machine learning

Machine learning enables fast, cost-effective, and accurate methane emissions detection in remote areas.

Despite streams of data being available on platforms about the condition of topside and drilling equipment, most experts agree that only a small fraction of such data is used. Whether for a fleet or single platform, AI can transform an offshore enterprise.

An AI-based application enabled operators to preempt ESP failures while optimizing production.

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.