Abstract
Reservoir characterization is one of the main engineering components to ensure the optimum exploration and development of oil and gas. Deep-learning-based approaches are effective and widely used in reservoir modeling in recent years. In this work, a novel reservoir characterization method based on variational auto-encoder and selective attention mechanism (SA-VAE) is proposed. We construct a new network architecture and improved loss functions to achieve conditional reservoir modeling. To improve the ability of extracting connectivity characteristics of variational auto-encoder, the selective attention mechanism is also applied in the proposed SA-VAE model. Besides, to accurate describe the spatial characteristics of connected structures, an innovative strategy by reconstructing the whole structures step by step according to the corresponding structure connectivity is used in SA-VAE. We validate the performance of SA-VAE by using both 2D and 3D datasets with different reservoir structures. The experimental results indicate that our method can reproduce geological structures accurately under the constraints of conditioning data both in morphology and geostatistics. These prove that SA-VAE is able to characterize heterogeneous reservoir structures effectively.
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Data availability
The test data used in this work were collected from open datasets, with appropriate citations given in this paper. They are available on request from the corresponding author (chenqiyu403@163.com).
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This work is supported by the National Natural Science Foundation of China (42172333, 41902304) and the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206).
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All authors contributed to the conception and design of the study. Material preparation, data collection and analysis were performed by Dajie Chen, Zhesi Cui, and Ruyi Wang. The first draft of the manuscript was written by Dajie Chen, Qiyu Chen and Gang Liu. All authors reviewed and approved the final manuscript.
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Communicated by: X. Ma
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Chen, D., Chen, Q., Cui, Z. et al. SA-VAE: a novel approach for reservoir characterization based on variational auto-encoder and selective attention mechanism. Earth Sci Inform 16, 3283–3301 (2023). https://doi.org/10.1007/s12145-023-01095-4
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DOI: https://doi.org/10.1007/s12145-023-01095-4