To investigate the rare earth element (REE) fractionation patterns, grades, and their influencing factors in different ore blocks of the Bayan Obo REE-Nb-Fe symbiotic deposit, and to provide guidance for research on REE patterns and directional mineral processing at Bayan Obo, elemental mass fraction was conducted using inductively coupled plasma mass spectrometry on samples from different ore blocks of TK13-04 core in the main orebody. Results show that two distinct REE fractionation patterns exist in the main orebody. The REE fractionation pattern of the middle dolomite Fe-REE ores differ from that of the upper and lower dolomite REE ores. The former exhibits an “initial upward inclination followed by rightward inclination” characteristic with increasing from La to Nd and then decreasing from Nd to Lu. In contrast, the latter shows a “rightward inclination” pattern with gradually decreasing from La to Lu. The middle dolomite Fe-REE ores have a lower mass fraction of total REEs compared to the upper and lower dolomite REE ores. The total REE mass fraction is closely related to the degree of light-to-heavy REE (LREE-HREE) fractionation, the REE fractionation pattern, and the total iron (TFe) mass fraction. Higher degrees of LREE-HREE fractionation and higher La/ Nd ratios correlate with higher total REE mass fraction. When the TFe is below 20%, there is no definitive correlation between TFe and the REE mass fraction. However, when the TFe mass fraction exceeds 20%, the total REE mass fraction decreases signifi cantly. Compared to the average proportions of individual light REEs (LREEs) in the mining area, the proportion of Ce in the middle dolomite Fe-REE ore block of TK13-04 is significantly lower ( ~37%), while the proportion of Nd is significantly higher ( ~40. 7%), exceeding the proportion of Ce that is typically the dominant REE in the deposit. The proportion of La in the middle dolomite Fe-REE ore block is also reduced ( ~9%) and the proportions of Pr and Sm are increased. Therefore, the middle dolomite Fe-REE ore blocks hold greater potential for the utilization of Nd, Pr, and Sm elements. For the utilization of La and Ce, development of the upper and lower dolomite REE ore block would likely yield more substantial benefits.

 The exploration focus of the Junggar Basin has gradually shifted from the hydrocarbon generation center of the sag to the peripheral uplift area. To understand the distribution characteristics of oil and gas reservoirs in the Dabasong Uplift, the authors have conducted research on the main controlling factors and development mode of high-quality reservoirs in the Dabasong Uplift by means of core observation, thin section identification, well logging interpretation and various experimental analyses. It is believed that the Dabasong Uplift was in the underwater reducing environment during the eruption period, and the carboniferous igneous rocks were mainly basic rocks, including diabase of volcanic channel facies, crystalline tuff of the underwater sedimentary subfacies, breccia clastic tuffs of the underwater volcaniclastic flow subfacies, basalt and andesite of underwater overflow facies, and sedimentary tuffs of the volcanic sedimentary facies. Among them, breccia clastic tuffs of the underwater volcani clastic flow subfacies and the diabase of volcanic channel facies are the dominant lithologic lithofacies in the study area. The breccia clastic tuff has more concentrated pore throat distribution, good connectivity and greater contribu tion of pore throat, while the diabase has more dispersed pore throat distribution and good connectivity. The reser voir space of igneous rocks in the study area is dominated by intrachrystalline solution pores, matrix solution pores, structural fractures and solution fractures. The porosity of carboniferous igneous rocks is less than 5% (51. 9%), between 5% and 12% (29.6%), more than 12% (18.5%), the permeability is less than 1 ×10-3μm2 (81. 5%), between (1 and 5) ×10-3μm2 (16.6%), and more than 5 ×10-3μm2 (1.9%), the whole belongs to the medium and low porosity-low permeability reservoir. The formation of effective reservoirs is controlled by lithologic lithofacies, dissolution and fractures, which is a weathering and dissolution modified reservoir based on lithologic lithofacies and dominated by fractures. The results show that the favorable zone for oil and gas exploration in areas with dominant lithological lithofacies, weathering and dissolution, active fluid dissolution and fracture development.

