Data-driven advances in micro-scale petroleum research: From digital rocks to molecular simulations

The oil and gas industry faces the dual challenge of improving recovery from mature reservoirs while reducing carbon emissions during the energy transition. Understanding micro-scale mechanisms (phenomena spanning from molecular and nano-pore fluid behavior to pore-scale multi-phase flow) is essential for advancing carbon dioxide (CO 2 )-enhanced oil recovery (CO 2 -EOR) and carbon capture, utilization, and storage (CCUS) technologies. Digital imaging and numerical simulation methods, while foundational, face inherent limitations, including the resolution-field of view (FoV) trade-offs and prohibitive computational costs that hinder their application in optimizing sustainable recovery strategies. This review provides a comprehensive assessment of how data-driven methods are transforming micro-scale petroleum research across three aspects. At the imaging level, we examine deep learning advances in automated segmentation, super-resolution reconstruction, and 3D pore structure generation. At the pore level, we analyze neural network approaches for rapid transport-property prediction and physics-informed methods balancing computational efficiency with physical rigor. At the molecular level, we evaluate machine learning interatomic potentials achieving near-quantum mechanical accuracy at classical simulation costs and their applications in nano-scale fluid-surface interactions. Our analysis indicates that while data-driven methods offer substantial computational acceleration, critical challenges remain in training data quality, model transferability, physical consistency, and pore-to-reservoir-scale bridging. This review aims to provide researchers and engineers with a systematic framework for evaluating both the transformative potential and current limitations of these approaches in sustainable oil and gas development.