Improved and More-Rapid History Matching With a Nonlinear Proxy and Global Optimization

Abstract

Abstract The generation of reservoir simulation models that match field production data has been a long-time industry challenge. This paper presents two workflows for assisted history match. One workflow minimizes the misfit between simulated versus history data with a global optimizer, by adjusting reservoir and well unknown parameters in the simulation model. An alternative workflow is used to reduce the number of numerical simulations. The workflow trains a comprehensive nonlinear proxy model with a small set of numerical simulations from experimental design. The nonlinear proxy neural network is used to characterize parameter sensitivities to reservoir parameters and to generate solution sets of the parameters that match history. The neural network solution sets can be validated with the simulator or used as initial solutions for a full optimization. The paper demonstrates that the neural network is an excellent proxy for the numerical simulator over the trained parameter space. A field example from a water injection project illustrates the approach with excellent matches for individual well fluid rates, using a number of reservoir and well parameters. The use of the proxy yields excellent matches for well production profiles.