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Texas A&M researchers design reinforcement-based system that automates prediction of subsurface environments

Posted at 7:24 PM, Feb 26, 2021
and last updated 2021-02-26 20:24:51-05

Researchers at Texas A&M University have created a new way to predict the amount of natural resources.

Texas A&M University researchers have designed a reinforcement-based algorithm that automates the process of predicting the properties of the underground environment, facilitating the accurate forecasting of oil and gas reserves.

The technology would be put into the drilling well bores that contain data censors which sends messages to each other to better map underground oil and gas reserves.

By introducing this algorithm, A&M experts hope for better predictions of where to drill for natural resources.

Simulating the geology of the underground environment can greatly facilitate forecasting of oil and gas reserves, predicting groundwater systems and anticipating seismic hazards. Depending on the intended application, boreholes serve as exit sites for oil, gas and water or entry sites for excess atmospheric carbon dioxide that need to be trapped underground.

"Once you understand how these things are connected, you can then plan where to drill next. You can then plan how can you maintain and manage pressure. You can improve the recovery of the oil," said Siddharth Misra, associate professor in the Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics.

The researchers found that within 10 iterations of reinforcement learning the algorithm was able to correctly and very quickly predict the properties of simple subsurface scenarios.

The algorithm is described in the December issue of the journal Applied Energy.