Search Results (1,313)

The Cambrian Qiongzhusi Formation shale in southern Sichuan is a promising new marine shale gas exploration target, often considered the next major potential source following the Silurian Longmaxi Formation. Clarifying its reservoir characteristics of shale is crucial for identifying shale gas sweet spots. As the most distinctive structure feature in shale, laminae development plays a vital role in the formation and evolution of shale reservoirs. Based on core samples, thin sections, and a variety of test data, this study investigates the laminae development characteristics and reservoir significance of the Qiongzhusi Formation shale in the southern Sichuan Basin, yielding the following conclusions: (1) A three-level classification and nomenclature system for shale laminae in the Qiongzhusi Formation is proposed based on mineral composition and stacking patterns, dividing laminae into single laminae, lamina sets, and lamina series. The study area exhibits diverse lamina types, including four types of single laminae, three types of lamina sets, and seven types of lamina series. (2) The vertical heterogeneity in lamina series is pronounced. Within the organic-rich interval, the lithology transitions upward from organic-rich massive shale, through organic-rich argillaceous–felsic laminae, to organic-lean argillaceous–felsic laminae. In the low-TOC interval, increasing water depth corresponds to a transition from massive sandstone to predominantly organic-lean argillaceous–felsic–calcareous laminae and organic-lean argillaceous–felsic laminae. (3) Lamina development exerts a significant control over reservoir properties, with marked differences observed between various lamina series and massive shale. Among them, the organic-rich argillaceous–felsic lamina series exhibits the most favorable reservoir characteristics, including the highest total organic carbon (TOC) content, porosity, and gas content, representing the optimal shale reservoir type. Full article

Accurate prediction of Rate of Penetration (ROP) in carbonate formations remains constrained by the arbitrary selection of geomechanical input parameters in empirical drilling models. This study presents the first systematic field-based evaluation of sixteen geomechanical properties—grouped into three categories: strength parameters (uniaxial compressive strength (UCS), confined compressive strength (CCS), shear strength, thick-walled cylinder strength (TWC), friction angle, and cohesion), elastic moduli (Young’s modulus, shear modulus, bulk modulus, bulk compressibility, dynamic combined modulus (DCM), Poisson’s ratio, brittleness index), and in situ stress parameters (overburden pressure, minimum, and maximum horizontal stresses)—to identify optimal predictors for ROP modeling across PDC bit sizes of 12.25″ and 8.5″. Continuous wireline log data from two vertical carbonate wells in the Middle East (Well A: 1000–3370 m; Well B: 1945 to 3128 m; total intervals of 2370 m and 1183 m, respectively) penetrating formations comprising limestone, dolomite, sandstone, shale, anhydrite, and marly limestone were used. All sixteen geomechanical properties were computed using Interactive Petrophysics (IP) software with lithology-specific empirical correlations and validated against laboratory core measurements (R² = 0.79–0.95). Pearson and Spearman correlation analyses quantified parameter–ROP relationships, and the Al-Abduljabbar empirical model, recalibrated via multiple nonlinear regression, served as the evaluation framework. DCM consistently exhibited the strongest negative correlation with ROP across both bit sizes and achieved the highest model accuracy (R² = 0.54, AAPE = 25.33%), significantly outperforming the Bourgoyne and Young model (R² = 0.26, AAPE = 36.55%). A statistically validated scale-dependent effect was identified: Fisher’s Z-transformation tests confirmed that the correlation reversal between CCS and UCS across bit sizes is statistically significant (CCS: Z = −16.84, p < 0.001; UCS: Z = −6.75, p < 0.001), establishing CCS as the superior predictor at 12.25″ and UCS as the superior predictor at 8.5″—a finding not previously reported in the ROP literature. This reversal is attributed to the larger contact area of the 12.25″ bit, which promotes confinement-dominated rock failure better described by CCS, whereas the smaller bit produces localized stress concentration better represented by UCS. These results establish that (1) optimal geomechanical input selection is bit-size dependent, (2) nonlinear modeling outperforms linear frameworks for strength–ROP relationships, and (3) parameter relevance outweighs coefficient tuning in model robustness. DCM is recommended as the most operationally practical universal input, requiring only conventional compressional sonic and density logs. This study provides a systematic framework for geomechanical parameter selection with direct implications for drilling optimization in heterogeneous carbonate reservoirs. Full article

