modeling

This work introduces an analytical model for nonisothermal CO2 injection that accounts for both Joule-Thomson cooling and interformation heat exchange.

This study demonstrates how routinely acquired downhole temperature data allows for direct estimation of fluid saturations and delivers more accurate reservoir volume assessments, particularly in aquifer-supported systems.

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 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 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.

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.

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 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.