Application of Machine Learning Models in Georeservoirs: Applications and Outlooks

The application of machine learning (ML) in the study and management of georeservoirs has gained tremendous momentum over recent years. With advances in data collection technologies and the emergence of sophisticated computational approaches—including deep learning and artificial intelligence—researchers have begun leveraging large and complex datasets to develop more precise models for geological reservoir characterization and forecasting. Traditionally, reservoir modeling relied on physically grounded but relatively simple frameworks. The current state of the art, however, melds these physical principles with advanced data-driven methods, sharply improving predictive accuracy and expanding our ability to interpret reservoir properties. Recent strides have been made in petrophysical property estimation, seismic and subsurface imaging, proxy modeling for production forecasting, digital rock physics, studies of unconventional reservoirs, and innovative applications in geothermal resources, carbon capture and storage, and induced seismicity. Despite these advances, critical gaps persist regarding model reliability, interpretability, and transferability.The primary objective of this Research Topic is to confront and address methodological and practical challenges that limit wider adoption and effectiveness of ML models in georeservoir sciences. Outstanding questions remain around data adequacy, with many studies hampered by limited or low-quality datasets. Trust in ML solutions is undermined by their perceived “black box” nature, which stifles professional adoption and interpretability in geological contexts. Additionally, issues of generalizability are evident, as many models are site-specific and not easily transferable across geological environments. Validation and reproducibility practices remain inconsistent, which further constrains reliable application and benchmarking of new approaches. By spotlighting these concerns, this Research Topic aims to stimulate the development of more robust, transparent, and physically consistent ML models for georeservoir systems.This Research Topic welcomes cutting-edge research and comprehensive reviews focused on advancing machine learning methodologies for georeservoir analysis. Topics will be limited to those directly contributing to improvements in the accuracy, reliability, interpretability, and transferability of ML applications in reservoir studies. Areas of interest include, but are not limited to:• Integrating porous media equations (e.g., Navier-Stokes, Darcy’s law) within neural network architectures to enhance physical consistency• Generative models for realistic subsurface scenario reconstruction, data augmentation, and uncertainty quantification• Bayesian frameworks to support probabilistic modeling and risk-aware decision-making• Dynamic reservoir modeling through real-time ML data stream integration• Development of benchmarking protocols for the fair comparison and reproducibility of ML-based reservoir studiesSubmissions may include original research articles, reviews, methods papers, and perspective pieces. Topic editors: Faisal Ur Rahman Awan, Qingchao Li, Mohsen Farrokhrouz Source: https://www.frontiersin.org/research-topics/79991/application-of-machine-learning-models-in-georeservoirs-applications-and-outlooks