simulation

Modeling solubility trapping at reservoir scale remains a key challenge for carbon storage projects. This study introduces a novel sub-grid convective mixing model, implemented in the Open Porous Media Flow simulator and applied to the SPE11 benchmark, that enables accurate prediction of carbon dioxide dissolution trapping in geological storage using computationally e…

This article from the SPE Integrated Reservoir Management Technical Section (IRMTS) addresses decisions that look stronger than they are and take longer to implement than the reservoir can afford.

This paper presents a competency-centered, data-driven approach implemented to strengthen control-room emergency-response capability through a cloud-hosted, scenario-based virtual plant simulator.

This work evaluates CO2-based fracturing as a sustainable development approach for shale gas reservoirs in the Burgos Basin of Mexico.

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 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 paper reviews fracturing-program design, completion technology, real-time data collection, data integration, and lessons learned for the Pikka development on the North Slope of Alaska.

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 describes the integration of iterative torque/drag/buckling and hydraulic simulations for multiple tapered string combinations, the results of which guided the selection of a string configuration that deemed planned well total depths feasible.

Two examples from ONGC show how supervised AI-driven automation scaled well modeling across hundreds of offshore wells, saving more than 1,000 engineering hours.