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.
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.
Within the Earth’s crust, layers of rock hold bountiful reservoirs of groundwater, oil and natural gas. Now, using machine learning, researchers at Texas A&M University have developed an algorithm that automates the process of determining key features of the Earth’s subterranean environment. They said this research might help with accurate forecasting of our natural reserves.
Specifically, the researchers’ algorithm is designed on the principle of reinforcement or reward learning. Here, the computer algorithm converges on the correct description of the underground environment based on rewards it accrues for making correct predictions of the pressure and flow expected from boreholes.
“Subsurface systems that are typically a mile below our feet are completely opaque. At that depth we cannot see anything and have to use instruments to measure quantities, like pressure and rates of flow,” said Siddharth Misra, associate professor in the Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics. “Although my current study is a first step, my goal is to have a completely automated way of using that information to accurately characterize the properties of the subsurface.”
Read more at Texas A&M University