To address the scientific challenges associated with complex microscopic pore structures and the unclear mechanisms of miscible gas injection in typical low-permeability carbonate reservoirs in the Middle East, online nuclear magnetic resonance (NMR) imaging experiments were conducted during water-alternating-CO₂ miscible flooding. The microscopic oil mobilization mechanisms were quantitatively investigated for different pore structure types and at various displacement stages. The results indicate that water-alternating-CO₂ miscible flooding achieves a relatively high degree of oil mobilization in large and medium pore–throat structures. This behavior is likely associated with Jamin-type flow resistance effects and flow regulation induced by gas–water alternating slugs. Differences in microscopic oil mobilization are mainly observed in mesopores (0.3–1.5 μm). The recovery degrees of mesopores in Cores 1, 2, and 3 reach 89%, 94.2%, and 78%, respectively, contributing 93.7%, 80.6%, and 50.9% to the total oil recovery. The degree of microscopic heterogeneity controls the distribution of remaining oil in core slices after breakthrough of the displacement front. In Core 1, the signal amplitude exhibits a gradual and uniform decline, indicating that gas–water alternating injection suppresses gas channeling and improves mobility control. In Core 2, the signal amplitude decreases more rapidly with increasing heterogeneity. In Core 3, the signal disparity continues to intensify, leading to the formation of dominant gas–water channeling pathways, while low-permeability pore–throat structures evolve into typical bypassed oil zones. As the CO₂–oil contact front progressively advances toward the outlet end, the swept volume gradually decreases due to the development of preferential flow channels. Consequently, significant remaining oil accumulation occurs near the outlet region. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

Seismic inversion is a key technique for quantitative characterization of subsurface elastic parameters and detailed reservoir description. However, due to the limited bandwidth of seismic signals and the strong heterogeneity of complex reservoirs, conventional inversion methods struggle to simultaneously achieve high vertical resolution and lateral continuity. To address these challenges, an intelligent elastic parameter inversion method based on kernel density estimation within a Bayesian framework is proposed. First, kernel density estimation is introduced to augment the training samples, thereby alleviating data scarcity. Second, a hybrid architecture integrating convolutional modules, Mamba, and cross-attention mechanisms is constructed to achieve collaborative modeling of local spatial features and long-range temporal dependencies. The cross-attention mechanism is further employed to adaptively weight and fuse multi-source features, thus enhancing the representation capability of the model. Subsequently, by designing a joint loss function, the strengths of deterministic inversion and data-driven approaches are effectively integrated, ensuring physical consistency while enhancing data adaptability, thereby improving the stability and accuracy of the inversion results. Furthermore, the neural network outputs are used as the initial model for Bayesian inversion to construct a probabilistic inversion framework for elastic parameter inversion. Finally, experimental results demonstrate that the proposed method improves the R² values of inversion results by more than 8.0% and 5.0% compared with conventional methods in thin interbedded models and real data experiments, respectively. Full article

Due to the strong heterogeneity of the reservoir, early gas breakthrough and low CO₂ displacement efficiency are common issues in the CO₂ flooding process of domestic gravel reservoirs. This study focuses on a gravel reservoir in Xinjiang, proposing a quantitative evaluation method that combines early gas breakthrough identification and the inversion of gas channel characteristic parameters. The aim is to provide theoretical support and technical guidance for gas breakthrough risk warning, injection-production system optimization, and control measures during the CO₂ flooding process. The research method includes the following several key steps: first, clarifying the criteria for determining the time of gas breakthrough and proposing a classification method for early gas breakthrough types based on CO₂ concentration levels; second, adopting a “matrix-dominant gas channel” dual-medium model, considering the geometric and physical characteristics of inter-well gas channels, and deriving a theoretical calculation formula with gas breakthrough time and CO₂ concentration in the produced gas as the target; third, using actual gas breakthrough time and CO₂ concentration as constraints, constructing a method to invert the characteristic parameters of gas channels, quantitatively representing key parameters such as gas channel thickness ratio, permeability variation, and equivalent permeability; finally, through the combined analysis of CO₂ concentration and gas channel characteristic parameters, establishing a method for identifying gas channel types suitable for domestic gravel reservoirs. The practical application results show that the test area has formed localized dominant gas channels, but the overall stage is still in the early phase of weak gas breakthrough. Most gas breakthrough phenomena are weak, with only a few well groups experiencing severe gas breakthrough issues. The gas channel thickness ratio is generally less than 0.05, and the permeability variation mainly ranges from 2 to 20. The gas channels are primarily of the fracture type, with some areas also containing ordinary fractures and main control fractures. The method proposed in this study, which combines early gas breakthrough identification with the inversion of gas channel characteristic parameters, not only provides a new approach to revealing the characteristics of gas breakthrough during CO₂ flooding but also offers solid theoretical and technical support for optimizing CO₂ flooding technology and controlling gas breakthrough risks. Full article

