As oil and gas exploration expands into new frontiers such as deep water and deep strata where drilling is sparse, conventional well-log-constrained acoustic impedance inversion faces significant challenges. Developing well-free inversion techniques that reliably estimate absolute acoustic impedance from seismic data and other available information—without relying on well logs—is of great importance for reducing exploration risks and enabling early reservoir evaluation. From the perspective of the information source and core mechanism of low-frequency compensation, this paper constructs a systematic taxonomy of well-free acoustic impedance inversion methods covering four technical pathways: (1) methods based purely on seismic data (using traveltime information, sparse priors, or wave equation to recover low frequencies); (2) methods based on integration of multi-geophysical-field data (using gravity, magnetic, electromagnetic data to provide ultra-low-frequency structural framework); (3) methods based on geological and statistical prior modeling (using pseudo-wells, geological frameworks, or geostatistical simulation to introduce prior knowledge); and (4) data-driven and artificial intelligence methods (using deep learning to implicitly learn the complex mapping from seismic data to acoustic impedance). The paper elaborates the principles, low-frequency recovery mechanisms, advantages, and limitations of each method. Through comparative tables and a case study in a deep-water well-free area, it reveals the evolutionary trend from single information sources to multi-mechanism integration. The study shows that no single technical pathway can independently overcome the strong non-uniqueness challenge of well-free inversion. Promoting deep integration of physics-based modeling and data-driven intelligence, along with synergistic utilization of multi-source information and multi-method approaches, is the key to narrowing the current technical gap and enhancing the reliability of well-free inversion. This paper aims to provide a clear theoretical framework and practical guidance for quantitative reservoir evaluation and risk decision-making in the early exploration of well-free areas.
Periodic mobile sampling was implemented for observation wells across the Zhangjiakou area. Major elements, trace elements, hydrogen–oxygen isotopes, and gaseous He–Ne isotopes of water samples were measured to characterize hydrogeochemical signatures. The results demonstrate that thermal springs in the study area are classified into four hydrochemical types: Na-SO4, Na-Cl, Na-HCO3 and Ca-HCO3 types. In terms of trace element composition, typical geothermal indicative elements including Li, B, Sr and Mo are markedly enriched, whereas transition group elements such as Fe, Co and Ni are generally depleted, which matches the geochemical properties of low-to-medium temperature geothermal fluids. The circulation depth of thermal springs in Zhangjiakou ranges from 1.28 km to 2.50 km, the recharge elevation varies between 1646.66m and 2185.00 m, and the reservoir temperature is estimated at 56.11°C–98.78 °C. Stable hydrogen and oxygen isotope analysis reveals that all thermal spring samples plot slightly below both the Chinese Meteoric Water Line and the Meteoric Water Line of Eastern China, verifying that geothermal groundwater is recharged dominantly by atmospheric precipitation. Measured He and Ne isotopic ratios display obvious spatial differentiation, which is primarily controlled by the development intensity of regional fault structures and the mixing proportion of shallow cold groundwater. A comparative study was further carried out between fixed-site periodic hydrochemical observations and crustal deformation rates. From 2015 to 2025, regional seismicity was dominated by moderate and small earthquakes with local magnitudes ML 1.0–ML 3.0, featuring alternating active and quiescent episodes, and no strong earthquakes of M≥5.0 occurred. The stable hydrogeochemical background is well consistent with the overall seismic activity level, indicating that the contemporary tectonic activity of major fault zones within the research area is generally weak.
Based on the data of the Equivalent Blackbody Brightness Temperature product from the Fengyun-2 geostationary meteorological satellite, and using wavelet analysis and relative power spectrum estimation methods, this study selected the North China region (30°—43°N, 108°–124°E) and collected 19 moderate earthquakes with M≥4.6 that occurred from 2006 to 2025. By analyzing their thermal infrared anomaly characteristics, the results show that 16 of these earthquakes were preceded by significant thermal infrared anomalies. The evolution of these anomalies generally followed a pattern: anomaly emergence → gradual enhancement and persistence → weakening and disappearance. However, the characteristic periods of anomalies varied among earthquakes, mainly within the first three periods. Most earthquakes occurred during the ascending or descending phases of the anomalies (i.e., the transition periods), suggesting potential significance for short-term earthquake prediction. The maximum amplitude of the anomaly does not show a positive correlation with earthquake magnitude—some smaller earthquakes exhibited larger amplitudes—making it difficult to estimate the magnitude of an earthquake based solely on peak values. The findings of this study can provide a reference for earthquake prediction research using thermal infrared data in the broader North China region.
