Search Results (1,548)

Cemented natural fractures are widely developed in unconventional reservoirs and play a key role in controlling hydraulic fracture propagation and fracture network evolution. However, their mechanical effects are often oversimplified in conventional numerical models, limiting the reliability of fracture prediction. A numerical framework was established to investigate hydraulic fracture propagation in reservoirs containing cemented natural fractures. Cementation effects are quantitatively characterized using two parameters—cementation degree ($C_d$) and cementation strength ($C_s$)—representing the filling condition and interfacial resistance of natural fractures. Based on this formulation, both single-fracture and multiple-fracture models are constructed to analyze the influence of cementation properties and fracture density on fracture propagation. The results show that $C_d$ mainly controls fracture activation and propagation mode. Lower $C_d$ promotes fracture diversion and branching, whereas higher $C_d$ favors fracture penetration through the interface. $C_s$ governs interfacial resistance, with higher $C_s$ leading to more stable propagation. Increasing fracture density enhances fracture network complexity but also intensifies stress interference, affecting propagation stability and connectivity. These findings provide mechanistic insights into the role of cemented natural fractures in hydraulic fracturing and may support a more reliable interpretation of fracture propagation behavior in naturally fractured reservoirs. Full article

The ultra-deep carbonate reservoirs of the Fuman Oilfield in the Tarim Basin are characterized by intense fracture development. The coupled effects of high in-situ stress and fracture structures significantly deteriorate the mechanical properties of the rock mass, thereby constraining wellbore stability evaluation and safe drilling and completion operations. Existing studies have primarily focused on medium- to low-confining-pressure conditions and isolated fracture parameters, making it difficult to characterize the mechanical response of fractured rock masses under the high-stress conditions of ultra-deep reservoirs. To address this issue, limestone from the Yingshan Formation of the target reservoir was selected as the research object, and fractured specimens with varying fracture angles, widths, and densities were prepared. Uniaxial compression tests and triaxial compression tests under high confining pressures of 90 MPa and 120 MPa were conducted to systematically reveal the evolution of rock strength, deformation parameters, shear strength parameters, and failure modes under the coupled influence of fracture geometric parameters and confining pressure. On this basis, a confining-pressure–fracture coupled damage prediction model was established, and wellbore stability around the reservoir was analyzed using Finite Difference Method. The results indicate that fracture angle causes the peak strength and Young’s modulus to first decrease and then increase, with an inclination angle near 45° representing the most unfavorable fracture orientation. Increases in fracture width and density lead to continuous degradation of strength and stiffness. Although high confining pressure can close fractures and enhance load-bearing capacity, it cannot eliminate the controlling influence of fractures on failure pathways. Sensitivity analysis shows that the Young’s modulus and Poisson’s ratio are most sensitive to fracture width; cohesion is mainly governed by fracture width and density; and the internal friction angle is most sensitive to fracture density. Numerical simulations of wellbore stability further confirm that medium-inclination, large-aperture, and high-density fractures significantly increase the risk of wellbore instability. The findings provide experimental and theoretical support for mechanical-parameter correction, wellbore stability assessment, and construction-risk control in ultra-deep fractured carbonate reservoirs. Full article

