DSDE: In Theory

This paper examines how data-science-driven work practices can result in substantial reductions in methane emissions compared with other leak-detection and -repair methods.

AI can transform our work, but it demands the highest accuracy. Anything less than perfect in oil and gas and other heavy-asset industries is unacceptable.

Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.

This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.

This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.

This paper describes a new application that leverages advanced machine-learning techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads.

This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.

This paper proposes a holistic, automatic, and real-time characterization of cuttings/cavings, including their volume, size distribution, and shape/morphology, while integrating 3D data with high-resolution images to pursue this objective for use in the real-time assessment of hole cleaning sufficiency and wellbore stability and, consequently, for the prediction, prev…

The goal of the project described in this paper was to test the added value of shear-wave velocity interpreted from underwater multichannel analysis surface waves, particularly as a propagation tool of geotechnical information.

This paper assesses the potential of augmented depletion development in four US plays: Bakken, Eagle Ford, Midland, and the Anadarko Basin.