With the continued growth in global demand for mineral resources and the gradual depletion of shallow mineral resources, deep and concealed deposits have become an increasingly important mineral exploration target. Conventional single-method exploration techniques face numerous challenges under complex geological conditions. In contrast, remote sensing and geophysical exploration technologies, based on air-ground-well integration, provide novel approaches to mineral exploration. This study presents a systematic review of the development history and current application status of various airborne geophysical technologies (i.e., airborne magnetics, airborne radiometrics, airborne gravimetry, and airborne electromagnetics) and remote sensing technology in mineral exploration, with particular emphasis placed on their recent advances in integrated mineral prospecting based on multi-source data. The results indicate that airborne geophysical technologies have evolved from the medium- and low-precision measurement stage into the high-resolution comprehensive measurement stage. Meanwhile, remote sensing technology has evolved from multispectral remote sensing into diverse directions, including hyperspectral remote sensing and synthetic aperture radar (SAR). By integrating multi-scale, multi-parameter geophysical data, the air-ground-well integrated exploration framework has significantly enhanced the capability to detect concealed deposits. Future development directions include intelligent data processing, the application of unmanned aerial vehicle (UAV) platforms, the integration of deep learning algorithms, and further innovations in multi-source data fusion technology. Overall, this review provides a valuable theoretical reference for the technology selection and method optimization of mineral exploration.
To investigate the evolution characteristics and underlying mechanisms of thermal deformation in sandstone under high-temperature environments, sandstone samples from the Wangzhuang Mine were evaluated. Complete-process thermal deformation tests comprising heating and isothermal holding stages were conducted from 400 °C to 900 °C. The evolution of the meso-structure, variation in sample height, and coefficient of thermal expansion (CTE) were systematically analysed. The results indicate that the residual microstructure of sandstone changes from a relatively compact state to a loosened, deteriorated state after high-temperature treatment. During the isothermal holding stage, the sample height variation increases gradually at 400 °C–700 °C, whereas the growth rate decreases at 800°C–900 °C, indicating that high-temperature-induced structural deterioration begins to suppress further deformation accumulation. The CTE generally increases with temperature during the heating and isothermal holding stages, and the CTE-temperature relationship exhibits a nonlinear increasing characteristic described by logistic fitting. The logistic fitting relationships obtained in this study provide a reference for describing the high-temperature thermal deformation behavior of sandstone under the present experimental conditions. The response around 700 °C indicates that high-temperature-induced microstructural adjustment begins to increasingly affect the thermal deformation behavior, resulting in weakened height growth during isothermal holding and a reduced CTE growth rate at higher temperatures. The findings provide practical guidance for the design, construction, and surrounding rock stability assessment in deep, high-temperature geotechnical and underground engineering applications.
The discontinuous Galerkin (DG) method shows great application potential for solving wave equations in computational geophysics, because of its excellent ability to accurately capture discontinuous solutions, suppress numerical dispersion, and support flexible mesh adaptation. In this study, we derive an optimized staggered Adams scheme. A key advantage of the staggered Adams scheme is its high computational efficiency, and the optimized formulation we propose is specifically designed to achieve optimal numerical stability. We combine the scheme with the interior penalty discontinuous Galerkin (IPDG) method for solving wave equations. Comprehensive analyzes of numerical stability and dispersion for acoustic equation are conducted, which demonstrate that the developed method effectively improves computational efficiency and maintains the ability to suppress numerical dispersion. We perform numerical experiments on multiple benchmark models for the staggered Adams DG method, including the isotropic model, three-layer model, corner model, and Marmousi model. The results show that the computational efficiency of the proposed method is more than 3 times that of the traditional third-order classic Runge–Kutta DG method. In addition, numerical tests on interface models confirm that the method introduces no obvious numerical dispersion at media interfaces. Overall, the proposed method has favorable prospects for large-scale wavefield simulations.
