Carbonate laminae in saline lacustrine shale sequences are widely developed, and their diverse combinations and spatial stacking patterns represent a key geological factor controlling the differential enrichment of lacustrine shale oil. These laminae, composed of micro- to millimeter-scale thin layers of minerals such as calcite and dolomite, serve as preferred targets for saline lacustrine shale oil “sweet spots” due to their significant economic potential and development prospects. The laminae exhibit various structures, including cryptocrystalline, sparitic, and fibrous structures, which originate from diverse formation mechanisms—mechanical, physicochemical, and biochemical processes—leading to distinct mineralogical and structural heterogeneities. In terms of pore-fracture systems, carbonate laminae develop advantageous pore types such as intercrystalline, dissolved, and intergranular pores. Their high brittleness promotes the formation of heterogeneous fracture networks, including bedding-parallel and structural fractures. During diagenesis, organic acid dissolution and recrystallization further enhance pore connectivity, significantly improving in storage capacity and permeability. This facilitates the micro-migration and enrichment of light hydrocarbons within the laminae, forming movable oil. The control of carbonate laminae on shale oil sweet spots is manifested in four aspects: extensive distribution facilitates segmental and areal sweet spot development; high organic matter content provides the material basis for hydrocarbon generation; high-quality source-reservoir configurations promote near-source migration and enrichment; high brittleness enhances reservoir fracability and development potential. In conclusion, detailed study of carbonate laminae provides critical guidance for shale oil exploration and sweet spot prediction in saline lacustrine basins.

Substantial advancements have been achieved in the exploration of Gulong shale oil in Songliao Basin. Pronounced disparities exist in oil-bearing capacity and productivity between the pure shale at the base of the Qingshankou Formation and the sandy interlayer at its top. However, the investigation of the microscopic characteristics underlying the oil-bearing differences in millimeter-scale assemblages of relatively high-yield lamina shale remains ambiguous, thereby fueling debates regarding the evaluation and genesis mechanisms of oil-bearing shale. This study integrates multiple analytical approaches, including conventional thin-section petrographic analysis, organic geochemical characterization, X-ray diffraction (XRD), argon ion polishing scanning electron microscopy observations, gas adsorption experiments, and thermal simulation experiments for hydrocarbon generation. The dark, organic-rich laminae and the bright, organic-poor laminae in the core of the Guye 3HC well section are analyzed to identify distinct lamina combination modes. The relationship between hydrocarbon generation, reservoir characteristics, and oil-bearing properties of these different lamina combinations during the hydrocarbon generation and storage processes is investigated. The results are obtained in four aspects. First, based on the total organic carbon (TOC) content and lamina types, the shale of the first member of Qingshankou Formation is categorized into two types: “organic-rich matter mixed” and “ organic-poor matter felsic” binary lamina combination modes. Second, Thermal simulation experiments were performed on samples from both lamina combination types. During the hydrocarbon generation process, the organic-rich mixed lamina combination, which is characterized by a high TOC content, exhibited a higher oil-bearing evaluation index (S₁). This outcome of S₁ during the storage process was primarily influenced by clay minerals, specifically the illite-montmorillonite mixed layer. In contrast, the organic-poor matter felsic lamina combination mode demonstrated superior reservoir development potential. Third, the oil content reaches its peak when the vertical thickness ratio of organic-rich to organic-poor laminae in the Gulong shale is 2:1. At this ratio, the TOC content of the organic-rich laminae exceeds 2.15%, while the clay mineral component content exceeds 55%. This combination exhibits strong hydrocarbon generation potential but relatively poor reservoir capacity. Following hydrocarbon generation, the hydrocarbons migrate into the reservoir space provided by the organic-poor laminae. The “2:1 combination mode” represents the optimal source-to-reservoir ratio for developing “lamina-type” shale reservoirs. Fourth, the 2:1 combination model, defined at the millimeter scale, was identified by high gamma-ray (GR) values, TOC content exceeding 2.15%, and clay mineral content exceeding 55%. This model offers a novel approach for predicting sweet spots and efficiently developing shale oil from the Qingshankou Formation in the Gulong Sag.

In order to make sure the sedimentary characteristic and evaluation of Dongying Formation, we quantitatively divide the sequence framework and distribution of sedimentary facies, comprehensive application the wave coefficient curve and maximum entropy spectrum analysis, on the base of sequence boundaries recognize. The result is that, there are 2 long-term and 3 mid-term cycle boundaries in Dongying Formation. We divide the Dongying Formation into 4 mid-term cycles, like MSC1, MSC2, MSC3 and MSC4. The fan delta plains and submarine fans are concentrated on the Xinanzhuang Fault, Baigezhuang Fault and Gaoliu Fault, they change to the fan delta front and shallow lake gradually, from the boundary fault to the central of Nanpu Sag. The evaluation of sedimentary in Dongying Formation is controlled by the union of fault, provenance and base level cycle. The base level is rising quickly in MSC1, the sediment are mainly conglomerate, medium sandstone, fine sandstone in fan delta plains and fan delta front. The base level rising slowly, and attain the maximum in MSC2, the sediment are mainly mudstone in shallow lake and semi-deep lake, it is the main source rocks in study area. The subsidence rate decrease in MSC3 and MSC4, and the base level began to decline, the sediment are mainly siltstone, fine sandstone in fan delta front, they are the main reservoirs and productive series in the study area.