 Recent significant breakthroughs in natural gas exploration have been achieved in the tight sandstone reservoirs of the Bashijiqike Formation (Member 1) within the Cretaceous system of the Luntai S3 block, Tarim Basin, which has emerged as a crucial exploration target with substantial hydrocarbon resource potential. However, the reservoir characteristics and main controlling factors of these potential intervals remain insufficiently understood, constraining zone evaluation and target optimization in this area. Based on physical data including mud logging, well logging, core samples, and cuttings from existing wells, this study systematically investigated the petrological characteristics, reservoir space types, and physical properties of the tight sandstone reservoirs through thin-section analysis, cast thin-section observation, X-ray diffraction, mercury injection, and scanning electron microscopy. The results revealed that: (1) The reservoirs predominantly consist of lithic arkose and feldspathic litharenite with low compositional maturity (quartz is 59. 7%, feldspar is 23. 0%, lithic fragments is 17. 3%) and moderate tex tural maturity. The interstitial material (predominantly calcite) accounts for an average of 7. 1%. The reservoir space is dominated by residual primary intergranular pores (plane porosity is 2%) and intergranular dissolution pores (average pore diameter is 109. 65 μm), exhibiting bimodal pore-throat radius distribution (main peak at 1 μm). The average displacement pressure measures 0. 27 MPa, with porosity ranging from 8. 6% to 11. 9% and permeability between 0. 64 ×10-3 and 6. 10 ×10-3 μm2, collectively indicating low porosity and low permeability characteristics. (2) Reservoir heterogeneity was controlled by depositional environment, with medium-coarse sand stones in subaqueous distributary channels of braided river delta fronts (sandbody thickness proportion 42%-91%) being identified as effective reservoir development zones, where high-energy environments promoted better sorting and lower clay volume fractions. (3) Reservoir evolution was dominated by diagenesis: early calcite cementation (volume fraction 7. 1% to 11. 3%) inhibited compaction and preserved residual intergranular pores (porosity reduced to 5%-8%), while late dissolution created intergranular/ intragranular dissolution pores (maximum plane porosity is 15%), though local re-cementation intensified heterogeneity. (4) Structural uplift (Late Cretaceous strata exposure) enhanced epigenetic dissolution, with differential uplift causing spatial variations in dissolution intensity. The study concluded that effective reservoir development in Member 1 was jointly controlled by sedimentology diagenesis-tectonics coupling, with the subaqueous distributary channel sand bodies in the eastern 3-4 sand groups being prioritized exploration targets, particularly focusing on calcite cement dissolution zones and fault-modified areas.

 This study investigates members 2-3 of the Late Cretaceous Qingshankou Formation in the Gulong area of the Songliao Basin. By integrating core data, well-logging information, magnetic susceptibility and chromaticity measurements, wavelet transform analysis of logging curves, and cluster analysis, the coupling mechanism between high-frequency climatic oscillations and sequence development in continental lacustrine basins was systematically revealed. The principal findings are summarized as follows: (1) Two depositional environments lacustrine and deltaic facies were identified in the studied interval, which were further subdivided into six microfacies: deep-lake mud, semi-deep lake mud, shell sand, turbidite, prodelta mud, and sheet sand. A delta-lacustrine sedimentary evolution model was established to characterize their vertical stacking patterns. (2) Two complete third-order sequences were recognized based on lithologic associations, well-logging responses, and wavelet transform time-frequency analysis. These sequences were divided into four system tracts, lowstand (LST), transgressive (TST), highstand (HST), and regressive (RST). Sequence boundaries were characterized by GR curve inflection points, lithologic abrupt changes, and energy cluster transitions in time-frequency spectrograms. (3) Paleoclimate evolution was quantitatively reconstructed using magnetic susceptibility and chromaticity indices through SPSS cluster analysis. The results revealed a climatic cyclicity of “cooling-warming-cooling-warming-cooling” with an anomalously warm-humid phase during 86. 895-86. 364 Ma closely linked to lacustrine anoxic events (LAEs). This phase was attributed to enhanced hydrological cycling-triggered terrestrial coarse clastic input and intensified reducing conditions. Overall, paleoclimate governed sequence differentiation by regulating hydrological cycling and sediment supply. During warm-humid phases, strengthened water circulation promoted fine-grained mudstone deposition, forming TST and HST system tracts, whereas under relatively dry-cold conditions, weakened water circulation favored coarse-grained gravity flow sedimentation, developing RST and LST system tracts.

