3D-pix2pix generative adversarial network for history matching with complex geologies

Yunxue Lü; Weifeng Liu; Kai Zhang; Baodi Liu; Yanjiang Wang; Jun Wang

doi:10.1117/12.2612149

19 November 2021 3D-pix2pix generative adversarial network for history matching with complex geologies

Yunxue Lü, Weifeng Liu, Kai Zhang, Baodi Liu, Yanjiang Wang, Jun Wang

Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 120591C (2021) https://doi.org/10.1117/12.2612149
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China

Abstract

Automatic history matching is a process of using an optimization algorithm to adjust the parameters of the reservoir model. The reservoir model can reproduce the historical performance of the reservoir and realize the prediction for future production. Accurate prediction of oil well performance guarantees to establish a reliable reservoir model, which is traditionally realized by ESMDA and ensemble Kalman filter. We design and implement history matching using a 3D-pix2pix generative adversarial network(3D-pix2pix GAN) structure for the first time, which can correct the parameters of the complex heterogeneous reservoir based on dynamic response. The adversarial generative network includes generator and discriminator. The generator attempts to use the fast feedforward operation of historical production data (input) to reconstruct the calibrated model, while the discriminator attempts to distinguish the pseudo output and the prior (real data) so that 3D-pix2pixGAN finally learns an infinitely close to the real reservoir model. The most significant contribution of this work is to train a 3D-pix2pixGAN model to correct reservoir model parameters. Compared with traditional work ow, 3D-pix2pixGAN has several advantages. First, the reservoir parameters estimated from history matching help to improve 3D reservoir characterization. Second, the reservoir obtained by history matching can accurately predict the future production of water and oil. Third, 3D-pix2pixGAN is used as a proxy model instead of using a numerical simulator in the training process to reduce the amount of computation and achieve end-to-end offline processing.

Citation Download Citation

Yunxue Lü, Weifeng Liu, Kai Zhang, Baodi Liu, Yanjiang Wang, and Jun Wang "3D-pix2pix generative adversarial network for history matching with complex geologies", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 120591C (19 November 2021); https://doi.org/10.1117/12.2612149

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KEYWORDS

3D modeling

Data modeling

Visual process modeling

Gallium nitride

Process modeling

Convolution

Geology

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