Reservoir simulation

This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
This paper describes a full-field and near-wellbore poromechanics coupling scheme used to model productivity-index degradation against time.

Page 2 of 13