AI & Machine Learning
The Microsoft Energy Core Methane Emissions Hackathon virtual event is designed to overlay oil and gas asset and geographic information system data to pinpoint leak location.
Texas A&M researchers have developed an algorithm that automates the process of determining key features of the Earth’s subterranean environment such as groundwater, oil and natural gas.
Enhanced machine-learning systems developed by Texas A&M researchers can quickly compress data so they can render how fluid movements change during production processes.
A student researcher at Texas A&M is using the combination of electrical currents, called electromagnetics, and data from tightly-focused microseismic measurements to accurately render existing natural fracture networks in shale rock.
When other methods fail, AI is a critical finding and solving tool
Use SCADA to best advantage, and the way it’s meant to be used
Real-time remote monitoring of a pressing concern in oil & gas
Siemens Large Drive Applications delivered a drive train, which includes artificial intelligence and machine learning to Deer Park Refinery located in Houston, Tex., one of the nation's largest refineries.
MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable and robust.
Researchers at UW-Madison have found a way to speed up the process of finding suitable reaction conditions using machine learning, which may help the era of biofuels come a little bit sooner.