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Conditional stochastic simulation of fluvial reservoirs using multi-scale concurrent generative adversarial networks

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Abstract

To accurately grasp the comprehensive geological features of fluvial reservoirs, it is necessary to exploit a robust modelling approach to visualize and reproduce the realistic spatial distribution that exhibits apparent and implicit depositional trends of fluvial regions. The traditional geostatistical modelling methods using stochastic modelling fail to capture the complex features of geological reservoirs and therefore cannot reflect satisfactory realistic patterns. Generative adversarial network (GAN), as one of the mainstream generative models of deep learning, performs well in unsupervised learning tasks. The concurrent single image GAN (ConSinGAN) is one of the variants of GAN. Based on ConSinGAN, conditional concurrent single image GAN (CCSGAN) is proposed in this paper to perform conditional simulation of fluvial reservoirs, through which the output of the model can be constrained by conditional data. The results show that ConSinGAN, with the introduction of conditional data, not only preserves the model and parameters for future use but also improves the quality of the simulation results compared to other modeling methods.

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Data availability

The data and code used to support this study are available from the corresponding author upon request.

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Funding

This work is supported by the National Natural Science Foundation of China (Nos. 41672114, 41702148) and Key research base of Humanities and Social Sciences in Guangdong Province–Open Fund Project of Local Government Development Research Institute of Shantou University (07422002).

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Ting Zhang, Mengkai Yin, Hualin Bai & Anqin Zhang

  2. Institute of Local Government Development, Shantou University, Shantou, 515063, China

    Anqin Zhang

  3. School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, 201209, China

    Yi Du

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  1. Ting Zhang
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Correspondence to Anqin Zhang or Yi Du.

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Zhang, T., Yin, M., Bai, H. et al. Conditional stochastic simulation of fluvial reservoirs using multi-scale concurrent generative adversarial networks. Comput Geosci (2024). https://doi.org/10.1007/s10596-024-10279-w

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