 To deeply reveal the paleoclimate characteristics of the Sifangtai Formation in the Songliao Basin, infer the controlling factors of climate change, and improve the information implied by climatic evolution, magnetic susceptibility and chromaticity tests were conducted on the core (140. 0-380. 5 m) from the ZKQA1-1 in the Qian􀆳an area. Through Pearson correlation analysis, it was found that magnetic susceptibility and chromaticity data were weakly correlated with stratigraphic depth. Brightness (L∗) was negatively correlated with redness (a∗) and yellowness (b∗), while redness (a∗) was positively correlated with yellowness (b∗), indicating that the data were minimally affected byagenesis and had reliable paleoclimate significance. Through SPSS cluster analysis, high-frequency magnetic susceptibility, low-frequency magnetic susceptibility, frequency-dependent magnetic susceptibility, brightness (L∗), redness (a∗), and yellowness (b∗) were filtered once, and four filtered mean values were obtained for each dataset. The filtered values were classified into four climatic types (cold-dry, relatively cold-dry, relatively warm-wet, and warm-wet) based on paleoclimate proxy indicators. To avoid errors caused by semi-quantitative analysis of climatic types using magnetic susceptibility and chromaticity data, the six datasets representing climatic types were assigned numerical values (cold-dry: 0. 5, relatively cold-dry: 1. 5, relatively warm-wet: 2. 5, warm-wet: 3.5), averages were calculated based on depth summation, and comprehensive temporal scale analysis was performed in combination with lithological and well-log data. The results showed that the magnetic susceptibility and chromaticity data of the Sifangtai Formation core from the Well ZKQA1-1 indicated a relatively cold-dry climate during the mid-Campanian (76. 08-75. 65 Ma), and the paleotemperature decrease in this stage was associated with the migration of the intertropical convergence zone (ITCZ). A relatively warm-wet to warm-wet climate was identified during the mid-late Campanian (75. 65-74. 32 Ma), with an extreme paleotemperature peak observed at 75. 55 Ma, while the slight paleotemperature decline from 75. 55 to 74. 32 Ma was linked to the Campanian Maastrichtian Boundary Event (CMBE). During the late Campanian (74. 32-73. 19 Ma), a cold-dry to relatively cold-dry climate was recorded, with an extreme paleotemperature minimum at 74. 13 Ma, followed by a relatively warm-wet climate from 73. 19 to 72. 86 Ma, and the paleotemperature rise from 74. 13 to 72. 86 Ma might be related to intermittent volcanic activity prior to the late Campanian Deccan Traps eruption event. Overall, the paleoclimate of the Sifangtai Formation was demonstrated to correspond well with global climate trends, representing a relatively warm and dry climatic type.

Multiple wells in the carbonate gas reservoirs of the Lower Permian Qixia Formation in the central
 Sichuan region of the Sichuan Basin have encountered industrial gas flows, but the anisotropy of the single well
 reservoir is strong and the physical parameters are obviously different, so it is difficult to guide the well location
deployment of this type of gas reservoir by conventional geophysical means. Therefore, to clarify the main controlling
 factors of the Qixia Formation gas reservoir, accurately predict the distribution of thin dolomite reservoirs, and
 further improve the development efficiency of the gas reservoir, the authors combine core, logging, seismic and
 other data to first determine the main controlling factors for the enrichment and high yield of the Qixia Formation gas
 reservoir in the study area, and summarizes the rock physical characteristics and seismic response patterns of the
 dolomite reservoir. Innovation has formed key technical processes for thin reservoir prediction, such as high-resolution
 processing technology, qualitative reservoir prediction based on wavelet reconstruction, fine characterization of ancient
 landforms constrained by layers, inversion technology constrained by reconstruction curves, and fracture and cave
 prediction technology constrained by porosity curves. This has improved the ability to vertically resolve thin reservoirs of
 1-8 m in the research area, and ultimately characterized a favorable area for the development of dolomite reservoirs
 as 75 km2. The research results indicate that: (1) There is an obvious positive correlation between the thickness
 of fracture-vuggy dolomite thin reservoir, fracture-cave development degree and productivity in the Qixia Formation
 gas reservoir in the study area. (2) Before the sedimentation of the Qixia Formation (paleogeomorphology), reservoirs
 were developed in the lower part, and the reservoir properties in the slope area during the karst period were better.
 (3) The reservoirs of Qixia Formation in the study area are mainly developed in the middle and upper parts, and
 the seismic profile shows the reflection characteristics of “weak peaks” in the middle and upper parts, and the more
 the reservoirs are developed, the more obvious the response characteristics of “weak peaks” are. This article
 combines seismic facies, paleogeomorphology, reservoir thickness, and fracture distribution parameters for the first
 time to establish a series of key technologies for predicting dolomite reservoirs suitable for the study area, achieving
 effective prediction of the spatial distribution of thin reservoirs in the Qixia Formation and laying the foundation for
 increasing reserves and production in oil fields.

