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Prediction of Shale Gas Production by Hydraulic Fracturing in Changning Area Using Machine Learning Algorithms

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Abstract

Machine learning has been widely used for the production forecasting of oil and gas fields due to its low computational cost. This paper studies the productivity prediction of shale gas wells with hydraulic fracturing in the Changning area, Sichuan Basin. Four different methods, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and artificial neural network (ANN) are used, and their performances are compared by the value of the mean absolute percentage error to determine the best method of all. The training and validation results show that the MLR and SVM methods exhibit poor performances with relatively high errors (> 15%), while the ANN and RF methods show obviously better results, where the RF has a median error (~12%) and the ANN has the smallest error (<10%). After the production forecasting, the particle swarm optimization is implemented as a parameter optimization approach to improve the gas production, which can be increased by around two times after optimization. This study provides a guideline for the shale gas production via hydraulic fracturing in the Changning area.

Article Highlights

  • Machine learning algorithms are used to predict the shale gas production by hydraulic fracturing in Changning area.

  • An integrated data set that includes geological and engineering parameters as well as recorded production rates are used.

  • Productivity can be substantially improved by optimizing fracturing parameters using particle swarm optimization.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank the National Key Research and Development Program of China (2019YFA0708300), and Science Foundation of China University of Petroleum, Beijing (No. 2462022QNXZ002) for supporting this work.

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The authors have not disclosed any funding.

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Authors and Affiliations

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

    Dongshuang Li, Shaohua You, Qinzhuo Liao, Mao Sheng & Shouceng Tian

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  1. Dongshuang Li
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  2. Shaohua You
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  3. Qinzhuo Liao
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  4. Mao Sheng
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  5. Shouceng Tian
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Contributions

All authors contributed to the study conception and design, investigation, original draft write, edit and review. The software development was performed by DL. The funding and supervision of the work were conducted by QL. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qinzhuo Liao.

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Li, D., You, S., Liao, Q. et al. Prediction of Shale Gas Production by Hydraulic Fracturing in Changning Area Using Machine Learning Algorithms. Transp Porous Med 149, 373–388 (2023). https://doi.org/10.1007/s11242-023-01935-3

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  • DOI: https://doi.org/10.1007/s11242-023-01935-3

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