Enhanced geothermal systems (EGS) are a key technology for developing deep geothermal resources, yet they face significant challenges in constructing efficient thermal reservoirs within high-stress, high-strength, and low-permeability crystalline rock formations. Traditional hydraulic fracturing (HF) techniques encounter deep challenges in these environments, including excessively high fracturing pressures, limited fracture network patterns, and the risk of induced seismicity. This paper reviews the multi-scale thermal-mechanical mechanisms, fracture evolution patterns, and control strategies associated with thermal stimulation and permeability enhancement in the modification of deep geothermal reservoirs. Research indicates that thermally induced fracturing triggers intergranular and transgranular cracks at the microscopic scale due to mineral thermal expansion mismatches, which macroscopically manifests as nonlinear degradation of rock strength and modulus. The redistribution of the thermal elastic stress field significantly lowers the breakdown pressure, while matrix thermal contraction increases fracture aperture, leading to an exponential enhancement of permeability following a cubic law. However, the high confining pressure constraints, true triaxial stress anisotropy, and thermal short-circuiting risks present substantial suppression and challenges to the effectiveness of thermal stimulation in deep in situ environments. Different fracturing media, such as water, liquid nitrogen (LN₂), and supercritical CO₂, exhibit varying advantages in thermal stimulation efficiency due to their unique thermal-flow characteristics. Future research should focus on the thermal-mechanical coupling mechanisms under true triaxial stress conditions, and develop intelligent control strategies for permeability enhancement and thermal short-circuiting risk mitigation. This study synthesizes existing analyses and proposes potential engineering strategies for stimulating deep EGS reservoirs, offering significant strategic value for the development of geothermal energy as a baseload renewable resource.

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence.

As a disruptive technology to overcome the bottlenecks of conventional mechanical excavation and blasting, microwave-assisted rock fragmentation demonstrates tremendous potential in deep geo-resource development and extreme-environment engineering. This paper presents a comprehensive review of the latest advances in this field, thoroughly dissecting the electromagnetic–thermal–mechanical–chemical (THMC) multi-field coupling mechanisms governing microwave–rock interactions. Particular emphasis is placed on elucidating the mechanisms of thermal stress fracturing, phase-transition expansion, and thermo-chemical coupling damage triggered by mineral dielectric heterogeneity. The nonlinear effects of microwave radiation parameters, intrinsic rock properties, and complex in-situ stress environments on fracturing efficiency are rigorously analyzed, and precision multi-scale characterization methodologies—from macroscopic mechanical degradation to microscopic fracture networks—are comprehensively summarized. Building upon this foundation, the paper critically evaluates the current engineering application status and electromagnetic safety shielding challenges of this technology in frontier scenarios, including microwave-assisted tunnel boring machines (TBM), intelligent mineral sorting, deep unconventional oil and gas fracturing, and space in-situ resource utilization (ISRU). Finally, the limitations of existing research regarding cross-scale effects, high-temperature and high-pressure in-situ testing, and dynamic intelligent control are identified, and future development pathways oriented toward multi-source data fusion and adaptive power modulation are prospected, aiming to provide robust theoretical support and forward-looking guidance for the cross-scale engineering transformation of microwave rock-breaking technology.