Surrogate-assisted reservoir history matching

In the community of petroleum engineering, the use of surrogate modelling techniques have recently gained more and more popularity to improve the efficiency of history matching. However, it is still not possible to fully utilize their potential in realistic applications. One of the challenges is to retain high accuracy while increasing the computational efficiency using a surrogate model. This dissertation proposed a projection-based reduced-order model and a data-driven deep convolutional neural network. In the first part of the thesis, a non-intrusive subdomain POD-TPWL method for solving gradient-based reservoir history matching problems is presented. It is a projection-based reduced-order modelling approach wherein the adjoint model of the original high-dimensional non-linear model is approximated by a subdomain reduced-order linear model. Furthermore, by introducing domain decomposition for the reduced-order model and by restricting the number of uncertain parameter patterns to the subdomains, the number of full order simulations required for the derivation of this surrogate model is reduced drastically. In the second part of the thesis, we propose two kinds of deep-learning inversion frameworks for efficiently solving large-scale history matching problems. The first deep-learning deterministic inversion framework primarily explores the possibility of applying a DNN surrogate to approximate the gradient of the objective function by making use of auto-differentiation (AD). In combination with the DNN surrogate, the AD enables us to evaluate the gradients efficiently in a parallel manner and without the need of explicitly coding of the adjoint model. The second framework is the deep-learning stochastic inversion which constructs a deep-learning surrogate based on an image-oriented distance parameterization for ensemble-based seismic history matching. Instead of directly assimilating spatially dense seismic data, image-oriented distance parameterization is employed to extract valuable information from the water fronts. Inspired by the methodologies developed for image segmentation in the field of computer vision and image processing, we propose an advanced image segmentation network for accurately predicting water fronts with highly-complex spatial discontinuities. In comparison with the conventional workflows entirely based on high-fidelity simulation models, experimental results show that the proposed surrogate-supported workflow achieves an accuracy equal to or better than the conventional workflow at significantly lower cost.