modeling

This study applies Monte Carlo simulation and an XGBoost regression model to assess the influence of various formations, geologic provinces, tectonic-plate types, and boundary conditions on hydrogen concentrations.

This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.

This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.

This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.

The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.

This paper details a new enhanced oil recovery method piloted successfully by several operators in the Bakken and recently implemented in the Midland play of the Permian Basin.

The authors reach conclusions that the industry should define a standard testing method to improve swelling performance, including validation of repeatability, to complement existing guidelines.

This paper presents the development of an advanced simulation tool aimed at providing a better understanding of the complex fluid-displacement phenomena present in well-cementing processes.

This paper examines the effects of cement voids and microannuli on the collapse resistance of pipe/cement/pipe systems with void angles ranging from 0º to 70º.

The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.