As a key parameter characterizing the physical properties of reservoir fluids, crude oil density plays an important indicative role in the comprehensive evaluation of oil and gas reservoirs. Traditional laboratory analysis methods suffer from time lag, while existing logging-while-drilling identification technologies excessively rely on rock pyrolysis parameters, which are limited by insufficient parameters and inadequate information dimensions, thus failing to meet the needs of efficient exploration. To address these challenges, this paper takes 115 sets of typical thermal evaporation of gas chromatography samples from Bohai Oilfield as research objects (density range: 0.7542~1.0077 g/cm³), and innovatively constructs a quantitative characterization system for thermal evaporation hydrocarbon gas chromatography. Multidimensional characteristic parameters are extracted through digital processing of the spectra, and the internal coupling relationship between the characteristic parameters and their derived variables and crude oil density is deeply explored via machine learning. The sample dataset was divided into a training set (80%) and a test set (20%) using the stratified random sampling, and a high-precision crude oil density prediction model was finally established. The results show that the model exhibits excellent prediction performance both on the test set and practical application cases. The mean absolute error between the predicted and measured values is less than 0.02, indicating a high prediction accuracy and reliability. Compared with traditional methods, this technique does not depend on post-completion field sampling and testing, and can quickly realize the quantitative calculation of crude oil density based on cuttings samples during drilling. It provides key technical support for on-site decision-making in oil and gas exploration, with remarkable engineering application value and promation prospects.

Deep saline aquifers, characterized by wide distribution, large storage capacity, and low competition with oil and gas resources, are widely regarded as ideal media for geological CO₂ storage. This study elucidates the dissolution and diffusion behavior of CO₂ in real formation water under complex temperature-pressure conditions is of great significance for improving storage efficiency and ensuring long-term safety. Taking the Jurassic Sangonghe Formation in the Junggar Basin as the research object, a NaHCO₃ - type formation water model containing Na⁺, K⁺, Ca²⁺, Mg²⁺ and ions was constructed based on regional geological and hydrochemical data. Molecular dynamics simulations were then performed to systematically investigate the dissolution behavior, density distribution, intermolecular interactions, and diffusion mechanisms of CO₂ in formation water under varying temperature and pressure conditions. The simulation results indicate that under isothermal conditions, pressure is the dominant factor controlling CO₂ occurrence and dissolution. Low pressure favors rapid dissolution of CO₂, moderate pressure enables efficient utilization of storage capacity, and high pressure helps maintain long-term system stability. Under isobaric conditions, elevated temperature markedly enhances CO₂ solubility by weakening hydrogen bonding and intensifying molecular thermal motion, thereby promoting uniform dispersion of CO₂ molecules and the formation of stable solvation structures. Under coupled temperature-pressure conditions, the CO₂ adsorption amount increases significantly with burial depth in the low-pressure interval (approximately 1000~4000 m), indicating a strong promotion of dissolution by pressure increase; in the high-pressure interval (>4000 m), the adsorption continues to increase but with a decreasing rate, reflecting that temperature-induced desorption gradually offsets the promoting effect of pressure. In addition, the diffusion coefficient shows a stratified trend: elevated temperature in shallow zones promotes CO₂ molecular migration, whereas high pressure in deep zones significantly suppresses diffusion, with a relatively small overall variation. Comprehensive analysis suggests that the depth interval of 1000~4000 m is the most suitable range for CO₂ geological storage, as it ensures both high dissolution efficiency and long-term stability. This study elucidates the microscopic mechanisms of CO₂ dissolution and diffusion in formation water under complex temperature-pressure conditions, deepens the understanding of CO₂ fluid occurrence and migration in deep geological environments, and provides scientific guidance for evaluating storage potential, selecting suitable storage sites, and designing injection strategies, offering valuable references for CCUS engineering practice.

Formation pressure prediction is a critical task in drilling engineering, as its accuracy directly affects well control safety, drilling risk management, and the determination of appropriate drilling fluid density windows. Conventional formation pressure prediction methods mainly rely on a single data source, such as well logs or seismic data. In complex structural settings or abnormal overpressure zones, they often fail to accurately characterize the location and magnitude of pressure variations, leading to considerable prediction uncertainty. For example, in the Huangliu Formation of the Ledong area in the South China Sea, abnormal overpressure is developed, and the associated pressure variations exhibit strong correspondence with seismic impedance and lithological changes. Meanwhile, engineering responses, such as drilling fluid density adjustments, usually indicate abnormal overpressure at shallower depths than the physical variations inferred from seismic or logging data. This phenomenon indicates that engineering information and physical information are complementary in identifying formation pressure anomalies. This study proposes an intelligent formation pressure prediction method that integrates physical and engineering priors. Physical priors derived from seismic inversion and engineering well-control priors are jointly embedded into a deep learning model to achieve accurate pre-drilling pressure prediction for target wells. Application results from field data show that the proposed method improves formation pressure prediction accuracy while effectively capturing pressure variation characteristics. It also provides higher reliability in identifying high-risk intervals and determining pressure safety windows in key formations.

Traditional model-driven inversion methods rely on prior information and regularization, which often lead to oversimplification of geological features. With the rapid development of deep learning, diffusion models have emerged as a powerful alternative for solving inverse problems, as they can learn the complex data distribution characteristics and provide better prior information. Inspired by this, this paper introduces diffusion models to enhance the reliability and stability of inversion results. The method learns the data distribution through the processes of noise addition and denoising applied to a synthetic impedance model. Subsequently, by utilizing posterior sampling conditioned on seismic data, it incorporates low-frequency model constraints, 3D lateral constraints, and momentum estimation to improve lateral continuity and the stability of gradient updates, thereby achieving a robust mapping between seismic data and the impedance model. Application results on both synthetic and real data demonstrate that the new method can recover impedance models that are both detailed and geologically plausible. Compared to traditional model-driven methods, the proposed method improves the accuracy of single-trace inversion by 5%. The new inversion framework reduces reliance on prior information and significantly enhances generalizability and reliability, while also providing new approaches for solving other complex geophysical inverse problems.

