Approaches to Proxy Modeling of Gas Reservoirs

Alexander Perepelkin (), Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach and Shamil Islamov ()
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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

Energies, 2025, vol. 18, issue 14, 1-22

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.

Keywords: proxy modeling; gas production forecasting; Spatio-Temporal Graph Neural Networks (ST-GNN); data-driven models; time series prediction; reservoir modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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