: Peter Wang, Geophysical Technical Advisor
Featured Domain: Interpretation
Featured Technologies: SeisEarth
For comments or questions, please contact Peter Wang
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.
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.