Full-physics reservoir simulation for CO₂ storage becomes computationally expensive when many operational schedules must be screened, motivating machine-learning (ML) surrogates that reduce simulation burden while preserving the essential physics-driven response. We propose an activity-based partitioning methodology that produces an interpretable applicability map, identifying regions where surrogate substitution is expected to be reliable and regions where highly active dynamics make it unsafe. In this work, we focus exclusively on the slow-varying region and develop proxy models for pressure and saturation time derivatives in that domain. The fast-varying region is intentionally excluded, and no fully coupled hybrid simulator is claimed at this stage. The partition is constructed from temporal changes in derivative signals and aggregated across multiple schedules to obtain a conservative, scenario-robust delineation. For slow cells, local stencil-based neural proxies leverage overlapping time windows and features describing the local state, schedule forcing, and injector influence. Because saturation derivatives in the slow region are strongly zero-inflated, with many cells remaining outside the advancing CO₂ plume for long periods, a two-stage strategy is adopted: first detecting whether meaningful change occurs and then predicting the derivative magnitude only when active, with additional smoothing to suppress near-zero artifacts. The framework also supports selective surrogate deployment over user-selected time windows. The objective is therefore to establish a conservative zone of applicability for derivative-based ML updates, rather than to demonstrate full simulator replacement or end-to-end coupled acceleration. In the case study, 5914 of the 8243 grid blocks evaluated by the proxy workflow were classified as slow-varying, corresponding to 71.7% of the evaluated proxy-analysis domain. For the blind schedule, full-rollout pressure reconstruction produced mean absolute errors of 5.34, 3.69, and 2.80 psi over early, middle, and late time-window groups, respectively. In a future coupled implementation using the same partition, these 5914 cells could be advanced by the ML proxy, while the remaining dynamically active or unsupported cells would remain under full-physics treatment. This would reduce the full-physics active-cell count from 9212 to 3298 in the future coupled setting, although direct wall-clock acceleration remains to be quantified after simulator integration. Full article

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (R_a) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal R_a value is 6. The coefficient of determination (R²) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R² (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation. Full article

Reservoir construction and agricultural irrigation have substantially altered the natural hydrological regimes of many Mediterranean watersheds. This study aims to reconstruct the natural flow conditions of the Egirdir Lake Basin (Türkiye) and quantify the impacts of irrigation and reservoir operations on water inflows to Egirdir Lake using the Soil and Water Assessment Tool (SWAT). The SWAT model consisted of 14 subbasins and 274 hydrologic response units (HRUs) and initially calibrated and validated using naturalized flow data provided by the State Hydraulic Works (DSI) for the period from 1990 to 2014. The same model structure and parameters were then applied to simulate a regulated condition representing the combined effects of irrigation and reservoir operation. Results showed a considerable reduction in annual streamflows under the regulated condition. This study demonstrated the significant impact of irrigation water use and reservoir operation on the hydrological dynamics of semi-arid basins. Full article

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic mechanics theory. Statistical results demonstrate that the actual fracture initiation pressure of 60% of wells in the target block is significantly higher than the values predicted by traditional elastic theory, primarily attributed to plastic yielding and stress concentration effects around perforations induced by high in situ stress. An elastoplastic rock fracture initiation pressure model is established based on the Mohr–Coulomb criterion and the plastic zone radius criterion, which is applied to predict the fracture initiation pressure of selected wells in the target block. The relative error between the model predictions and field measurements is less than 2%, significantly improving the prediction accuracy of fracture initiation pressure in deep-to-ultra-deep formations. This provides precise guidance for subsequent optimization of operational parameters and selection of pressure ratings for wellhead equipment. The study further clarifies that in situ stress difference, rock yield stress, and the power-law hardening exponent are the key factors controlling the transition of fracture initiation modes. To mitigate the high pump pressure challenge in deep-to-ultra-deep reservoir fracturing, the field application of weighted fracturing fluid effectively increases the wellbore hydrostatic column pressure, reduces wellhead operational pressure, and ensures construction safety. The findings of this study provide critical theoretical and technical support for achieving the goal of “successful fracture initiation and effective fracture control” in deep-to-ultra-deep reservoir fracturing. Full article

