history matching

The authors propose a deep-learning-based approach enabling near-real-time CO2-plume visualization and rapid data assimilation incorporating multiple geological realizations for predicting future CO2 plume evolution and area-of-review determination.

In this study, forward simulation is executed by a commercial reservoir simulator while external code is developed for backward calculations.

In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.

This paper provides guidelines for thermal modeling for carbon capture and storage projects in a depleted gas field.

This paper investigates condensate-banking effects on well performance by conducting field-modeling studies on Delaware Basin deep Wolfcamp condensate producers using compositional simulation models with hydraulic fractures.

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

This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.

The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.

This paper presents a specialized workflow that aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies for a deep formation in the Permian Basin.

This paper describes numerical modeling studies of fracture-driven interactions using a coupled hydraulic-fracturing-propagation, reservoir-flow, and geomechanics tool.