Flight altitude is a critical parameter in airborne geophysical survey, as it directly affects both the data quality and the magnetic anomaly resolution of measured signals. Compared with conventional airborne total-field magnetic survey, airborne vector magnetic (VM) survey is more sensitive to aircraft attitude, making the relationship between flight altitude and data noise more complex. Furthermore, both data noise and magnetic anomaly resolution can significantly impact inversion results. Based on measured data acquired at multiple flight altitudes, this study analyzes how data noise varies with altitude and applies the magnetization vector inversion (MVI) method to the corresponding datasets. The results indicate that, unlike airborne total-field magnetic data, the noise level in airborne VM data is less dependent on flight altitude, highlighting the inherent complexity of airborne VM survey. MVI results further reveal that flight altitude has a relatively minor influence on the magnetization direction, but exerts a more pronounced effect on the resolution of shallow magnetic sources, the differentiation of magnetization magnitude values, and the correlation among magnetic bodies. These findings provide useful guidance for designing flight parameters in airborne VM surveys and for evaluating the reliability of MVI results. They also offer practical insights for improving the quality and application effectiveness of airborne VM data.
Aiming at the strong nonlinearity and ill-posedness of prestack seismic inversion in scenarios with scarce logging data, this paper proposes a physics-guided geological augmented clustering enhanced generative adversarial network (GAC-GAN) framework. The core innovation of this method lies in the proposed physics-guided geological facies clustering (PGFC) strategy. By extracting the AVO intercept-gradient attributes and spectral features of seismic gathers, it performs K-means clustering with clear geological significance, divides subsurface media into several geologically homogeneous groups according to petrophysical response characteristics, and defines each geological facies cluster as a targeted subtask. PGFC primarily targets few-shot adaptation under scarce well control, which in turn improves the the inversion accuracy of S-wave velocity and density. This clustering mechanism ensures that the subtask division corresponds to the real subsurface geological structure. The framework further integrates a dedicated data augmentation strategy for prestack processing distortions and the Aki-Richards forward physical constraint. Tests on synthetic data and actual work areas show that under extreme conditions with only a very small number of training wells, GAC-GAN is significantly superior to traditional generative adversarial networks in terms of inversion accuracy, lateral continuity and noise robustness of P-wave velocity (Vp), S-wave velocity (Vs) and density (ρ).
The resurgence of spring and well water is a direct manifestation of groundwater level recovery. From the perspectives of geological structure, hydrogeochemical response, geophysical response, the South-to-North Water Diversion Project, ecological water replenishment, and groundwater over-exploitation control, this paper systematically analyzes the causes of typical spring and well water surge events in central and southern Hebei in recent years. The primary conclusions are drawn as follows: ➀The buried depth of the water level at Xingtai Baiquan Spring is relatively shallow, and the recovery magnitude of the shallow groundwater level is affected by rainfall; the buried depth of groundwater is negatively correlated with the South-to-North Water Diversion and ecological water replenishment. By contrast, the water level of Baoding Yimuquan Spring features a relatively large buried depth and is less influenced by rainfall, with its shallow groundwater regime mainly controlled by ecological water replenishment. ➁The spontaneously resurged groundwater at Xingtai Baiquan Spring and Baoding Yimuquan Spring is predominantly shallow groundwater, belonging to the bicarbonate water type and immature water. Isotope tracing indicates that the groundwater is primarily recharged by atmospheric precipitation with limited deep crustal information. The rock mass fractures in the study area are moderately developed, and the stress state of the aquifer medium has no obvious variation, implying no significant change in the regional stress field. Accordingly, the surge of spring and well water exhibits no obvious precursory characteristics of seismic activity. ➂The evolutionary processes of Xingtai Baiquan Spring and Baoding Yimuquan Spring clearly demonstrate the complete evolution path of water replenishment – exploitation reduction – water level rise – spring and well resurgence in the spring domains of central and southern Hebei. It is verified that the coordinated implementation of the South-to-North Water Diversion Project, ecological water replenishment and groundwater over-exploitation control constitutes the core approach for ecological restoration of spring domains. ➃Based on comprehensive hydrogeochemical and geophysical analysis, this study clarifies the non-seismic genetic attribute of typical spring and well water surge in central and southern Hebei, and reveals the anthropogenic driving mechanism of the evolution path of water replenishment – exploitation reduction – water level recovery – water resurgence. The research findings not only provide a scientific case for the verification of seismic anomalies and effectively respond to public concerns, but also offer empirical evidence for water resources management and ecological restoration effect evaluation in groundwater over-exploitation areas of North China.