The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m³/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article

Rhyolites serve as unconventional hydrocarbon-water reservoirs and also as high-quality volcanic reservoirs. Well BZ8-3S-B exhibits maximum productivity in vertical wells. Drilling results reveal significant mega-scale heterogeneity among different wells within the same rhyolitic volcanic edifice, as well as between different intervals within single wells. To clarify the mega-scale heterogeneity characteristics of rhyolitic reservoirs, this study investigates Block BZ8-3S in the Bozhong Depression, Bohai Bay Basin, China. Based on sidewall cores, logging data and seismic datasets, comprehensive research methods including petrographic/mineralogical analysis, image processing, porosity–permeability testing, mercury capillary pressure measurements, logging facies interpretation and seismic facies analyses were employed. The study establishes correlations between volcanic edifice architecture, stratigraphic boundaries, depositional units and their relationships with reservoir space composition/permeability characteristics, aiming to identify principal controlling factors of mega-scale heterogeneity through systematic stratigraphic architecture analysis. The key findings are summarized as follows: (i) The volcanic edifices in Block BZ8-3S exhibit massive-pseudostratified structural characteristics. (ii) Wells A and B belong to the same volcanic edifice system but occupy distinct facies belts. Well A is located in the crater-near crater belt, while Well B lies in the proximal belt. (iii) Eruptive interval unconformity boundaries (EIUBs) are identified at 1 and 4 depths in Wells A and B, respectively. The EIUBs control the vertical heterogeneity of depositional-unit reservoirs. Reservoir porosity exhibits inverse correlation with burial depth below EIUBs, indicating stratigraphic boundary control on reservoir development. Mega-scale heterogeneity of rhyolitic reservoirs is primarily controlled by the number of stratigraphic boundaries and depositional unit types. From an exploration perspective, lava dome deposited units within crater-near crater belt should be avoided, while priority should be given to proximal belt targets featuring thick sequences with high proportions of lava flow units. This study provides critical insights for optimizing exploration strategies and enhancing development efficiency of rhyolite volcanic edifices. Full article

Purely data-driven machine-learning methods are currently limited by weak physical interpretability; meanwhile, the sparsity of well-site data in oil and gas fields further degrades the prediction performance of deep learning models for reservoir seepage simulation. To overcome this bottleneck, this study embeds Darcy’s law-based seepage equations as physical constraints into the loss function of a deep learning framework, thereby constructing a physics-informed neural network (PINN) for seepage flow in porous media of oil and gas reservoirs. Numerical simulations are performed in heterogeneous porous media to compare the predictive performance of the proposed PINN against conventional purely data-driven approaches, via evaluation metrics including the coefficient of determination (R²) and root mean square error (RMSE). The results show that both models achieve comparable predictive accuracy with sufficient training samples. In contrast, the PINN retains high predictive accuracy even with a reduced number of samples, and it delivers prominent superiority under conditions of sparse well data and strong reservoir heterogeneity. This study clarifies the applicable scenarios of the two aforementioned methods (physics-informed neural networks and purely data-driven machine-learning models) for fluid flow simulation in porous media and provides a solid theoretical and technical foundation for the accurate prediction of reservoir seepage fields and the optimization of oil and gas reservoir development. This work also offers a validated physics-constrained deep learning framework to guide the deployment of intelligent algorithms in practical subsurface flow engineering. Full article

