A machine learning approach to accelerate convergence of the nonlinear solver in
multiphase flow problems is presented here. The approach dynamically controls an …

Reservoir simulators model the highly nonlinear partial differential equations that represent
flows in heterogeneous porous media. The system is made up of conservation equations for …

Reservoir simulations for petroleum fields and seismic imaging are known as the most
demanding workloads for high-performance computing (HPC) in the oil and gas (O&G) …

Flash calculation is an essential step in compositional reservoir simulation. However, it
consumes a significant part of the simulation process, leading to long runtimes that may …

We present a novel reservoir simulator time-step selection approach which uses machine-
learning (ML) techniques to analyze the mathematical and physical state of the system and …

(English) The Oil and Gas (O&G) industry ranks prominently among the leading commercial
users of powerful supercomputers worldwide, as indicated by global High-Performance …

We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial
differential equations (PDEs) that is based on online/adaptive learning. It is applied in the …