I read an article today about how the Texas/New Mexico Permian Basin shale region is driving extreme frack sand demand. This article references an IHS Markit study indicating the value of this market has grown from $1.3B to $4B USD over the past two years. This growth also likely means that a lot of geologists, geophysicists and many others in the oil & gas business are extremely busy and could use some help.

Machine learning technology is one way to help. For those around the Permian Basin region, Emerson’s Peter Wang will co-present along with independent geologist and Vice-Chair of the Society of Independent Earth Scientists (SIPES) Fort Worth, Monte Meers, Exploring for Wolfcamp Reservoirs in Permian Basin TX, Using a Machine Learning Approach at the Fort Worth SIPES chapter September 5 luncheon ($30 to register).

Here is the abstract of the case study that Peter and Monty will present:

Voxel visualization display of the Lower and Upper Wolfcamp oil-filled facies probability cloud, Eastern Shelf of Permian Basin, Texas

One of the leading challenges in hydrocarbon exploration and production 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.

If you’re not in the area to catch this luncheon presentation, visit the Interpretation and Modeling area of the Emerson website as well as DNNA-based machine learning in the Paradigm SeisEarth application.

You can also connect in person with these oil & gas experts at the October 1-5 Emerson Exchange conference in San Antonio, Texas.