In strongly diagenetically altered low- to medium-permeability sandstone reservoirs, disconnected pores are widely developed, making it difficult for conventional nuclear magnetic resonance (NMR)-based permeability evaluation methods to accurately reflect the true seepage capacity of the reservoir. These methods generally use an empirical T₂ cutoff to partition free fluid volume (FFV) and bound volume irreducible (BVI), and then estimate permeability using the classical Timur-Coates model. However, because they cannot effectively distinguish connected pores from disconnected pores, the predicted permeability is often systematically overestimated. To address this issue, this study proposes a pore-throat-connectivity-constrained method for determining the T₂ cutoff and incorporates it into the Timur-Coates permeability calculation workflow. First, core NMR T₂ spectra and high-pressure mercury intrusion capillary pressure data are jointly utilized and transformed into equivalent pore-size distributions and cumulative distribution curves. By quantitatively analyzing the correspondence between these two types of curves, the volume of disconnected pores and the associated critical pore-size range are identified, thereby determining a free-fluid T₂ cutoff with clear physical significance. Based on this cutoff, FFV and BVI are reclassified and then substituted into the Timur-Coates model to recalculate permeability. A case study from sandstone reservoirs in the Bozhong Sag, Bohai Bay Basin, demonstrates that the proposed method effectively reduces the interference of disconnected pores in FFV estimation and transforms the T₂ cutoff from an empirical selection into a quantitatively determined parameter constrained by pore-throat connectivity. Compared with the conventional empirical cutoff method, the proposed approach yields permeability predictions that are in much better agreement with measured core permeability, with the logarithmic root mean square error reduced from 1.079 to 0.104. These results indicate that the proposed method significantly improves the accuracy and stability of permeability evaluation in reservoirs affected by complex diagenesis, and provides a more geologically meaningful and practically valuable approach for the refined permeability characterization of low-permeability complex sandstone reservoirs.

Marlstone reservoirs stand as the pivotal and primary target formations throughout the entire process of shale oil exploration and development. Oil saturation functions as the fundamental core parameter utilized for characterizing the degree of hydrocarbon enrichment within reservoir rocks and assessing the inherent development potential of subsurface reservoir systems, and its corresponding prediction accuracy exerts a decisive influence on both the screening efficiency of favorable sweet-spot zones and the scientific rigor and rationality in the formulation of practical development strategies for shale oil reservoirs. The conventional Archie model was initially formulated and established on the basis of a set of idealized assumptions that are exclusively applicable to relatively homogeneous sandstone reservoirs. When this classical model is implemented and utilized in marlstone reservoirs characterized by highly complex sedimentary environments and significant reservoir heterogeneity, it is confronted with prominent defects such as insufficient accuracy in the prediction of oil saturation. To address the inherent applicability limitations of conventional methodologies, the present study has conducted systematic petrophysical investigations targeting the Es_3x marlstone reservoir intervals within the Shulu Sag, culminating in the development of an advanced Archie model modification methodology incorporating organic porosity corrections. Initially, compute total porosity utilizing an enhanced Herron petrophysical model incorporating joint corrections for pore fluids and organic constituents, subsequently transform mineral mass fractions derived from elemental spectroscopy logging into mineralogical volume fractions. Establish a volumetric quantification model for organic matter fraction through integration of total organic carbon concentration, kerogen-bitumen density characteristics, and formation bulk density measurements, subsequently formulating a multivariate linear regression relationship between transformed mineralogical volume constituents and dry matrix grain density parameters. Calibrate and correct density-derived porosity and neutron-derived porosity measurements, then optimize these values to obtain total porosity. Subsequently, computationally derive organic-hosted porosity through petrophysical integration of total organic carbon concentration and kerogen transformation ratio parameters, then arithmetically isolate inorganic porosity by subtracting this quantitatively determined organic-hosted porosity component from the pre-established total porosity value. Ultimately, apply the Archie formation resistivity relationship, calibrated specifically for organic porosity systems, to predict hydrocarbon saturation distributions throughout the study area’s reservoir interval. The results indicate that the organic porosity-corrected Archie model enhances the physical plausibility of the relationship between porosity and saturation, significantly improves prediction accuracy, and demonstrates strong adaptability to the complex geological settings and pronounced heterogeneity of the study area. This model provides technical support for the identification of “sweet spot” zones, productivity potential evaluation, and development plan optimization in marlstone reservoirs.

