Speaker
Description
Accurate prediction of fluid flow in porous media underpins the safe and efficient utilization of subsurface resources. While computational methods for porous media flow have traditionally been developed and validated within the context of oil and gas recovery, the subsurface is now increasingly envisioned as a critical asset for carbon dioxide (CO₂) sequestration and hydrogen (H₂) storage applications that impose fundamentally different physical, chemical, and operational constraints. Variations in fluid properties, multiphase interactions, transport mechanisms, geochemical coupling, and leakage risk necessitate a re-evaluation of the suitability and limitations of existing modeling approaches.
This study presents a systematic comparison of widely used computational methods for flow prediction in porous media, including continuum-scale approaches based on Darcy and extended Darcy formulations, pore-scale methods such as lattice Boltzmann and direct numerical simulations, and hybrid and data-driven techniques integrating physics-based models with machine learning. The strengths, assumptions, and computational trade-offs of each approach are critically assessed with respect to their applicability across oil recovery, CO₂ geostorage, and H₂ subsurface storage scenarios.
By benchmarking these methods against key performance criteria i.e. accuracy, scalability, representation of heterogeneity, and capability to capture multiphase and reactive transport, this work highlights gaps in current modeling frameworks and identifies pathways for next-generation predictive tools. It underscores that reliable flow prediction is not merely a reservoir engineering challenge but a foundational requirement for the long-term integrity, safety, and scalability of subsurface energy and climate solutions.
| Country | India |
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