neural networks

The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.

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.

This paper presents an approach using artificial neural networks to predict the discharge pressure of electrical submersible pumps.

The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.

This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.

This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.

This paper presents an artificial intelligence algorithm called dual heuristic dynamic programming that can be used to solve petroleum optimization-control problems.

The authors of this paper discuss a global rate-of-penetration machine-learning model with the potential to eliminate learning curves and reduce time and costs associated with developing a new model for every field.

The authors discuss the development of a deep-learning model to identify errors in simulation-based performance prediction in unconventional reservoirs.

The authors of this paper propose a novel approach to data-driven modeling for transient production of oil wells.