The coupled models for selecting statistical methods and machine learning models to achieve better prediction results for geological hazard susceptibility assessment, as well as the interpretation of the contribution of model feature factors, are further studied in this paper. Two statistical methods (information value, IV and certainty factor, CF) and two machine learning models (support vector machine, SVM and random forest, RF) are combined to construct four hybrid models. Taking Panshi City as an example, the study investigates the effect of coupled models on the accuracy improvement compared to single statistical models, and selects the most accurate model to explain the contribution of various hazard-causing factors to the prediction results. The interactive self organization (ISO) clustering algorithm is first used to select non-harzard samples. Then, the information value (IV) method and the certainty factor (CF) method are combined with support vector machine (SVM) and random forest (RF) to obtain four coupled models (IV-SVM, CF-SVM, IV-RF, CF-RF) for training, respectively. The performance of the models is evaluated using confusion matrix and receiver operating characteristic curve. The natu ral break method is then used to generate the geological hazard susceptibility classification map (with five levels: very low, low, moderate, high, and very high). The geological hazard intensity index is used to evaluate the clas sification accuracy, and geological hazard susceptibility assessment is conducted. Finally, the Shapley additive ex planations algorithm is used to explain the best-performing model. The results show that the performance of the four coupled models is generally better than that of the single statistical models, with the CF-RF model achieving the best accuracy evaluation results of accuracy (0. 896), precision (0. 872), F1 score (0. 899), and AUC value (0. 959), which is an improvement of 0. 066, 0. 098, 0. 054, and 0. 059, respectively, compared to the single CF model. Most historical geological hazard points are distributed in high and very high susceptibility zones, and the geological hazard intensity index increases as the susceptibility level increases. Among them, the CF-RF model has the best classification effect. The Shapley additive explanations algorithm can help understand the reasons behind the model􀆳s decisions and the occurrence patterns of geological hazards. The study indicates that land use and road construction are the main inducing factors for geological hazard susceptibility in the study area.

When using machine learning techniques to identify lithology based on the feature vectors of each
 sample in the logging dataset, it is generally imperative to first clean and preprocess the dataset to detect missing
 data and outliers in it. Affected by the distortion of logging curves or the disparities in the quantity of curves
 between wells, the logging dataset used for lithology identification is usually incomplete, with a large amount of
 missing data and outliers. This renders it challenging for the majority of machine learning methods to be directly
 applied. To tackle this problem, a K-nearest neighbors algorithm based on the partial distance strategy (PDSKNN) is
 proposed. This algorithm improves the traditional K-nearest neighbors algorithm (KNN) based on a method (PDS)
 that capable of computing the distance between feature vectors containing missing values, enabling the direct appli
cation to incomplete dataset. The PDSKNN algorithm was experimentally implemented and tested in the lithology
 identification task of complex igneous rock reservoirs in a certain region. When the data missing rate was 2. 7%,
 the lithology identification accuracy of the PDSKNN algorithm was as high as 91. 90%. This result indicates that the
 PDSKNN algorithm effectively solves the problem of incomplete dataset at the algorithm level. For the purpose of
 further validating the effectiveness of the PDSKNN algorithm, certain data in the dataset was randomly deleted to
 elevate the missing rate, and the changes in the identification accuracy were observed. The experimental results
 reveal that as the missing rate increases, the identification accuracy gradually decreases. However, even when the
 missing rate attains 20%, the identification accuracy of the PDSKNN algorithm still remains above 80%. This
 result proves that the PDSKNN algorithm can maintain a relatively high identification accuracy in the case of severe
 data missing. Finally, by simulating the missing conditions of curves in different wells, a comparative analysis of
 the changes in the identification effect of the PDSKNN algorithm was carried out. As the number of missing curves
 increases, the lithology identification performance in the wells decreases to some extent, but the identification effect
 of most well sections is still guaranteed. It shows that the PDSKNN algorithm has strong robustness to the missing
 curves in the wells.