Tight sandstone and gravel oil and gas reservoirs constitute a significant category of unconventional hydrocarbon resources, typically exhibiting low porosity and low permeability, complex pore structures, and high heterogeneity, which make the identification and prediction of “sweet-spot reservoirs” particularly challenging. This study focuses on the Upper Urho Formation tight gravelstone reservoirs in the Fudong Slope area of the Fukang Depression, Junggar Basin. Following a comprehensive review of domestic and international research progress on the genesis mechanisms, pore structure characteristics, and prediction methods of tight reservoirs, the study integrates core experiments, well logging data, and petrophysical analysis to accurately characterize the reservoir’s micro-pore structure and physical properties. The results indicate that the reservoir exhibits a complex pore structure and high heterogeneity, with macroscopic sedimentary facies, micro-scale throat structures, and fracture development jointly controlling reservoir quality and spatial distribution. Based on these findings, a rock physics model consistent with the geological characteristics of the study area was established, identifying sensitive logging response parameters for sweet-spot intervals, thereby achieving an effective coupling between petrophysical parameters and geological characteristics and establishing a method for the identification and prediction of sweet spots in tight gravelstone reservoirs. The research findings provide a theoretical basis and technical support for the detailed vertical identification of sweet spots and the prediction of spatial distribution of high-quality reservoirs, holding significant guiding value for the efficient exploration and development of unconventional oil and gas resources.
Occam inversion inherently produces smooth models, making it challenging to accurately resolve the geometries and sharp boundaries of complex structures such as faults, with topographic variations further distorting the inverted electrical images. Post-stack seismic reflection profiles provide precise identification of stratigraphic and fault interfaces, while independent seismic surveys yield prior information on subsurface S-wave velocity structures. To leverage these complementary datasets, we propose a two-dimensional magnetotelluric (MT) inversion method that accounts for topographic effects and is jointly constrained by seismic reflection interfaces and cross-gradient structural coupling. Within the Occam inversion framework, seismic reflection envelopes are first extracted from migrated profiles via the Hilbert transform. Combined with topographic conditions, the terrain-adaptive seismic constraint matrix is calculated from the envelope data, and this matrix is incorporated into the model regularization term. Furthermore, a topography-incorporated cross-gradient term is introduced to enforce structural similarity between the resistivity model and a prior seismic Vs model. Synthetic tests demonstrate that this dual-constrained inversion yields more accurate delineation of anomalous bodies and sharper structural boundaries, particularly for low-resistivity features, compared to conventional smoothness-constrained or single-constraint inversions. Application to field data from the Longmen Shan fault zone reveals improved data fit and electrical structures that are more consistent with interpreted seismic reflection interfaces. Notably, the approach improves the resolution of fault zone geometries and delineates stratigraphic contacts with greater precision within the study area.
Mine seismic monitoring is a core technical support for geological disaster early warning in intelligent mine construction, with precision directly tied to mining production safety. Deep learning has become a mainstream method for mine seismic phase identification due to superior nonlinear feature extraction, yet its practical application is severely hindered by the label-scarcity dilemma—high professional thresholds and time costs of manual annotation cause a severe shortage of high-quality labeled microseismic data in engineering practice. To address this, this paper proposes Tri-EQU, a Tri-training-based semi-supervised automatic labeling model integrating three heterogeneous complementary neural networks (EQTransformer, U-Net++, Gated Recurrent Unit (GRU)) to tackle mine microseismic signals’ inherent characteristics: strong mining noise, multi-scale feature coupling, and long-term temporal dependencies. Specifically, EQTransformer suppresses noise and captures salient phase features via self-attention; U-Net++ realizes multi-scale feature extraction and adaptive fusion through dense skip connections; GRU models long-term temporal correlations of seismic time-series via gating mechanisms. Comparative experiments on the 2011–2017 Shanxi mine microseismic dataset show that with only 20
On April 3, 2024, an Mw 7.4 oblique-slip earthquake occurred in the sea near Hualien City, Taiwan, China. To investigate the directivity effects of this event, this study analyzed the spatial distribution of response spectral amplitudes at different periods using 1,153 strong-motion records obtained from the Taiwan Strong Motion Instrumentation Program (TSMIP) and the P-Alert Strong Motion Network. The observed spectral amplitudes were compared with the predictions ground-motion attenuation relationships for northeastern Taiwan, China. Long-period ground motions were examined through the spatial distribution of spectral acceleration residuals. The results show that, at periods greater than 1.0 s, systematic differences exist across different fault azimuths. In the forward direction of the rupture propagation, the response spectral values are significantly higher than the attenuation relationship predictions, whereas in the backward direction of the rupture propagation, the spectral values are significantly lower, indicating a pronounced directivity effect. Based on these observations, a parameter is proposed to quantify the directivity effect with respect to the northeastern Taiwan ground-motion attenuation relationship, and its effectiveness is validated by fitting against the residuals.
