DSDE: In Practice

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

This paper describes a smart-tracer-portfolio testing and design solution for multistage hydraulic fracturing that will, write the authors, enable operators to reduce operating cost significantly and optimize production in shale wells.

The complete paper demonstrates the benefits of honoring data measurements from a multitude of potential sources to help engineers do a better job of including more diagnostics into routine operations to provide additional insight and result in improved models and completion designs.

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

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

The complete paper presents a discussion of the use of intelligent well completion in Santos Basin Presalt Cluster wells.

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.

A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream.

AltaML has announced a partnership with engineering and design firm Kleinfelder in which the two companies will pair 3D reality scans of facilities with artificial intelligence to look for potential problems and risks.

Researchers with the National Center for Airborne Laser Mapping at the University of Houston are creating a set of algorithms that would allow users to more-precisely align data sets collected at different times and reliably estimate changes between images captured at different times.