 In order to reveal the surface deformation characteristics and fault movement mechanism of the Luding
 earthquake of 5 September 2022, to mitigate the impact of seismic hazards, and to enhance disaster prevention and
 mitigation capabilities, the authors extracted the coseismic deformation results by using D-InSAR technology and
 inverted the geometric and kinematic parameters of the seismic faults by combining with the dislocation model. The
ascending orbit images on 26 August and 19 September 2022 and the descending orbit images on 2 September and
 14 September 2022 of the Sentinel-1 satellite from the European Space Agency are selected for the study. Through
 the dual-track differential interferometric processing, errors such as topographic phase and atmospheric delays are
 eliminated, and the coseismic deformation results are obtained with high accuracy. The results show that the surface
 deformation induced by the earthquake is significant, and the maximum uplift of 0. 16 m and 0. 13 m, and the
 maximum subsidence of 0. 24 m and 0. 18 m are detected in the ascending and descending orbits, respectively, which
 provide reliable observation constraints for the subsequent inversion of fault parameters. Based on the acquired
 ascending and descending orbits InSAR coseismic deformation results, the Okada elastic half-space dislocation model
 is used for the fault parameter inversion, and the optimal geometric parameters and kinematic characteristics of the
 seismic fault are determined by the nonlinear optimisation method. The inversion results show that the seismic fault
 has a strike of 169°, spreads along the NNW-SSE direction, has a dip of 72°, and a slip angle of-3°. In addition,
 the total seismic moment obtained from the inversion is about 2. 46 ×1019 N·m, corresponding to a magnitude of
 MW 
6.8, which is basically consistent with the observation results from global earthquake monitoring and research
 institutions.

To investigate the conductivity mechanism and fluid distribution characteristics of tight sandstone
 reservoirs in Ordos Basin and to construct an accurate saturation model, the authors conducted a series of petrophysical
 experiments, including porosity-permeability tests, resistivity measurements using the centrifuge method, and NMR
 (nuclear magnetic resonance) response analysis, on 11 rock samples from the study area. Nitrogen gas was used in
 the porosity-permeability experiments, revealing porosity values ranging from 5. 21% to 11. 26%, and permeability
 values are (0. 132 8-1. 261 1) ×10-3 μm2. The resistivity measurements under varying centrifugation durations
 provided key electrical parameters: the cementation exponent m (ranging from 2. 053 to 3. 293) and the saturation
 exponent n (ranging from 0. 714 to 2. 131). The NMR technique, which leverages the relaxation time of hydrogen
 nuclei within the rock, was used to characterize reservoir pore structures and fluid mobility. The T2 
spectrum
 obtained through NMR enabled the distinction of pore sizes and their proportions. Results indicated that the tight
 sandstones in the study area exhibit similar properties and pore structures. The T2 
spectrum showed a wide distribution
 (0. 1-1 000. 0 ms), typically presenting a bimodal pattern with a boundary at 10 ms. Based on empirical methods,
 mercury intrusion data, and NMR T2 
spectrum characteristics, the T2 
range for small pores was defined as 0. 01
5 ms, and for large pores as 5-10 000 ms. The findings show that fluid mobility is significantly higher in large
 pores than in small ones, and water content in small pores increases during centrifugation. After converting T2
 values into corresponding pore diameters, it was found that movable water is mainly distributed in the 1-10 μm
 range, followed by 0. 01-0. 10 μm and 0. 10-1. 00 μm ranges. Using the fractal dimension method, a positive
 correlation was established between fluid distribution and electrical conductivity, which is greater fluid presence in
 large pores corresponds to smaller fractal dimensions and higher conductivity.