Currently, pore fractal characteristics of deep marine–terrestrial transitional coal-measure mudstones in the central Sichuan Basin remain poorly understood. To clarify the pore fractal characteristics and their controlling factors, seven representative deep mudstone samples were collected from the Longtan Formation of Well NT1H in the Suining area, central Sichuan Basin. These samples were subjected to total organic carbon (TOC) content determination, vitrinite reflectance (Ro) measurement, X-ray diffraction (XRD) analysis of whole-rock and clay minerals, and low-pressure nitrogen adsorption (LPN₂A) experiments. Pore fractal dimensions were calculated based on the Frenkel–Halsey–Hill (FHH) theoretical model. The influences of mineral composition, organic geochemical characteristics, and pore structural parameters on pore fractal dimensions were analyzed. The results indicate that shale pores in the study area are predominantly developed as mesopores, exhibiting dual fractal characteristics; fractal dimension D1 (structural fractal dimension at high-pressure segment) ranges from 2.6662 to 2.7366, and fractal dimension D2 (surface fractal dimension at low-pressure segment) ranges from 2.5895 to 2.6363. Mineral composition exerts differential control over pore fractal dimensions. The effects of organic matter content and thermal evolution degree on fractal dimensions exhibit stage-dependent characteristics. Correlations between pore structural parameters and fractal dimensions indicate that small-aperture pores (micropores and mesopores) constitute the primary factor controlling pore heterogeneity in shales. These findings provide a theoretical basis for “reservoir evaluation” and “sweet spot” optimization of deep marine–terrestrial transitional coal-measure shales in central Sichuan Basin. Full article

Accurate prediction of reservoir production dynamics remains a key challenge in the oil and gas industry, especially for complex, high-dimensional time-series data. Conventional models fail to capture temporal dependencies, while existing hybrid models suffer from high parameter complexity and lack automated hyperparameter tuning, increasing training difficulty. To address these issues, this study proposes a novel hybrid model, TCN-LSTM-AVOA, combining a temporal convolutional network (TCN) with a long short-term memory network (LSTM) and incorporating the African Vulture Optimization Algorithm (AVOA) to enhance forecasting accuracy. The model not only captures complex temporal relationships and nonlinear features in reservoir data but also facilitates automated tuning of critical hyperparameters (e.g., the number of TCN kernels, LSTM units, batch size, and learning rate), which significantly enhances its robustness. Compared to eight benchmark models (back propagation neural network (BPNN), LSTM, convolutional neural network(CNN)-LSTM, TCN-LSTM, LSTM-AVOA, CNN-AVOA, TCN-AVOA), TCN-LSTM-AVOA achieves superior performance on a two-dimensional, three-phase heterogeneous reservoir, yielding a root mean square error (RMSE) of 7.0806, mean absolute error (MAE) of 3.4780, coefficient of determination (R²) of 0.9975, and mean absolute percentage error (MAPE) of 1.81%. This work demonstrates a more accurate and efficient methodology for reservoir production prediction, with significant potential for oilfield production optimization and resource management. Full article

Hydrogen (H) is the most abundant element in the solar system. In the Earth’s interior, it primarily exists in the form of hydrogen gas, water, atomic hydrogen, and hydroxyl groups. Hydrogen gas, as a clean energy source, is widely distributed within the Earth and is mainly generated through serpentinization, with minor contributions from water radiolysis, rock fracturing, biological activity, etc. Hydrogen sequestration occurs mainly through clay adsorption, entrapment under rock layers, dissolution in water, and fluid inclusions. Besides being present as pore water, hydrogen in the deep Earth predominantly resides in minerals as point defects related to hydrogen species (e.g., OH⁻, H⁺). During the Earth’s evolution, substantial hydrogen was stored in the deep Earth through accretion, and surface water has been transported into the Earth’s interior via subducting slabs; meanwhile, it can migrate upward through magmatic activity and mantle plumes. The inputs and outputs constitute the global hydrogen cycle. Hydrogen concentration and distribution are highly heterogeneous across the crust, mantle and core. The upper mantle is likely mostly dry, while the Earth’s core is potentially a large reservoir of hydrogen. Small amounts of hydrogen can profoundly influence the physicochemical properties of the Earth’s interior materials, as well as the dynamic processes within the Earth’s interior. Full article