Fractured-vuggy carbonate reservoirs represent a major domain in global hydrocarbon exploration and development. However, their efficient development is constrained by strong heterogeneity, characterized by spatially discrete distribution of fractures and vugs, complex connectivity, and diverse filling types. This paper systematically reviews research advances in reservoir characteristics, experimental methods, remaining oil distribution, and water/gas injection mechanisms. Based on genesis and controlling factors, the reservoirs are classified into three types: (1) Epikarst: dominated by fractures and dissolution pores (depth < 70 m), with a filling ratio of up to 60%; (2) Subterranean river karst: characterized by large-scale dissolution caves (extending 0.8 ~ 3.5 km), which are further divided into four types based on cave height: hall caves, main channel caves, tributary caves, and terminal caves; (3) Fault-controlled karst: forming vertically connected “fracture-cavity complexes” along fault zones, with full filling ratios ranging from 20% to 38%. Physical experimental models have evolved from conceptual ones (e.g., spliced marble/acrylic plates) toward realistic structures: marble models reveal fundamental flow mechanisms; transparent acrylic models enable visualization of displacement processes; laser-etched models replicate complex fracture-vug networks; full-diameter leached core samples support high-temperature and high-pressure simulations; and 3D printing allows precise control of fracture-vug morphology. However, limitations remain in simulating diverse filling media. After water flooding, remaining oil occurs in various forms, including attic oil, blind-end oil, bottom-water coning oil, oil shielded by high-conductivity channels, and filling-dependent oil. Field analyses, numerical simulations, and physical experiments consistently indicate that attic oil accounts for the largest proportion, making it a key target for enhanced oil recovery. Development strategies have progressed from single-well huff-and-puff to multi-well water/gas flooding, and further to optimized synergistic gas-water injection. Specifically, single-well water flooding mainly reduces water-coning residual oil; gas huff-and-puff mobilizes attic oil via gravity segregation; bottom-water injection suppresses coning and expands sweep efficiency; nitrogen flooding effectively recovers attic oil but requires well pattern optimization to control gas channeling; cyclic water injection enhances water diffusion through pressure fluctuations; flow-reversal injection alters flow paths to enlarge sweep volume; and synergistic gas-water injection couples gravity segregation with mobility control to improve displacement efficiency. Based on these insights, this paper proposes an integrated framework for potential tapping, termed “reservoir-specific adaptation, remaining oil targeting, stage-wise optimization”. This framework entails precisely matching injection-production techniques to reservoir types, implementing targeted controls based on remaining oil patterns, and dynamically optimizing strategies according to development phases. Future efforts should address challenges in three-dimensional modeling of complex fracture-vug systems, suppression of gas channeling, and dynamic prediction of remaining oil. Integrating artificial intelligence and high-precision detection technologies will enable accurate characterization of fracture-vug architectures. Developing novel experimental materials and model fabrication techniques will enhance simulation authenticity. Constructing numerical models that account for multi-physics coupling and optimizing synergistic gas-water flooding strategies are crucial for advancing the development of fractured-vuggy reservoirs from an “experience-driven” to a “precision-controlled” paradigm.

The morphology of natural micro-fractures in shale reservoirs is a key factor controlling their fluid flow capacity and mechanical stability, while the microscopic distribution of minerals significantly influences the development characteristics of local micro-fractures. Accurately extracting the geometry of micro-fractures and establishing its relationship with mineral types and spatial distribution are essential for a deeper understanding of wellbore instability mechanisms in shale formations. However, due to the strong heterogeneity of the shale matrix, conventional threshold-based segmentation methods struggle to precisely distinguish micro-fractures from mineral boundaries, leading to considerable uncertainty in the extraction of fracture morphological parameters. To address this issue, this study proposes a TS-LSTM fracture extraction method based on scanning electron microscopy (SEM) images, which combines threshold segmentation with a long short-term memory neural network to achieve high-precision segmentation and completion of micro-fractures. Using the extracted fracture morphologies, the width and tortuosity of the fractures are quantitatively characterized. To quantify the mineral distribution around the fractures, different distances outward from the fracture boundaries are defined, and the area percentage of a specific mineral within each distance zone is designated as the threshold mineral percentage content. On this basis, correlation analysis is applied to investigate the statistical relationships between the local content of three major minerals-quartz, albite, and illite-and fracture width and tortuosity. The results show that the TS-LSTM fracture extraction method can effectively extract micro-fracture regions from complex shale SEM images, with strong completion capability particularly for discontinuous fractures. Using the threshold mineral percentage content at different distances, the mineral distribution around fractures can be quantitatively described. Illite content exhibits a negative correlation with fracture width and a strong positive correlation with tortuosity, indicating that fractures in illite-rich zones are narrower and more tortuous. Quartz content is positively correlated with fracture width and overall negatively correlated with tortuosity, which favors the formation of wider and straighter fractures. However, in local areas with dense quartz grains, fractures may propagate around the grains, leading to increased local tortuosity near quartz. Although albite content shows a certain positive correlation with fracture width, its relationship with tortuosity is more complex. In summary, the type and spatial distribution of minerals collectively shape the complex propagation paths of fractures. This study establishes, through an intelligent approach, the relationship between minerals and micro-fracture morphology, providing a new pathway for developing micro-scale models of wellbore stability in shale formations.

The rock-breaking efficiency and durability of PDC cutters are critical to improving the rate of penetration and drilling efficiency. Previous studies on PDC cutter rock breaking have mainly been conducted at a constant depth of cut, and the penetration-shearing hybrid rock-breaking behavior and associated shear fracture characteristics during cutter penetration into the formation remain insufficiently investigated. In this work, a penetration-shearing hybrid rock-breaking experiment was developed on a vertical turret lathe to investigate the effects of lithology, depth of cut, back rake angle, and cutter geometry on the rock-breaking performance of PDC cutters. Pearson correlation analysis was further introduced to quantitatively characterize the correlations between the influencing factors and rock-breaking efficiency, and shearing fracture tests were also conducted at back rake angles of 30° and 35°. During penetration-shearing hybrid rock-breaking, lithology was the primary factor controlling the rock-breaking efficiency of PDC cutters, with both the cutting forces and mechanical specific energy during granite cutting being significantly higher than those during sandstone cutting. Depth of cut was another key variable affecting rock-breaking efficiency. As the depth of cut increased, the mechanical specific energy decreased rapidly and gradually approached a stable value, whereas aggressiveness increased monotonically and remained independent of lithology. Shaped cutters required less energy for rock breaking, exhibited higher rock-breaking efficiency, and showed stronger resistance to shear fracture. However, the correlations of cutter geometry and back rake angle with rock-breaking efficiency were weaker than those of lithology and depth of cut. At back rake angles of 30° or higher, the rock-breaking mode gradually shifted from shearing to crushing. Meanwhile, cuttings became more difficult to remove, the cutting forces increased, and periodic dynamic impacts intensified, which readily induced shear-fracture failure of the PDC cutter. And rational selection of the back rake angle and proper control of the depth of cut were effective measures for preventing premature failure of PDC cutters. These findings provided theoretical guidance for the design optimization and field application of PDC cutters.

