Theory meets application: Machine learning techniques for geothermal exploration

phys.org | 3/1/2019 | Staff
darkkazune (Posted by) Level 3
Click For Photo: https://3c1703fe8d.site.internapcdn.net/newman/gfx/news/2019/theorymeetsa.jpg

When Jing Yang, assistant professor of electrical engineering, began looking for practical applications to her machine learning research, partnering with Chris Marone, professor of geosciences, for his work on safe and efficient geothermal exploration and energy production, was a perfect fit.

Yang and Marone were recently awarded a 2019 Penn State Multidisciplinary Seed Grant for their collaborative research "Machine learning approaches for safe geothermal exploration."

Machine - Learning - Number - Years - Yang

"I've been working on machine learning for a number of years," said Yang. "My research is more on the theoretical side, and I want to show how theory can be related to practice. Energy-related applications could be the place where machine learning techniques can manifest a great impact."

The work aims to use machine learning both to better predict seismic activity during geothermal exploration and to optimize geothermal energy production.

Systems - Creation - Fractures - Stimulation - Fracture

Geothermal systems require the creation of fractures through hydraulic stimulation. This fracture formation and stimulation is associated with microearthquakes (MEQs) that can damage buildings and other surface structures. Marone and Yang hope that by using Yang's machine learning (ML) algorithms they will be able to forecast and predict seismic events such as MEQs.

"We are very interested in whether certain precursors exist for microearthquakes so that we can predict when a major seismic activity is going to happen in the near future, upon which some immediate actions can be taken before anything destructive happens," said Yang.

Component - Research - Ability - ML - Algorithms

A critical component to this research is the ability of ML algorithms to predict this seismic activity on a large scale. The researchers currently have had success with gathering data and forecasting seismic activity in the lab, but they need to ensure that they can make these predictions at field scale.

"If you have thousands of sensors generating measurements in a streaming fashion, analyzing...
(Excerpt) Read more at: phys.org
Wake Up To Breaking News!
Sign In or Register to comment.

Welcome to Long Room!

Where The World Finds Its News!