DSDE: In Theory

This research aims to harness the advanced capabilities of artificial intelligence, specifically deep learning and large language models, to develop a comprehensive system for detecting and explaining oil spills.

This paper presents an efficient mathematical optimization method for well placement that maximizes contact with the productive zones for the best locations in the reservoir.

This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.

This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities.

With the right infrastructure and interoperability, subsea resident robotics could unlock more frequent, cost-effective inspections—and a new standard for offshore efficiency.

This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.

This study aims to thoroughly assess the influence of various completions, fracturing stimulation, and intrinsic reservoir properties affecting the productivity of 10 major unconventional plays while uncovering insights and trends unique to each play.

The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults.

An innovative approach uses a random-forest-based framework to link logging-while-drilling and multifrequencey seismic data to enable dynamic updates to lithology parameter predictions, enhancing efficiency and robustness of geosteering applications.

Best practices are not static; they evolve alongside advancements that redefine what is achievable.