Acoustic impedance inversion is an important approach for reservoir characterization, but MCMC-based facies-controlled geostatistical inversion may suffer from blurred facies boundaries and cross-facies impedance mixing when local lithological variations are not sufficiently constrained. To address this problem, an attribute-probability-constrained facies-consistent ensemble smoother with multiple data assimilation (ES-MDA) geostatistical inversion method is proposed. In the proposed workflow, seismic attributes calibrated by well facies information are first transformed into initial facies probability models. These attribute-derived facies probabilities are then embedded into each ES-MDA assimilation step as local probabilistic constraints. During impedance updating, the facies probability is used to weight the ES-MDA update increment and guide facies-consistent model correction, thereby reducing unreasonable cross-facies impedance mixing. After each assimilation step, the updated impedance response is used to correct the facies probability through facies-dependent impedance statistics. In this way, the proposed method establishes an iterative coupling between impedance updating and facies-probability correction, rather than using attribute-derived facies information only as post-inversion interpretation information. A synthetic thrust-fold model and a field post-stack seismic profile from a clastic reservoir area are used to test the method. In the synthetic example, the proposed method reduces the impedance RMSE from 428 to 241 and increases the correlation coefficient from 0.952 to 0.981 compared with MCMC-based facies-controlled geostatistical inversion. The facies classification accuracy increases from 86.7
Distinct lithological characteristics serve as critical indicators for identifying potential reservoirs. Leveraging the high vertical resolution of well logging data, machine learning-based lithology identification has been widely applied in hydrocarbon exploration; yet, its application in mineral exploration—where geological conditions are often more complex—remains limited. This study focuses on the eastern saltlake region of the Qaidam Basin, using 2248 data points from a single well (ZK20) derived from six well-logging curves to form a feature matrix of 2248 rows × 7 columns, with the last column representing lithology labels. The data were randomly split into training and testing sets in an 80
Suppressing strong cultural interference in magnetotelluric (MT) time series while preserving low-frequency geological responses remains a major challenge in MT data processing. We propose a joint denoising framework that follows a “detect-then-reconstruct” strategy and integrates discrete wavelet transform (DWT), recurrence quantification analysis (RQA), fuzzy C-means (FCM) clustering, and a UNet++–BiLSTM reconstruction network. DWT is first used to remove ultra-low-frequency baseline drift. The baseline-corrected signal is then segmented into frames, from which RQA features are extracted and clustered by FCM to automatically identify contaminated frames. Only the detected noisy frames are selectively reconstructed by the UNet++–BiLSTM model, avoiding unnecessary modification of high-quality segments. Experiments on both synthetic and field MT data show that RQA features effectively discriminate nonlinear dynamical behaviors of clean and contaminated frames, and the RQA–FCM detector provides robust localization of strong interference. Compared with conventional wavelet denoising and standalone deep learning models, the proposed UNet++-BiLSTM method achieves superior performance in terms of SNR, MSE, and correlation-based metrics. For field data, the processed apparent resistivity and phase curves exhibit substantially reduced low-frequency jumps and improved continuity, providing higher-quality inputs for subsequent impedance estimation, inversion, and interpretation.
