DSDE: In Theory

When engineers went searching for clues on how fractures move beneath the surface, they expected to uncover important learnings. They did not know they were on the path to a new invention.

Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take hours on even the fastest supercomputers. Now, work posted online shows how AI can easily produce emulators that can accelerate simulations by billions of times.

The complete paper explains the steps taken to improve surveillance of beam pumps using dynamometer-card data and machine-learning techniques and reviews lessons learned from executing the operator’s first artificial intelligence project.

This paper describes an accurate, three-step, machine-learning-based early warning system that has been used to monitor production and guide strategy in the Shengli field.

Many predictions have been made about what advances are expected in the field of artificial intelligence and machine learning. This column reviews a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.

This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.

The complete paper presents a process used to successfully implement a rig-based drilling advisory system (RDAS) across a mixed group of rig contractors.

The size of the digital prize is large. But deploying digital technologies at scale is proving harder than first thought. A report from Wood Mackenzie presents lessons from digitalization's early adopters.

Digitalization is now a staple of boardrooms and plays a key role in corporate strategies. It brings equal measures of opportunity and threat. A report from Wood Mackenzie takes a look at how it will affect different categories of the supply chain.

Digital twins are powerful combinations of models and data that “age” throughout the lifecycle of an asset as they gather and integrate data from the field. This technology is a quantum leap from earlier efforts at modeling complex systems.