Enhancing Machine-Learning Capabilities In Oil And Gas Production


A Texas A&M researcher is participating in a multi-university and national laboratory project to create an advanced system that understands and renders production processes and conditions as they happen.

Both a machine-learning algorithm and an engineer can predict if a bridge is going to collapse when they are given data that shows a failure might happen. Engineers can interpret the data based on their knowledge of physics, stresses and other factors, and state why they think the bridge is going to collapse. Machine-learning algorithms generally can’t give an explanation of why a system would fail because they are limited in terms of interpretability based on scientific knowledge.

Since machine-learning algorithms are tremendously useful in many engineering areas, such as complex oil and gas processes, Petroleum Engineering Professor Akhil Datta-Gupta is leading Texas A&M University’s participation in a multi-university and national laboratory project to reduce this limitation. The project began Sept. 2 and was initially funded by the U.S. Department of Energy (DOE). He and the other participants will inject science-informed decision-making into machine-learning systems, creating an advanced evaluation system that can assist with the interpretation of reservoir production processes and conditions while they happen.

Hydraulic fracturing operations are complex. Data is continually recorded during production processes so it can be evaluated and modeled to simulate what happens in a reservoir during the injection and recovery processes. However, these simulations are time-consuming to make, meaning they are not available during production and are more of a reference or learning tool for the next operation.

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