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DBN with IQPSO Algorithm for Permeability Prediction: A Case Study of the Lizhai Geothermal Field, Zhangye Basin (Northern China)

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Abstract

This study utilized the deep belief network (DBN) model to predict permeability, which has been shown to be superior in data fitting compared to traditional machine learning models. However, the DBN model requires optimization of multiple hyper-parameters in modeling, making it challenging to ensure that the model parameters are optimal. To address this issue, the improved quantum-behaved particle swarm optimization (IQPSO) algorithm was introduced to optimize the DBN parameters and propose an IQPSO–DBN model. The research focused on the tight sandstone reservoirs of the Baiyanghe Formation in the geothermal field of Zhangye Basin, Northern China. Two experiments were designed to evaluate the prediction ability of the IQPSO–DBN model, and it was compared with other models such as stepwise iteration, support vector regression, group method of data handling, and extreme gradient boosting methods. The results demonstrated that the IQPSO–DBN model can effectively predict permeability in pure data drive, and it had the lowest prediction error compared to the other models. Therefore, the combination of the DBN model and the IQPSO algorithm can be a powerful tool for permeability prediction, which can have significant implications in geothermal fields and other related fields.

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Notes

  1. * *1 mD = 1 millidarcy = 9.869233-16 m2

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Acknowledgments

We are very grateful to Professor Yu Guolong for his guidance on QPSO algorithm through e-mail and some data support of Gansu Provincial Bureau of Geology.

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  1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, 102249, China

    Haoyu Zhang & Wensheng Wu

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  1. Haoyu Zhang
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Correspondence to Wensheng Wu.

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Zhang, H., Wu, W. DBN with IQPSO Algorithm for Permeability Prediction: A Case Study of the Lizhai Geothermal Field, Zhangye Basin (Northern China). Nat Resour Res 32, 1941–1957 (2023). https://doi.org/10.1007/s11053-023-10240-1

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