machine learning

This paper presents a fatigue-prediction methodology designed to extend the life of unbonded flexible risers and improve the accuracy of floating production, storage, and offloading vessel response analysis.

Increasing accuracy in models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature-scaling methods of standardization and normalization and demonstrates when and how to apply each approach.

There is often an assumption that big data, together with machine learning, will solve whatever problems asset-heavy industries such as oil and gas face. This is not the case; big data alone isn’t enough. We need something else to solve these problems, and the answer lies in the world of physics.

In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection.

This paper discusses a prescriptive analytics framework to optimize completions in the Permian Basin.

MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.

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.

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.

Using the supplied data set of cone penetration test results, competing teams had to predict the number of hammer blows required to drive the pile a given unit of depth in the North Sea.

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.