application of a neural network based machine learning methodology called Democratic Neural Network Association (DNNA) to the problem of finding oil-filled packstones in the Middle Wolfcamp, Eastern Shelf of the Permian Basin, Texas. The DNNA algorithm searched through fifteen 3D seismic volumes simultaneously, and was able to build a model which reconstructed the nine lithofacies. No evidence was seen of false positive or false negative predictions at the wells for the oil-filled packstone facies. The neural network learnings were applied through the 3D survey, and results were delivered with up to a 0.5 ms two-way time vertical resolution, or about 5 ft, a significant uplift from conventional seismic resolution. Lateral resolution was also improved. Additional drilling opportunities can be identified from the seismic facies thickness map or the facies probability voxel clouds.

Biography

Peter-Wang_sm.jpgPeter Wang is a Geophysical Technical Advisor at Emerson E&P Software Solutions. He has a BS Degree in Geosciences from Brown University, and an MS in Geophysics and MBA from the University of Houston. He has a thirty-year history in the geophysical industry, having also served at Schlumberger as a Principal Geophysicist, Product Champion, and Workflow Champion, and Amoco Production Company (now BP) as a Senior Petroleum Geophysicist onshore USA Gulf Coast and Gabon.