Automated parameter tuning with accuracy control for efficient reservoir simulations

Computer simulations of complex physical processes typically require sophisticated numerical schemes that internally involve many parameters. Different choices of such internal numerical parameters may lead to considerably different levels of computational efficiency, some may even result in wrong simulation results. The task of finding an optimal set of the numerical parameters (e.g. for the purpose of minimising the simulation time), while ensuring an accepted level of numerical accuracy, is therefore extremely important but challenging. In this paper, we propose a new automated search algorithm that is based on constrained stochastic searches within the parameter space. This iterative search scheme is also equipped with an accuracy check, which adopts several complementary measures for quantifying the similarities between time series from different simulations, such that parameter choices that lead to insufficiently accurate results will be automatically rejected. As a concrete scenario of usage, we have applied the automated parameter search scheme to the open-source reservoir simulation framework OPM. An empirical study shows that a suitable design of the optimisation objective function, together with an appropriate choice of the number of trials per search iteration and the perturbation scale per trial, can produce fast and convergent improvements with respect to the optimisation objective. For example, for a set of 12 numerical parameters, 30 trials from five search iterations are sufficient for reducing the objective function by 30% for the open Norne black-oil reservoir model. The robustness of the automated search scheme is also demonstrated for two other open reservoir models. Moreover, it is found that the parameter values automatically identified for the Norne model can also greatly improve the simulation efficiency of another proprietary reservoir model that has drastically different scale, resolution and geological properties.