Three locally calibrated Indices of Biotic Integrity (IBIs) based on macroinvertebrates (B-IBI), zooplankton (Z-IBI), and phytoplankton (P-IBI) were developed to characterize relative aquatic ecological condition at impaired sites in Dongjiang Lake, a deep reservoir-type lake in China, during 2021–2023. Using synchronous monitoring data, candidate metrics for the three biotic groups were screened and assembled by integrating taxonomic diversity, community composition, pollution-tolerance attributes, trophic indicators, and functional feeding groups. Metric values were standardized using a linear transformation, and site conditions were classified using a unified five-class grading scheme under the present local calibration framework. A total of 327 taxonomic units (species or morphospecies) were recorded across the three biotic groups, indicating relatively high biodiversity in the study area. Under the present locally calibrated framework, most impaired sites were classified within the moderate-to-good range, with clear interannual variation and spatial heterogeneity. Upstream and downstream sections showed greater fluctuations in IBI classes than the lake area. The macroinvertebrate-based IBI was more sensitive to long-term and cumulative habitat disturbance, whereas the zooplankton- and phytoplankton-based IBIs responded more rapidly to short-term variation in nutrients and water quality. Together, these results indicate that multitaxon IBIs can provide complementary information on relative ecological condition within Dongjiang Lake and may support ecological zoning, pollutant management, and restoration prioritization in similar deep reservoir-type lake systems. Full article

Deep coalbed methane reservoirs are characterized by complex geological conditions, strong heterogeneity, and significant variations in in situ stress, posing challenges for accurate three-dimensional in situ stress prediction. To address the issues of strong dependence on rock mechanical parameters in traditional physical models, as well as the lack of physical constraints and poor generalization capability under small-sample conditions in purely data-driven methods, this paper proposes a LightGBM prediction model that integrates physical information and data clustering. A total of 1289 fracturing clusters in the DJ block are selected as the research objects. First, the K-means algorithm is used to divide the reservoir into three categories to reduce the impact of heterogeneity. Then, a LightGBM model is constructed for each category, and physical constraints based on Huang’s model and stress–gravity equilibrium are incorporated into the loss function to ensure that the prediction results conform to mechanical laws. Taking the fracturing clusters in Category I as an example, the proposed model achieves an MAPE of 2.78% and an R² of 0.89 on the test set. Comparative experiments show that the proposed model outperforms BP neural networks, random forests, and Transformers in prediction accuracy. Ablation experiments verify the independent contributions and synergistic effects of the clustering module and the physical information constraints. Transfer experiments demonstrate that the model has good applicability to blocks with similar geological conditions. This study provides an effective method for predicting in situ stress in deep coalbed methane reservoirs, balancing accuracy and physical interpretability. Full article

Salmonella persistence contributes to infection relapse, chronic carriage, and reduced antibiotic efficacy. Traditionally viewed as dormant subpopulations that passively survive antibiotic exposure, persister cells are now increasingly recognized as dynamic, heterogeneous, and context-dependent physiological states shaped by bacterial regulatory programs and host microenvironmental pressures. This review examines Salmonella persistence from a multiscale perspective. We first clarify key antibiotic survival phenotypes, including resistance, heteroresistance, tolerance, persistence, and viable but non-culturable states. We then discuss how host-derived stressors, such as phagosomal acidification, nutritional restriction, metal perturbation, and reactive oxygen and nitrogen species, promote growth-restricted, persistence-associated bacterial states. At the bacterial level, we summarize stress-response networks involving the stringent response, SOS response, toxin–antitoxin systems, and auxiliary regulators that coordinate metabolic remodeling, growth restriction, and antibiotic survival. At the host level, we highlight how organ reservoirs, immune cell subsets, metabolic cues, and Salmonella-mediated immune niche remodeling shape persistence-associated phenotypes in vivo. Finally, we discuss clinical and translational implications, including endogenous relapse, resistance evolution, and emerging anti-persistence strategies. Together, this review provides a framework for understanding Salmonella persistence as a multiscale, niche-dependent process relevant to recurrent and chronic infection. Full article