Advanced Predictive Models for Subsurface Energy Storage and Resource Management

Subsurface reservoir engineering has become increasingly pivotal as the world intensifies its focus on sustainable energy systems and effective resource management. Traditionally, this field concentrated on optimizing resource extraction—such as hydrocarbons—by refining knowledge of properties including irreducible water saturation, permeability, and fluid behavior in porous media. However, the contemporary drive for sustainability has introduced new complexities, as applications like geological carbon storage and large-scale geophysical energy storage gain prominence. These advances come with pressing questions about our current ability to observe, characterize, and predict multiphase, compressible, and turbulent flow processes in the deep subsurface, especially given their relevance to safe and efficient energy transition strategies. Despite significant progress in measurement, modeling, and monitoring techniques, substantial challenges remain. Latest studies have showcased the promise of data-driven strategies, advanced inversion methods, and sophisticated rock physics modeling, yet they also highlight difficulties in uncertainty quantification, the integration of heterogeneous datasets, and the representation of complex physical interactions. As emerging technologies expand the range of reservoir applications—from carbon sequestration to underground hydrogen storage and geothermal energy—the need for robust, multidisciplinary predictive models that capture the evolving subsurface landscape is more apparent than ever.



This Research Topic aims to accelerate the development and dissemination of advanced predictive modeling frameworks that tackle the multifaceted challenges in subsurface energy storage and resource management. By fostering collaboration across disciplines and integrating techniques in data assimilation, inverse problem-solving, and rock physics, we seek to improve not only our understanding of fundamental fluid and energy transport phenomena but also to enhance prediction and monitoring capabilities for critical energy storage projects. The objective is to bring together innovative contributions that bridge theory and practice, unravel hidden complexities of subsurface behavior, and support reliable decision-making for a range of geological settings and energy carrier systems.



This Research Topic is positioned at the intersection of reservoir engineering, geophysics, and computational modeling, with an emphasis on applications supporting both energy production and sustainable storage. While broad in scope, it focuses on methodologies that enhance predictive capacity and operational confidence in complex reservoirs. To gather further insights in subsurface characterization and management, we welcome articles addressing, but not limited to, the following themes:



• Modeling and monitoring of geological carbon storage, including CO₂ injection, migration, and trapping mechanisms

• Geophysical monitoring and novel observational techniques for energy storage systems, such as hydrogen or compressed air

• Advanced approaches in inverse problems and data assimilation for improved model calibration and uncertainty quantification

• Development and application of rock physics models linking geophysical measurements to reservoir properties and fluid saturation

• Simulation of advanced flow phenomena including multiphase, compressible, and turbulent flow in porous media

• NMR-based innovations for predictive reservoir modeling, evaluation, and characterization



Appendix: We invite original research articles, reviews, and case studies that contribute to the advancement of predictive modeling in georeservoirs.