Machine learning for reservoir potential assessment: a critical review of established and promising methods

Abstract

Relevance. The oil and gas industry remains strategically important to the global economy, and the efficiency of field development is directly determined by the accuracy of reservoir potential assessment and geological system behavior prediction. However, reliable quantitative assessment of reservoir productivity is hampered by high geological heterogeneity, limited and diverse geological and production data, and the difficulty of integrating static and dynamic parameters. With the industry digitalization, machine learning methods are emerging as a promising tool capable of complementing or partially replacing classic hydrodynamic simulators by identifying complex nonlinear relationships between input geological characteristics and output development indicators. Aim. To critically systematize global experience in applying machine learning methods to assess reservoir potential and forecast oil and gas field development indicators. Methods. Analysis of scientific publications devoted to the application of machine learning methods, including neural networks, ensemble algorithms, probabilistic and hybrid models, to forecasting flow rates, cumulative production, and pressure. The characteristics of the input data used, approaches to model training, and metrics for assessing forecast quality are discussed. Results and conclusions. Machine learning methods have been shown to significantly accelerate reservoir potential assessment while maintaining accuracy comparable to hydrodynamic models. They effectively identify complex relationships between geological characteristics and dynamic system responses, making them a promising tool for the rapid analysis of development scenarios. However, key limitations remain sensitivity to the quality of input data and the limited nature of training samples. The authors note a trend toward hybrid approaches combining machine learning and physical and mathematical modeling, improving the robustness and interpretability of results. Despite the high level of automation, expert participation remains critical at the stages of data preparation, feature selection, and results interpretation. For citation: Piskunov S.A., Truhachev M.S., Rukavishnikov V.S., Davoodi S. Machine learning for reservoir potential assessment: a critical review of established and promising methods. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 8, pp. 168–183.  http://doi.org/10.18799/24131830/2026/8/5657