Reservoir fracturing stimulation is the key to the successful development of tight oil and gas fields. To effectively improve the performance of fracturing stimulation, it is urgent to clarify the propagation laws of hydraulic fractures under the tectonically heterogeneous in-situ stress field of different structural types. A series of large-scale north-dipping faults and fold structures have developed in the Bozhi-Dabei area of the Kuqa Depression under the north-to-south thrust nappe action. The complex structural characteristics have caused significant disturbance to the in-situ stress field, which further restricts the propagation and extension of the fracture network formed by fracturing stimulation. In this paper, the typical structural characteristics of the study area were extracted, and geological models of fault structure, fold structure, and fault-fold composite structure were established respectively. Heterogeneous rock mechanical parameters were assigned to the models, followed by stress loading, to clarify the distribution characteristics of the in-situ stress field corresponding to different structures. On this basis, the three-dimensional hydraulic fracturing process in the heterogeneous in-situ stress field was simulated, and the differences in fracturing effects among different structural types were analyzed. The results show that the in-situ stress near the fault zone decreases significantly, and the closer to the fault zone, the lower the in-situ stress. For the fold structure, the in-situ stress is reduced in the core and crest of the anticline due to the tensile stress derived from tectonic deformation, while it is increased in the anticline bottom affected by the compressive stress derived from tectonic deformation. The in-situ stress field characteristics of the fault-fold composite structure present a superposition of those of the individual fault and fold structures. Fracture propagation is dominated by the heterogeneous in-situ stress field. Overall, the lower the in-situ stress, the easier the fracture opening. Within the same duration, the fracture propagation length is the largest in the fault-fold composite structure model and the smallest in the fold structure model. With the in-situ stress release near the fault zone, the growth rate of fracture opening area in the fault model and fault-fold composite structure model accelerates as the fracture propagates closer to the fault over time. For the fold model, the in-situ stress along the Z-axis remains unchanged; the fracture opens rapidly near the fluid injection point with sufficient energy, while the opening rate slows down as the fracture propagates outward. Within the same fracturing time, compared with the single fault and fold structures, the fault-fold composite structure features a larger fracture opening area and lower fluid pressure, thus being the most favorable for fracture propagation. It is recommended that well placement in the study area should be prioritized in the fault-fold composite structural belt. The optimal well location is on the hanging wall of the fault close to the fault plane, and the preferred drilling depth is above the neutral surface of the fold.

To address the challenge of real-time weight transfer recognition in directional well sliding drilling, an intelligent multi-level identification method based on measurement-while-drilling (MWD) real-time data is proposed. A four-level weight transfer evaluation system based on weight-on-bit (WOB) transfer ratio is established. By analyzing the response characteristics of downhole WOB and vibration signals, a multi-dimensional feature space is constructed from statistical domain, frequency domain, and temporal evolution perspectives. A comprehensive importance evaluation strategy is adopted to select 10 core features. To meet the strict requirements of real-time performance and lightweight design for downhole closed-loop control, a shallow random forest recognition model is designed, utilizing class weight methods to handle sample imbalance and well-based data partitioning strategy to ensure model generalization capability. Based on measured data from 5 directional wells in a western oilfield, the model achieves 90.2% accuracy and 0.900 Macro-F1 score on the independent well test set, with a recall rate of 87.6% for complete weight transfer. The model is successfully deployed on an ARM Cortex-M4 processor with 52 KB storage space and 355 milliseconds inference time, meeting all downhole hardware constraints. The consistency between the model decision logic and weight transfer physical mechanism is verified through interpretability analysis. The research results can be directly applied to intelligent on-off control of downhole active control devices such as hydraulic oscillators, reducing response time from minute-level of traditional surface control to within 5 seconds, which has significant engineering value for improving drilling efficiency and reducing downhole risks.