A novel integrated learning-based method for igneous reservoir fluid identification is proposed to address the limitations of traditional approaches in handling complex lithological variations and heterogeneous reservoir spaces, which are crucial for global oil and gas resource development. In this paper, the adaptive multi-objective swarm crossover optimization (AMSCO) innovatively combined with an engineered extreme gradient boosting (XGBoost) based on deep forest method for fluid identification in complex lithologic igneous reservoirs using conventional logging data set. Methodologically, firstly, the AMSCO algorithm is used to optimize the imbalanced conventional logging data set, effectively solving the problem of class imbalance in the data set, providing a more balanced data basis for subsequent model training. Secondly, a cross-adaptive XGBoost and deep forest (CXDF) is constructed by fully utilizing XGBoost􀆳s advantages in processing large-scale and high-dimensional data, as well as the excellent performance of deep forest in feature extraction and classification tasks. Thus, the accurate identification of reservoir fluids in complex lithologic igneous rocks is achieved. Then, to verify the effectiveness of this method, the model was applied to the simulated well together with support vector machine (SVM), XGBoost and XGBoost based deep forest for comparison. Finally, the model is applied to the actual stratum. The results show that the evaluation index of the proposed method in the simulated well is superior to other methods, especially in the identi fication of non-water-producing reservoir fluids. In the application to actual formations, this method maintains high identification performance in different reservoirs with different fluid structures, and shows good generalization ability and stability.

This study is the first to analyze the geothermal resource occurrence conditions and the formation evolution mechanisms in the southern part of Taiyuan City, Shanxi Province, from both geothermal geophysical and geothermal water isotope geochemical perspectives. Based on geophysical exploration data and geothermal water isotope chemical analysis, and combined with regional geological data, a comprehensive study of the geothermal field in southern Taiyuan is conducted. The geophysical methods primarily include gravity, magnetic, and electrical surveys, which are used to analyze the structural characteristics and reservoir distribution of the geothermal field. The geochemical methods mainly include hydrochemical and isotopic analyses, aimed at revealing the origin, evolution, and water-rock interaction processes of thermal groundwater. The geophysical exploration results indicate that the Taiyuan Basin as a whole exhibits a low gravity field and negative magnetic anomalies, reflecting a deeply buried basement and strong tectonic activity, which provide favorable heat sources and reservoir spaces for the formation of geothermal fields. The southern geothermal field of Taiyuan is located at the southeastern margin of the basin, where the gravity field shows a distinct gradient zone, and the magnetic anomalies are characterized by negative values, suggesting the presence of concealed fault structures that serve as channels for deep thermal flow upwelling. Moreover, an apparent arcuate low-resistivity zone exists within the geothermal field, inferred to represent the geothermal reservoir, further demonstrating the favorable conditions for geothermal resource accumulation in this area. Geothermal water isotope geochemical analysis reveals that the thermal groundwater in the southern Taiyuan geothermal field originates from meteoric water, undergoing processes of infiltration, leaching, and transformation into sedimentary (semi-confined) water. During this process, water interacts with surrounding rocks through leaching, dissolution, and cation exchange. Stable components accumulate in the groundwater, while unstable components precipitate, thus forming the hydrogeochemical characteristics of modern geothermal water. The formation of the geothermal field in southern Taiyuan is mainly controlled by the favorable regional geological structure, the development of concealed faults, and the good storage capacity and permeability of the geothermal reservoir.

 The Wuzhong-Lingwu area lies within the Taole-Hengshanpu thrust fault zone, characterized by complex geological structures. Aiming to address the exploration needs for active fault development and the unclear deep geothermal reservoir structures in the southern section of the Yinchuan Basin (Taole-Hengshanpu thrust fault zone), this study employs the microtremor-based spatial autocorrelation method (SPAC) using background noise. By analyzing the recorded Rayleigh wave signal data, the authors effectively inverted the high-resolution shear-wave velocity structure of the subsurface medium in the study area, investigated the subsurface structural development in the Wuzhong-Lingwu area, and evaluated the application of the background noise method in detecting subsurface faults and geothermal resources. The S-wave velocity structure inversion results obtained by the SPAC technique revealed key stratigraphic and structural features. Comparison with previous well-logging and electrical prospecting data confirmed the reliability of the recorded observations. The results indicate that the depth range of 700-1 000 m corresponds to the Upper Carboniferous (Ct) strata, where a slight decrease in S-wave velocity suggests the presence of an aquifer, consistent with the distribution of geothermal water resources in the area. Between 1 000 and 1 500 m, S-wave velocity increases again, indicating Ordovician (O) strata, with a thin, slightly lower-velocity layer at the top interface, also inferred to be an aquifer. Based on the velocity anomalies and spatial distribution of aquifers, it is concluded that the Upper Carboniferous aquifer (700-1 000 m) and the thin aquifer at the top of the Ordovician strata (1 000-1 500 m) possess favorable conditions for geothermal fluid storage and migration, making them highly promising target geothermal reservoirs. These two stratigraphic units are likely to host geothermal resources.