Knowledge about subsurface geological characteristics and a geothermic genetic model plays an essential role in geothermal exploration and resource assessment. To solve the problem in the Shunping area along eastern Taihang Mountain, geothermal geological conditions were analyzed by geophysical, geochemical, and geological methods, such as magnetotelluric, gas geochemistry, and structural analysis. The geothermic genetic model was developed by analyzing the characteristics of the heat source, water source, migration channel, reservoir, and cap rock of the geothermal geological conditions. Favorable deep thermal conduction conditions and sufficient atmospheric precipitation in the study area provide an original heat source and water supply for geothermal formation. The faults and unconformities of different scales have become effective channels for the migration of underground hot water. The thermal reservoir formed by marine carbonate rocks with karst fissure development provides suitable space for the storage of underground hot water. Although the Cenozoic strata have good thermal insulation and water insulation function, the thermal insulation and water insulation effect is not ideal because of the shallow coverage in the Shunping area and the damage by tectonic action, which affected thermal insulation and water insulation to some extent, restricting the practical preservation of underground heat energy in the Shunping area. The bedrock geothermal resource in the Shunping area is original from the combined action of multiple indexes of source, transport, reservoir, and cap. The geothermal geologic conditions of source and reservoir in the Shunping area are very similar to those in the Xiongan new area, and have obvious advantages in hydrodynamic conditions. Although limited by the cap’s effectiveness, the geothermal resources in the Shunping area can provide some clean energy support for local production and life, thereby satisfying economic development conditions and encouraging further geological exploration.

The absence of regional electrical data has constrained our comprehension of the deep geological structures in the Jizhong Depression. This limitation has impeded the exploration of factors and principles governing geothermal reservoirs. Historically, studies on these factors primarily centered on geothermal field attributes, such as anomalous geothermal flux, geothermal gradients, and deep Moho variations. In our research, we undertook an exhaustive interpretation of extended-period magnetotelluric readings. This was amalgamated with prior regional geological and geophysical studies to discern deep geological structural details pertinent to geothermal resource components, encompassing heat sources, pathways, reservoirs, and cap rocks. Our analysis spanned the foundational surface of the Cenozoic era, fault configurations, carbonate formation distribution, and layers with low velocity yet high conductivity. We also statistically evaluated geothermal wells, their density, and the water temperature attributes across various structural entities. This aided in comprehending how structural units influence thermal well features. Our research delineated the spatial interrelation between geothermal well distribution and deep geological structures, including carbonate rock distribution, Cenozoic thickness, fault patterns, and profound low-velocity, high-conductivity layers. Moreover, we scrutinized how structural unit types impact thermal well attributes, offering insights into the formation principles of deep carbonate geothermal reservoirs within the Jizhong Depression. Our findings suggest that the genesis of deep geothermal resources in the Jizhong Depression is swayed by regional geological conditions. Notably, the distribution of regional and especially deep geothermal wells is intimately tied to regional stratigraphy, structural designs, and profound geological structural traits. Furthermore, the temperature within geothermal wells correlates strongly with the burial depth, carbonate formation thickness, Cenozoic thickness, and proximity to fault channels responsible for fluid movement and heat transmission.

The shale reservoir serves as the primary driver of global oil and gas production growth. However, during the exploitation of these reservoirs, frequent instability in shale formations often leads to complex situations and significant nonproductive time. The underlying mechanisms of shale formation instability are not yet fully understood. Shale’s distinctive transverse isotropy, resulting from its depositional processes, differentiates it from other lithologies. To explore the mechanisms of instability, we perform triaxial compression experiments on shale samples under varying bedding dip angles and confining pressures. The experiments reveal shale’s strength anisotropy properties, and an anisotropic strength criterion is established using the least squares method. Based on this, a wellbore collapse pressure prediction model incorporating elastic and strength anisotropy is developed. The model is used to study the effects of elastic modulus and Poisson’s ratio anisotropy on the stress distribution around the wellbore. In addition, differences in collapse pressure predictions using three strength criteria are analyzed. The results indicate that the single plane of weakness (SPW) and Pariseau criteria exhibit significant prediction errors for layered rock strength, whereas the plane of patchy weakness (PPW) criterion provides more accurate predictions of anisotropic rock strength. Poisson’s ratio anisotropy has a minimal effect on tangential stress around the wellbore compared with elastic modulus anisotropy. The stress distribution is highly sensitive to variations in elastic modulus anisotropy. This, combined with Poisson’s ratio anisotropy, amplifies stress concentrations around the wellbore. Among the evaluated strength criteria, the SPW criterion tends to overestimate collapse pressure, whereas the PPW criterion underestimates it. In contrast, the Pariseau criterion provides predictions that fall between the two extremes, the PPW criterion is considered the most reasonable overall. These findings enhance the accuracy of wellbore safety predictions and the determination of optimal drilling fluid density in shale formations, aiding in the mitigation of complex subsurface challenges and improving operational efficiency.

Fiber optic sensing technologies, owing to their intrinsic safety, immunity to electromagnetic interference, and distributed sensing capabilities, demonstrate unique applications in monitoring CO2 geological storage. However, existing review studies often focus on a single sensor type or specific monitoring scenarios, lacking a systematic classification and scenario adaptation analysis of full-spectrum technologies from point and quasi-distributed to fully distributed sensing. To address this research gap, this paper systematically reviews the principles and performance characteristics of full-spectrum fiber optic sensing technologies, covering core mechanisms of distributed temperature, acoustic, and strain sensing, and establishes a technology-adaptation framework for typical monitoring scenarios including wellbore integrity, reservoir dynamics, caprock faults, and near-surface facilities while integrating the latest advances in machine learning and deep learning for fiber optic data processing. Based on systematic analysis, prioritized short-term and long-term recommendations are proposed, covering multitechnology integration, extreme-environment adaptability, intelligent data processing, and standardization. This work aims to provide theoretical support for the design of monitoring schemes in CCUS projects and to promote the large-scale application of fiber optic sensing technologies in carbon sequestration safety monitoring.