In oil and gas development engineering, shale may serve either as a reservoir subjected to repeated hydraulic fracturing or as a caprock subjected to long-term gas injection and production in underground gas storage. In both cases, it is exposed to the coupled effects of cyclic loading and fluid invasion. Therefore, clarifying the deformation behavior, damage accumulation, and instability mechanisms of shale under such coupled conditions is of great significance for understanding its mechanical response and evaluating its engineering stability. In this study, Fuling shale was selected as the research object. Uniaxial monotonic loading tests and graded cyclic loading tests were conducted under three conditions, namely dry, oil-saturated, and water-saturated states. In addition, cyclic loading tests within a high-stress range were carried out under the water-saturated condition. Through these tests, the evolution laws of shale strength, deformation, residual strain, and energy dissipation under different fluid conditions were systematically analyzed, and the effects of fluid state on the cyclic mechanical behavior of shale were further compared. The results show that dry shale exhibits the highest overall strength, whereas water saturation significantly weakens both the strength and stiffness of shale and markedly increases the proportion of energy dissipation during deformation and failure. Oil saturation has only a limited influence on the elastic modulus, but it enhances the residual deformation and energy dissipation during cyclic loading. Under all three fluid conditions, cyclic instability occurs when the stress level is still lower than the peak stress under monotonic loading, indicating that shale may become unstable before reaching its monotonic peak strength under repeated loading. Meanwhile, the corresponding peak total strain at instability is close to that at monotonic failure. Before the onset of instability, the growth rate of residual strain shows a pronounced increasing trend, reflecting the accelerated accumulation of irreversible deformation. Under all three conditions, the evolution of cyclic residual strain exhibits clear stage characteristics: the deformation in the first cycle is relatively large, then decreases and tends to stabilize, and finally increases rapidly again as failure approaches, showing an obvious stage-dependent pattern. These findings indicate that, compared with traditional evaluation methods based only on peak stress or uniaxial compressive strength, the criteria based on total strain, the growth rate of residual strain, and the energy dissipation ratio are more suitable for evaluating wellbore stability during shale oil and gas development and during long-term gas injection and production in underground gas storage. Therefore, these parameters can provide more appropriate indicators for stability assessment under coupled cyclic loading and fluid invasion conditions.

The coexistence of influx and loss in different formations represents the most complex scenario in well control. Accurate prediction of the gas-liquid two-phase flow behavior and pressure distribution within the wellbore is essential for effectively dealing with such high-risk incidents. In this study, a one-dimensional coupled gas-liquid two-phase flow model of the wellbore and the formation is developed under conditions of simultaneous influx and loss. By adjusting the distributions of pore pressure and fracture pressure to alter the relative positions of the influx and loss zones, the upper-influx/lower-loss and lower-influx/upper-loss scenarios are simulated. Additionally, by specifying different fracture pressures for the loss zone, the varying resistance of the formation to drilling fluid loss is simulated. The model is solved using a finite-difference numerical method, yielding the transient distributions of gas holdup, pressure, and velocity along the well depth under different operating conditions. The simulation results indicate that the fracture pressure of the loss zone is a key factor governing the flow regimes within the wellbore. When the fracture pressure is sufficiently high, the influx does not evolve into more complex conditions. At lower fracture pressures, a transition from influx to loss occurs within the wellbore. If a weak section with extremely low fracture pressure exists, a subsurface blowout characterized by the coexistence of influx and loss may develop. Under the upper-influx/lower-loss scenario, if the loss zone at the bottomhole has a relatively high fracture pressure, the influx within the wellbore ceases and the system evolves into a no-influx/no-loss state, with the shut-in casing pressure continuing to increase. When the fracture pressure of the loss zone is lower, a transition from influx to loss occurs, and the shut-in casing pressure first increases and then stabilizes. When the fracture pressure is further reduced, a subsurface blowout characterized by simultaneous influx and loss develops. The shut-in casing pressure initially increases and then rises slightly, and the inflection-point pressure is lower than the stabilized value in the influx-to-loss transition scenario. Under the lower-influx/upper-loss scenario, as the fracture pressure of the loss zone decreases, the flow regimes within the wellbore successively evolve from no-influx/no-loss, to influx-to-loss transition, and finally to simultaneous influx and loss. The variation of shut-in casing pressure is closely related to the fracture pressure. A lower fracture pressure corresponds to a lower inflection-point value of the casing pressure. In the no-influx/no-loss and influx-to-loss transition cases, a finite-length contaminated drilling fluid section detaches from the bottom and migrates upward. In contrast, under simultaneous influx and loss, a continuous contaminated section forms above the influx zone, while a high gas-fraction section with nearly constant gas holdup exists below it.

To achieve integrated drilling acceleration in geological engineering, a drilling efficiency and parameter optimization system has been established. The system consists of three modules: data management and configuration, drilling efficiency management, and wellbore mechanical analysis with parameter optimization, enabling the visualized management of drilling operation efficiency. First, based on selected indicators from mud logging data, a “virtual best-performing well” model and an efficiency analysis chart were developed to optimize drilling efficiency and meet the engineering requirements of high penetration rates and low risks. Second, thorough investigation and modeling of formation fluid loss, wellbore structure, bottom-hole assembly, and drilling fluid properties in the target block were conducted to establish an optimal drilling model tailored to different formations. Meanwhile, a wellbore environment monitoring and evaluation model was built to provide early warning of operational risks. Application results demonstrate that the accuracy of drilling condition identification reaches 85%, reducing the drilling cycle by approximately 20%. This research contributes to cost reduction, efficiency improvement, and safe drilling operations, laying a foundation for the digital and intelligent development of the drilling industry.

This study addresses the fluid flash evaporation phase change in geothermal production wells. A forced circulation visual experimental platform was designed to investigate flow pattern evolution and differential pressure fluctuation characteristics during flash evaporation, and high-precision flow pattern recognition was achieved via signal decomposition and machine learning. Key steps include: constructing an experimental system with fluid dynamic control, temperature regulation, data acquisition, and a visual pipe section; recording flow patterns (bubble, slug, churn, annular flow) via high-speed photography and analyzing their triggering conditions/morphological features; collecting differential pressure signals (2~3 meters height) and identifying distinct amplitude-frequency-morphology characteristics among flow patterns; applying CEEMD to decompose signals and extract IMF energy spectra; and developing a PSO-LSSVM model using multi-parameters (inlet temperature, velocity, IMF spectra) for high-accuracy recognition. Results provide theoretical support for flash evaporation localization and severity assessment, aiding wellbore optimization and geothermal extraction efficiency improvement.

