Deep learning surrogate model-based randomized maximum likelihood for large-scale reservoir automatic history matching

Abstract

Automatic history matching in large-scale reservoir simulations poses significant challenges due to the complexity and uncertainty inherent in reservoir parameters. In this paper, we introduced a deep learning-based surrogate model, termed Convolution Recurrent Neural Network, for addressing these challenges. The Convolution Recurrent Neural Network leverages Convolution Neural Network and Recurrent Neural Network to extract spatial and temporal features respectively to approximate the intricate map between reservoir parameters and production data. And then, through the Randomized Maximum Likelihood method, the posterior distribution of reservoir parameters is sampled by optimizing a series of perturbed objective functions. This method offers several advantages, including its ability to handle high-dimensional data, capture complex reservoir dynamics, and efficiently calibrate uncertain parameters. Through comprehensive numerical experiments on both synthetic and real-world reservoir models, we demonstrate the efficacy of the approach in enhancing the efficiency and accuracy of automatic history matching in large-scale reservoir simulations.

Cited as: Zhou, W., Fu, W., Liu, C., Zhang, K., Shen, J., Liu, P., Zhang, J., Zhang, L., Yan, X. Deep learning surrogate model-based randomized maximum likelihood for large-scale reservoir automatic history matching. Computational Energy Science, 2024, 1(1): 17-27. https://doi.org/10.46690/compes.2024.01.03

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