modeling

This paper details the importance and value derived from a real-time process-twin model for one of the world’s largest operating carbon dioxide sequestration projects.

This paper presents a high-fidelity, reservoir-centric stimulation-to-production modeling workflow to prevent fracture-driven interactions.

This work elaborates on recommended practices for calibrating a numerical model to a set of available infill-well field observations.

This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.

A new framework combines analytical and numerical insights, extending vertical equilibrium modeling and guiding reservoir-scale implementation, providing reservoir engineers with practical guidance for assessing accuracy across flow regimes and long-term carbon-dioxide migration.

The 2026 Upstream Scale Guidelines establish a common workflow for field measurements, scale modeling, and risk assessment to support more consistent operational decisions.

This paper presents a flexible, highly cost- and energy-efficient method of gas separation and CO₂ capture from many different gas mixtures with a large range of CO₂ concentrations.

This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.

A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.

By integrating experimental and mathematical modeling approaches, this work aims to advance the understanding of sand-detachment dynamics and formation instability under varying saturation conditions.