China possesses abundant deep coalbed methane (CBM) resources; however, such reservoirs are characterized by highly developed cleat-fracture systems, pronounced matrix heterogeneity, and highly complex pore architectures. These intrinsic geological characteristics tend to induce multiple engineering challenges during drilling operations, including excessive frictional resistance, wellbore instability, and severe reservoir damage. Collectively, these issues significantly hinder the safe, efficient, and large-scale development of deep CBM resources and pose substantial technical challenges for drilling fluid system design.To address these challenges, this study focuses on system construction and engineering adaptability, and develops a dual-hydrophobic film-forming organic brine-based drilling fluid through systematic optimization of key functional additives. The system is designed based on the synergistic regulation mechanisms of dual-hydrophobic lubrication and interfacial film formation, enabling coordinated control over interfacial interactions and fluid microstructure. A comprehensive series of laboratory experiments were conducted to evaluate the rheological properties, lubrication performance, inhibition capacity, as well as film-forming and plugging characteristics of the system. Furthermore, microscopic characterization techniques, including scanning electron microscopy and interfacial analysis, were employed to elucidate the underlying mechanisms. Particular emphasis was placed on revealing the synergistic effects and multi-scale interaction pathways governing friction reduction, wellbore stabilization, and reservoir protection, thereby establishing a mechanistic linkage between interfacial chemistry and macroscopic engineering performance.The results demonstrate that, compared with conventional drilling fluid systems, the developed system exhibits outstanding overall performance, with inhibition performance improved by 73.33%, lubrication efficiency enhanced by 79.62%, and reservoir protection capacity increased by 69.06%. Notably, the system maintains excellent rheological stability and structural integrity under conditions of high mineralization, high hardness, and complex ionic environments, indicating strong resistance to salt and calcium contamination as well as sustained functional reliability.Field application results from Well Jiatan XX demonstrate that the system successfully enabled safe and efficient drilling of a 1125 m horizontal section, with smooth drilling operations and intact wellbore conditions throughout the process. The open-hole section remained stable after 60 days of soaking, with no evident collapse or enlargement observed. Additionally, the system exhibited excellent operational compatibility and process stability in field applications, significantly reducing the risk of drilling complications and enhancing overall construction efficiency.Overall, the proposed system demonstrates strong engineering adaptability and long-term service capability under complex CBM reservoir conditions. By integrating interfacial regulation with structural optimization, the system achieves multi-functional synergistic enhancement. This work provides a novel technical pathway for optimizing drilling fluid systems in deep CBM wells and offers significant potential for reducing drilling costs and improving resource development efficiency in challenging geological settings.

Using quantum chemical Density Functional Theory (DFT) simulations, the adsorption mechanisms of three common molecular species in coal mines (H₂O, CO₂, and CH₄) onto four prevalent oxygen-containing functional groups on coal surfaces were investigated from a microscopic perspective. The electrostatic potentials of the adsorbate molecules and the functional groups were evaluated, along with the adsorption distances, adsorption energies, and Mulliken charge transfer before and after adsorption. The results indicate that the order of maximum positive electrostatic potential for the functional groups is:-COOH>-OH>-C=O>-OCH₃. The order of maximum negative electrostatic potential is:-OH>-OCH₃>-COOH>-C=O. For the adsorbate molecules, the order of both maximum positive and negative electrostatic potentials is H₂O>CO₂>CH₄. Adsorption energy calculations reveal that the adsorption strength of the three molecules follows the trend H₂O>CO₂>CH₄. Specifically, the adsorption strength of H₂O on the various functional groups follows the order -COOH>-OCH₃> -OH>-C=O, whereas the adsorption strengths of CO₂ and CH₄ follow the order -OCH₃>-COOH>-OH>-C=O. Mulliken charge analysis demonstrates that oxygen atoms in the functional groups readily accept electrons. A greater amount of electron transfer from the adsorbate correlates with a more stable adsorption configuration. The stability order of adsorption is confirmed as H₂O>CO₂>CH₄.

The failure of natural gas pipelines may lead to serious casualties and huge property losses, and it also poses a great threat to the surrounding ecological environment and social public safety. Risk analysis is an important technical means to predict potential safety hazards existing in pipelines. By carrying out systematic risk identification, quantitative evaluation and targeted preventive control, relevant departments can accurately grasp the risk level of pipeline operation and take corresponding measures to reduce the occurrence probability of pipeline failure effectively. However, in the actual engineering application of natural gas pipelines, the available historical accident data are often insufficient, and the relationship among various risk factors is complex and uncertain, which brings great difficulties to the accurate determination of pipeline failure probability and the rationality of risk assessment results. To solve this problem, this paper proposes a fuzzy Bayesian network risk analysis method for gas transmission pipelines based on the Ranking Nodes Method(RNM) and the Analytic Hierarchy Process (AHP). Firstly, Fuzzy Comprehensive Evaluation (FCE) is used to realize the quantification and systematic analysis of expert opinions, so as to reduce the subjectivity and fuzziness of expert judgment and calculate the prior probability of each key risk factor of pipelines. Secondly, the weight of each basic risk factor is calculated by using the Analytic Hierarchy Process, so as to reflect the relative importance of different risk factors in the process of pipeline failure. Thirdly, the Conditional Probability Table (CPT) between nodes is calculated according to the weight by using the Ranking Nodes Method, which provides a reliable data basis for the construction of Bayesian network model. Finally, the calculated prior probability and the conditional probability table between nodes are applied to the Bayesian Network (BN), so as to realize the accurate calculation of pipeline failure probability under the conditions of insufficient pipeline accident data and complex relationship of influencing factors. At the same time, through probability updating and reverse reasoning, the critical events that have a significant impact on pipeline failure can be efficiently identified. In order to verify the effectiveness of the method, it is applied to an actual gas transmission pipeline. According to the existing engineering database and on-site expert opinions, various potential risks in the operation process are identified comprehensively. These key risk factors are taken as basic nodes, and the logical relationship and influence mechanism between nodes are comprehensively analyzed to establish a complete Bayesian network model. Then, the prior probability and conditional probability are calculated by using the risk analysis method proposed in this study, and the calculation results are substituted into the Bayesian model to obtain the final pipeline failure probability and determine the critical events leading to pipeline failure. The calculated failure probability is close to the failure probability recorded in the existing database, which fully verifies the feasibility, rationality and accuracy of the method. The results show that this method can overcome the limitation of lack of accident data, realize the scientific and quantitative evaluation of pipeline operation risk, and provide important theoretical support and scientific guidance for pipeline technicians in daily safety management, risk early warning and maintenance decision-making.

