machine learning

Artificial intelligence systems can be trained to recognize visual content in drawings and provide a simplified context. The complete paper highlights the use of AI to process a scanned drawing and redrawing it on a digital platform.

Time-stamped data anomalies can lead to more-accurate identification and faster diagnosis.

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.

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.

This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights.

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.