With the large-scale development of renewable energy such as wind, solar and ocean energy, the demand for energy storage is more urgent. Pumped hydro energy storage (PHES) is one of the fundamental solutions to the problem of intermittent supply of renewable energy. The large-capacity/low-head pumped hydro energy storage (LL-PHES) system with the use of tubular pump turbine is a beneficial extension of traditional PHES systems owing to large flow rate and cheaper civil structures. However, the continuous competition between the “static water pressure difference caused by gravity” and the “pressure increase caused by accelerated impeller rotation” leads to prominent instability in the start-up process of the LL-PHES system under pump conditions. An explicit coupling algorithm is proposed for analyzing the transient characteristics in the start-up process of the LL-PHES system under pump conditions. This algorithm is based on the idea of dimensional transformation, and performs 3D flow calculations and 2D rigid body dynamics equation solution in the pump domain and the flap gate domain, respectively. This algorithm avoids the problems of high computational cost and poor convergence that exist in existing fully three-dimensional coupling algorithms and ensures the efficiency of transient hydraulic characteristic calculation. A comprehensive analysis of the transient characteristics of the LL-PHES system during pump start-up process is conducted using the proposed new algorithm. The entire process of the increase in rotational speed, valve opening, flow rate, and the continuous evolution of blade surface pressure during the start-up process is quantitatively described. The amplitude and spectral characteristics of the alternating pressure on multiple blades are clarified. The evolution law of blade load during the stage of severe pressure fluctuations during the start-up process is explained. The load distribution characteristics of “high in the leading and trailing edge areas and low in the middle” in the blade stream direction is presented. The research results have a direct guiding role in improving the hydraulic design and enhancing the operational stability of LL-PHES systems. Full article

To clarify how injection-induced cooling and reservoir properties jointly control rock damage during hydraulic fracturing of deep shale reservoirs, this study develops a coupled thermo–hydro–mechanical phase-field model incorporating fracture pressurization, matrix seepage, heat transfer, thermoelastic stress redistribution, and tensile damage evolution. The hydraulic fracture component is verified against the classical KGD analytical benchmark, and the thermal damage component is benchmarked against a ceramic quenching experiment. The phase-field formulation is constructed using tensile-compressive strain-energy decomposition so that only the tensile part of the elastic energy contributes to damage evolution, while the compressive stiffness is retained. The results show that low-temperature fluid injections produce a steep but spatially limited cooling zone near the fracture wall. The constrained contraction of the cooled rock generates additional thermoelastic tensile stress, strengthens fracture-tip stress localization, and accelerates phase-field damage accumulation. In the baseline case, thermal cooling increases the peak tensile stress near the fracture tip along profile c from 10.2 MPa in the hydraulic-only case to 22.5 MPa at t = 2 h, while the phase-field damage value increases from 0.03 to 0.77. Five-case sensitivity analyses show that, as α_T increases from 0.5 × 10⁻⁵ to 1.5 × 10⁻⁵ 1/°C, the fracture-tip tensile stress at t = 2 h increases from approximately 18.6 MPa to 25.7 MPa, and the damage value increases from approximately 0.80 to 0.96. As permeability increases from 0.0001 mD to 0.01 mD, the pore pressure at 2 m from the fracture wall increases from approximately 50.4 MPa to 71.2 MPa, and the tensile stress along profile c increases from approximately 16.4 MPa to 21.8 MPa. These results demonstrate that coupled thermal and hydraulic effects govern fracture initiation, localization, and propagation tendency during thermally assisted hydraulic fracturing in deep shale reservoirs. Full article

Surface seismic data often suffer from limited bandwidth and uneven illumination, which degrade PSDM (prestack depth migration) in deep and structurally complex settings. VSP (vertical seismic profiling), particularly DAS-VSP, provides a higher signal-to-noise ratio and richer high-frequency content near the wellbore but has a limited lateral imaging aperture. To exploit the complementary strengths of these two observation systems, we propose an OVT domain (offset vector tile) joint Kirchhoff prestack depth migration workflow that integrates surface seismic and VSP data within a unified depth domain framework. The workflow includes wavelet (amplitude–phase) matching, consistent datuming, joint well–surface tomographic velocity model building using both surface CIG (common image gather) residual moveout and VSP first-arrival constraints, efficient travel time table construction based on 3D eikonal solvers, OVT domain joint migration, azimuth-dependent CIG depth correction for anisotropy, and ray-based illumination compensation for amplitude balancing. Synthetic tests demonstrate that the proposed method improves reflector continuity and increases the effective bandwidth of the joint image relative to surface-only PSDM. A field application in the northwest Sichuan Basin further shows that the joint imaging better matches well synthetics in the target interval, increasing the correlation coefficient from 0.753 (surface-only) and 0.738 (VSP-only) to 0.787 (joint) while reducing inter-azimuth CIG depth residuals to within 3 m after anisotropy correction. These results indicate that OVT domain joint imaging can enhance thin-bed resolution and near-well structural delineation, providing a practical multi-source data fusion solution for high-fidelity depth imaging in complex reservoirs. Full article