Slow earthquakes, occurring in the form of ultra-low-frequency events in the source region before a mainshock, are of great significance for understanding the seismogenic physical processes of the mainshock. Targeting the Ms 4.7 Alxa Left Banner earthquake on July 26, 2025, and the Ms 5.8 Alxa Left Banner earthquake on April 5, 2015, both located at the junction of the northeastern margin of the Tibetan Plateau and the western margin of the Ordos Block, we selected continuous waveform data from seismic stations in the border region of Inner Mongolia, Gansu, and Ningxia. The data were corrected and processed with band-pass filtering. Using Fast Fourier Transform and Short-Time Fourier Transform, we detected ultra-low-frequency tremor events occurring before the mainshocks, analyzed their spectral and temporal characteristics, and continuously tracked the amplitude maxima variations in the dominant frequency band. This allowed us to observe the spatiotemporal patterns of pre-earthquake low-frequency anomalous signals and their correlation with source distance and tectonic setting. The spectral analysis results indicate that: (1) Seismic waveforms recorded at stations near the source region before the mainshocks simultaneously exhibit low-frequency shifts and low-frequency microseismic anomalies in the 0.005–2.0 Hz band, consistent with the spectral shift laws observed in rock fracture experiments. (2) Low-frequency amplitude anomaly events commonly occur before earthquakes. In the 9–10 days prior, their frequency increases and inter-event intervals shorten. These significant variations in amplitude and frequency show spatiotemporal correlation with the critical instability of the mainshock and can serve as candidate indicators for future case testing. (3) These long-period ultra-low-frequency tremors display narrow-band characteristics, with peak frequencies mainly concentrated in the 0–0.3 Hz range and a dominant peak at 0.05–0.10 Hz. The duration of a single event ranges from 1 to 3 hours, distinctly different from that of the mainshock. The signals are more prominent near fault zones, presumably related to the transition of deep faults from stable sliding to stick-slip and associated stress adjustments. (4) The epicenter is located at the junction of the northeastern margin of the Tibetan Plateau and the Ordos Block, a complex tectonic zone of multi-block interaction. The spectral shift characteristics of each station and the variation patterns of MASE curves are in good agreement with the stress conduction direction in the Alxa region. Future work will expand the study area and accumulate more typical earthquake cases to deepen the understanding of impending earthquake patterns.
Mine return air is characterized by relatively stable temperature and humidity, high moisture content, and abundant latent heat, making it a low-grade waste heat resource with high utilization potential. However, in existing mine return air waste heat recovery projects, considerable discrepancies are often observed between the designed heat transfer performance of heat exchangers and their actual operating performance. To address this issue, this study takes an indirect finned heat exchanger used for mine return air waste heat recovery as the research object and focuses on the condensation heat transfer characteristics under high-humidity return air conditions. Based on the enthalpy difference method, Threlkeld’s heat and mass transfer theory under humid conditions was introduced to model the coupled sensible and latent heat transfer processes on the mine return air side. A single-element heat transfer model incorporating a wet-fin efficiency correction model was developed to account for condensation heat transfer under high-humidity conditions. The thermal performance of the heat exchanger was then evaluated using a row-by-row numerical solution method. Furthermore, the model predictions were validated using on-site operational data from a mine return air waste heat recovery project in Yulin, Shaanxi Province. By analyzing the variations in return air outlet temperature, overall heat transfer coefficient, logarithmic mean temperature difference, and heat transfer rate under different inlet temperatures of the ethylene glycol solution, the influence mechanisms of coolant temperature and flow conditions on heat transfer performance were systematically revealed. The results indicate that the calculated results show good agreement with the measured data in both trend and magnitude, demonstrating that the proposed model can accurately capture the actual heat transfer characteristics of mine return air waste heat recovery systems under humid operating conditions. The findings provide a theoretical basis for the design optimization and operational parameter selection of heat exchangers used for mine return air waste heat recovery..
The Yangxia Sag in the eastern Kuqa Depression is a deeply buried Jurassic exploration domain in which the occurrence and regional continuity of coal-measure source rocks remain incompletely constrained because of sparse well control and complex foreland deformation. This uncertainty has hindered evaluation of whether the sag can host an independent Jurassic petroleum system. Using well logs, geochemical data, stratigraphic correlation, seismic interpretation, and post-stack acoustic-impedance inversion, this study evaluates Jurassic coal-measure source rocks in the Yangxia Sag. The fourth members of the Kizilenur and Yangxia formations are identified as the principal source-rock intervals, with coal-bearing successions characterized by high organic richness, moderate maturity (Ro mostly ∼0.9–1.2
Temporal coupling between reservoir densification and hydrocarbon charging critically controls accumulation efficiency in tight sandstone systems but remains poorly constrained in the Yangxia Sag, Kuqa Depression. This study investigates Jurassic tight reservoirs in the eastern Yangxia Sag using integrated petrographic observations, fluid inclusion analysis, burial–thermal history modeling, and quantitative porosity evolution reconstruction. The Jurassic reservoirs are dominated by lithic sandstones with low porosity (1.0–11.3
