MAR 01, 2021 11:46 AM PST

Automating the forecasting of oil and gas reserves

A study published in the journal Applied Energy describes a series of algorithms developed by Texas A&M researchers that can be used to predict subterranean oil and gas reserves. The algorithms intend to automate the process of characterizing features such as porosity and permeability of rocks in the underground environment that provide key information to quantifying natural resource reserves.

"Subsurface systems that are typically a mile below our feet are completely opaque,” explains associate professor Siddharth Misra, who teaches in the Department of Geology and Geophysics. “At that depth, we cannot see anything and have to use instruments to measure quantities, like pressure and rates of flow. 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."

With that in mind, Misra’s team developed a reinforcement-based machine-learning algorithm. This kind of machine-learning may sound familiar to anyone who has trained a pet before, for, in this system, the algorithm is rewarded for favorable predictions and penalized for unfavorable ones; this ultimately helps it to learn to make decisions based on the feedback it receives.

"Imagine a bird in a cage. The bird will interact with the boundaries of the cage where it can sit or swing or where there is food and water. It keeps getting feedback from its environment, which helps it decide which places in the cage it would rather be at a given time," Misra elaborated. "Algorithms based on reinforcement learning are based on a similar idea. They too interact with an environment, but it's a computational environment, to reach a decision or a solution to a given problem."

It turns out that this kind of reinforcement-based learning is quite effective (for pets, humans, and algorithms!). The researchers report that that within a mere 10 iterations of reinforcement learning, the algorithm was able to rapidly and accurately forecast the properties of simple subsurface environments.

"A subsurface system can have 10 or 20 boreholes spread over a two- to five-mile radius. If we understand the subsurface clearly, we can plan and predict a lot of things in advance, for example, we would be able to anticipate subsurface environments if we go a bit deeper or the flow rate of gas at that depth," Misra said. "In this study, we have turned history matching into a sequential decision-making problem, which has the potential to reduce engineers' efforts, mitigate human bias and remove the need of large sets of labeled training data."

Misra says his team hopes to continue developing these algorithms to improve their accuracy and efficiency within more complex environments. In addition to predicting oil and gas reserves, he says the algorithms can also forecast seismic hazards and groundwater reserves.

Sources: Applied Energy, Science Daily

About the Author
BA Environmental Studies
Kathryn is a curious world-traveller interested in the intersection between nature, culture, history, and people. She has worked for environmental education non-profits and is a Spanish/English interpreter.
You May Also Like
OCT 14, 2022
Technology
Charging an Electric Vehicle in Just 10 Minutes
OCT 14, 2022
Charging an Electric Vehicle in Just 10 Minutes
Electric vehicles are common and increasingly popular alternative to vehicles powered by traditional combustion engines. ...
OCT 22, 2022
Earth & The Environment
Rainfall in Majority of United States Intensifying
OCT 22, 2022
Rainfall in Majority of United States Intensifying
In a recent study published in Geophysical Research Letters, a team of researchers from Northwestern University examined ...
OCT 19, 2022
Plants & Animals
Unparalleled Levels of Insects Harming Modern Day Plants
OCT 19, 2022
Unparalleled Levels of Insects Harming Modern Day Plants
In a recent study published in the Proceedings of the National Academy of Sciences, a team of researchers led by the Uni ...
NOV 07, 2022
Technology
Scientists Examine Human-Machine Workplace Relationship
NOV 07, 2022
Scientists Examine Human-Machine Workplace Relationship
In a recent study published in Applied Ergonomics, a team of researchers from Texas A&M University (TAMU) examined t ...
NOV 10, 2022
Technology
Potholes Might Say Bye-Bye Thanks to New Machine-Learning Technique
NOV 10, 2022
Potholes Might Say Bye-Bye Thanks to New Machine-Learning Technique
In a recent study published in Engineering Structures, a team of researchers from the University of Technology Sydney ha ...
NOV 19, 2022
Health & Medicine
Artificial Light Exposure May Increase Diabetes Risk
NOV 19, 2022
Artificial Light Exposure May Increase Diabetes Risk
In the modern world, many people are exposed to artificial light at night. It's even become such a problem that dark sky ...
Loading Comments...