DSDE: In Theory

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.

This paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.

The authors of this paper describe a project to develop a virtual sensor to monitor the cooling effect downstream of a subsea choke to avoid hydrate plugs during cold-start operations.

This paper presents a continuous passive magnetic ranging technique that can provide real-time distance and direction to the offset well while drilling without interrupting drilling operations.

In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.