Many aspects of reservoir management can be expected to benefit from the application of
computational optimization procedures. The focus of this review paper is on well control …

A new deep-learning-based reduced-order modeling (ROM) framework is proposed for
application in subsurface flow simulation. The reduced-order model is based on an existing …

A machine learning–based framework for modeling the error introduced by surrogate
models of parameterized dynamical systems is proposed. The framework entails the use of …

Abstract A POD-TPWL reduced-order modeling framework is developed to simulate and
optimize the injection stage of CO 2 storage operations. The method combines trajectory …

We present a global/local model reduction for fast multiscale reservoir simulations in highly
heterogeneous porous media. Our approach identifies a low-dimensional structure in the …

A new reduced-order model based on trajectory piecewise quadratic (TPWQ)
approximations and proper orthogonal decomposition (POD) is introduced and applied for …

We develop a proxy model based on deep learning methods to accelerate the simulations of
oil reservoirs–by three orders of magnitude–compared to industry-strength physics-based …

Well placement optimization is a crucial and complex task in oil field development. Well
placement is usually optimized by coupling reservoir numerical simulator with optimization …

This study presents an efficient gradient-based production optimization method that uses a
deep-learning-based proxy model for the prediction of state variables (such as pressures …

Two well-known model reduction methods, namely the trajectory piecewise linearization
(TPWL) approximation and the discrete empirical interpolation method (DEIM), are …