NTNU

This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.

This paper describes a decision-support system that integrates field data, system specifications, and simulation tools to quantify system performance, forecast operational challenges, and evaluate the effect of system modifications in water management.

A contest where teams of college students design and build an automated drilling rig able to deal with hazardous obstacles in a test block, showed how a small change can be engineered to matter.