AI & Machine Learning
Hackathon event to analyze, prevent greenhouse gas emissions
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.
Machine-learning algorithm developed to provide accurate natural reserve forecasting
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.
Enhancing machine learning capabilities in oil and gas production
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.
Merging mapping methods to find invisible shale cracks
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.
Artificial intelligence identifies process abnormality causes
When other methods fail, AI is a critical finding and solving tool
Cloud solutions enable remote work productivity, without replacing SCADA
Use SCADA to best advantage, and the way it’s meant to be used
Flare stack monitoring made easy with edge-enabled video analytics
Real-time remote monitoring of a pressing concern in oil & gas
Drive train with AI, machine learning delivered to refinery
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.
Doing machine learning the right way
MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable and robust.
Machine learning speeds up biofuel production process development
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.