Enhancing the Performance of the Distributed Gauss-Newton Optimization Method by Reducing the Effect of Numerical Noise and Truncation Error With Support-Vector Regression

Summary Numerical optimization is an integral part of many history-matching (HM) workflows. However, the optimization performance can be affected negatively by the numerical noise existent in the forward models when the gradients are estimated numerically. As an unavoidable part of reservoir simulation, numerical noise refers to the error caused by the incomplete convergence of linear or nonlinear solvers or truncation errors caused by different...