Against the backdrop of the full advancement of China’s “Dual Carbon” strategy and the impending inclusion of the oil and gas industry in the national carbon emissions trading system (ETS), traditional optimization models for reservoir development, which mostly adopt a static carbon emission accounting mode for the net present value (NPV) maximization problem, cannot accurately capture the real-time impact of production strategy adjustments on carbon emissions, and thus struggle to address the dual challenges of economic benefits and emission reduction brought by carbon costs. This study develops a carbon-embedded NPV optimization model that establishes a coupling system between subsurface reservoir simulation and surface facility energy consumption simulation. An improved particle swarm optimization (PSO) algorithm with a constraint repair operator is adopted to solve the global optimal control strategy of the model with production well liquid rates as decision variables. Three carbon quota allocation mechanisms are designed for quantitative analysis: free allocation, mixed allocation, and paid auction. Numerical experiments demonstrate that, compared with the baseline scenario, the NPV improvements under the three mechanisms are 7.63%, 5.56%, and 3.86%, respectively, indicating that stricter carbon constraints lead to greater compression of profit margins. Furthermore, the paid auction mechanism exhibits the strongest water-cut control and the most significant emission reduction effects. The study verifies that the carbon-embedded NPV optimization model can achieve the synergistic optimization of production and emission reduction, and effectively transmit carbon price signals to development enterprises. Carbon quota mechanisms are ideal transitional policy tools for the low-carbon transformation of the oil and gas industry.

Against the backdrop of global climate governance and the transition toward low-carbon development, increasing attention has been paid to carbon emissions embodied in international trade in energy products. As a typical carbon-intensive commodity, oil products pose pressing challenges in terms of carbon accounting, driving mechanisms, and responsibility allocation along cross-regional trade flows. Based on input-output theory, this study quantitatively estimates the embodied carbon emissions associated with China’s oil product trade with its major trading partners and further examines their structural characteristics. Structural decomposition analysis (SDA) is employed to identify the key drivers behind changes in embodied carbon emissions and to explore feasible approaches for allocating carbon emission responsibilities across regions. The results indicate that, first, China’s oil product exports are accompanied by a considerable scale of embodied carbon flows, with carbon emissions exhibiting a structurally increasing trend. Second, changes in embodied carbon emissions are jointly driven by export scale effects and structural effects, while technological effects partially offset the upward pressure on carbon emissions. Finally, the contribution of structural effects to embodied carbon growth has intensified in recent years, underscoring the importance of optimizing export product structures for the low-carbon transition of oil product trade. Based on these findings, this study proposes targeted policy implications. At the national level, efforts should focus on optimizing trade structures and promoting technological upgrading to accelerate the transformation of the domestic energy mix. At the international level, it is necessary to advance equitable responsibility sharing and cooperation, while enhancing data sharing and transparency in carbon emissions, thereby promoting harmonization and mutual recognition of standards in carbon data collection, accounting methodologies, and indicator systems across countries.

Under the “Double Carbon” strategy, the high-energy-consuming and high-emission petroleum industry faces urgent demands and pressures for green transformation. However, traditional evaluation models focusing on financial performance are ill-suited for current high-quality development requirements, and existing general ESG evaluation systems lack targeted characterization of petroleum industry traits. To address this issue, based on a macro-micro dual perspective, this paper constructs a comprehensive evaluation index system covering economic, environmental, social, and governance dimensions, specifically incorporating industry-characteristic indicators such as carbon emission intensity and low-carbon technology investment. Using the AHP-Entropy Weight-TOPSIS combined evaluation model, the sustainable development capability of 32 A-share listed petroleum enterprises from 2018 to 2023 is evaluated. The results indicate that: Macroscopically, constrained by cyclical fluctuations in international oil prices and initial green transformation costs, the industry’s overall sustainable development performance declined in 2019 and subsequently exhibited a fluctuating adjustment trend. Microscopically, while the average comprehensive performance of state-owned enterprises (SOEs) is generally superior to that of private enterprises, the gap demonstrates a significant convergence trend, with the score difference narrowing from 0.113 in 2018 to 0.002 in 2023. Further analysis reveals that this disparity primarily stems from SOEs’ institutional advantages in environmental compliance and social responsibility, whereas private enterprises have achieved rapid catch-up by improving governance efficiency and increasing social responsibility investment. This study reveals the differentiated evolutionary paths of petroleum enterprises under different ownership structures in the “Double Carbon” context, providing a quantitative basis for the government to improve industry regulatory policies, and offering scientific decision-making references for SOEs to consolidate their green benchmark status and for private enterprises to optimize ESG governance strategies.