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Exploring for Wolfcamp reservoirs, Eastern Shelf of the Permian Basin, Texas, using a Machine Learning approach @ Emerson Houston TX

This invitation-only lunch and learn for influential scientists, engineers, and managers involved in Permian Oil Exploration and Production will be held on June 28, 2018, at Emerson, 6005 Rogerdale Road, Houston, Texas 77072. If you would like to receive an invitation link, please send an email to from your corporate email address by Tuesday, June 26. You must be registered in order to pass through security.

Abstract: One of the leading challenges in hydrocarbon exploration and production is predicting rock types & fluid content distribution throughout the reservoir away from the boreholes. We have applied an Emerson 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, USA. This project was executed for Halliburton Operating Company, Dallas, TX.

The DNNA method is trained using facies logs from wells simultaneously with seismic data. The principle of DNNA is to combine several neural networks, all with different learning strategies, to segment the seismic data space, according to the facies interpretation provided by geologists or petrophysicists. DNNA thereby provides a convenient bridge between finer scale geology / petrophysics interpretations and coarser scale seismic data which also does not inherently possess geological information.  The DNNA algorithm searched through fifteen 3D seismic volumes simultaneously, and was able to build a model which reconstructed the nine lithofacies. The oil-filled packstones facies with no false positives or false negatives seen at the wells. The seismic volumes were: Hi-Res time migrated, Instantaneous Frequency, Instantaneous Q Factor, Inversion (P-Impedance), Semblance, Dominant Frequency, Most negative curvature, and eight angle stacks: 0-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40 degree ranges.

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 identified from the seismic facies thickness map or the facies probability voxel clouds. The bootstrap classification rate, an alternative to total well replacement, was 80%, indicating good prediction quality.


* Emerson E&P Software
1 Independent Geologist
2 Independent Geophysicist
3 Reservoir Geophysicist, Hardin International, 5300 Democracy Dr, Suite 100, Plano, TX 75024
4 Geophysical Technician, RAM Imaging Technology