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Approaches to Proxy Modeling of Gas Reservoirs

Author

Listed:
  • Alexander Perepelkin

    (Center for Information Technologies, Connection and Automation, Gazprom VNIIGAZ LLC, 195112 Saint Petersburg, Russia)

  • Anar Sharifov

    (Center for Information Technologies, Connection and Automation, Gazprom VNIIGAZ LLC, 195112 Saint Petersburg, Russia)

  • Daniil Titov

    (Center for Information Technologies, Connection and Automation, Gazprom VNIIGAZ LLC, 195112 Saint Petersburg, Russia)

  • Zakhar Shandrygolov

    (Center for Information Technologies, Connection and Automation, Gazprom VNIIGAZ LLC, 195112 Saint Petersburg, Russia)

  • Denis Derkach

    (AI and Digital Science Institute, National Research University Higher School of Economics, 101000 Moscow, Russia)

  • Shamil Islamov

    (Research and Development Department, Center for Engineering Technologies LLC, 121170 Moscow, Russia)

Abstract

In the gas industry, accurate forecasting of gas production is critical for optimizing well operating conditions. Although traditional hydrodynamic models offer high accuracy, they are often computationally intensive and time-consuming, prompting a growing interest in proxy-based alternatives. This study proposes a hybrid methodology based on Spatio-Temporal Graph Neural Networks (ST-GNNs) for gas production forecasting. The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. The model was validated using an extensive set of hydrodynamic simulation calculations and real-world field data. On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. The ST-GNN framework demonstrates promising potential as a tool for operational and strategic planning.

Suggested Citation

  • Alexander Perepelkin & Anar Sharifov & Daniil Titov & Zakhar Shandrygolov & Denis Derkach & Shamil Islamov, 2025. "Approaches to Proxy Modeling of Gas Reservoirs," Energies, MDPI, vol. 18(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3881-:d:1706376
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    References listed on IDEAS

    as
    1. Dongkwon Han & Sunil Kwon, 2021. "Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs," Energies, MDPI, vol. 14(12), pages 1-24, June.
    2. Zekun Guo & Hongjun Wang & Xiangwen Kong & Li Shen & Yuepeng Jia, 2021. "Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation," Energies, MDPI, vol. 14(17), pages 1-17, September.
    3. Taiyu Jin & Yang Xia & Haolin Jiang, 2023. "A Physics-Informed Neural Network Approach for Surrogating a Numerical Simulation of Fractured Horizontal Well Production Prediction," Energies, MDPI, vol. 16(24), pages 1-21, December.
    4. Peyman Bahrami & Farzan Sahari Moghaddam & Lesley A. James, 2022. "A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering," Energies, MDPI, vol. 15(14), pages 1-32, July.
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