To investigate the three-dimensional seepage response and safety implications of high concrete-face rockfill dams (CFRDs) under waterstop failure scenarios, this study establishes a refined three-dimensional finite element model for a high CFRD at the JD Hydropower Station using COMSOL (version 6.1) Multiphysics. A comparative analysis is conducted for six representative scenarios, including peripheral joint failure, single vertical joint failure, overall vertical joint failure, and combined failures. The seepage safety assessment is based on the phreatic surface, seepage discharge, hydraulic gradients in key zones, and left- and right-bank abutment bypass seepage. The results show that waterstop failure significantly changes the seepage field, phreatic surface, leakage discharge, and hydraulic gradients. Among the six scenarios, S5, representing overall vertical joint failure with an aperture of 0.5 mm for each of the 41 vertical joints, produces the most unfavorable leakage response, with the total seepage discharge reaching 3010.46 L/s and the water level behind the face slab reaching 3888.23 m. In contrast, peripheral joint failure mainly induces local hydraulic-gradient concentration in the special cushion zone. Under S1, the maximum hydraulic gradient in the special cushion zone reaches 2.72, exceeding the allowable value of 0.72. The results also reveal asymmetric bypass seepage around the dam abutments, with the right-bank foundation leakage being 90.4–137.7% higher than that on the left bank. These findings clarify the distinct seepage risk mechanisms of different waterstop failures and provide support for waterstop design, construction quality control, targeted monitoring, and operation-stage safety assessment of high CFRDs. Full article

Suspended particulate matter (SPM) is a key environmental driver in aquatic ecosystems and plays a significant role in shaping microbial communities, particularly in sediment-rich rivers. Dam construction alters hydrological regimes and creates distinct SPM gradients; however, the response mechanisms of microeukaryotic plankton communities remain poorly understood. In this study, we used 18S rRNA gene high-throughput sequencing to characterize microeukaryotic plankton communities across riverine, lacustrine, and transitional zones of the Xiaolangdi Reservoir on the Yellow River (China). Our results revealed distinct community compositions in the lacustrine zone, with SPM identified as the primary factor driving community differentiation. Alpha diversity was highest in the riverine zone, while beta diversity differences among zones were dominated by species turnover. Dominant taxa included Cryptophyta (44.71% ± 30.79%), Metazoa (18.98% ± 17.71%), Perkinsea (7.97% ± 9.78%), Chlorophyta (7.06% ± 5.80%), and Dinophyta (6.06% ± 6.73%). Metazoa, Dinophyta, and Phaeophyta were enriched in high-SPM riverine waters, whereas Alveolata dominated low-SPM lacustrine zones. Community assembly was primarily deterministic, governed mainly by homogeneous selection, with stochastic processes exerting stronger influence in riverine zones. Network analysis indicated that riverine zones exhibited more complex and stable networks, lacustrine zones showed higher local but lower global connectivity, and transitional zones displayed stronger interactions but lower stability. These findings advance our understanding of microeukaryotic plankton responses to dam-induced environmental changes and provide a basis for assessing biodiversity impacts in regulated river systems. Full article

This study proposes an integrated optimization and control-based approach for reservoir development analysis in the Kurovdagh oil field of the Lower Kura Depression. The methodology combines reservoir parameter evaluation with Shewhart statistical control charts to identify deviations in production performance, analyze water breakthrough processes, and support production optimization in mature reservoirs. Based on geological and production data, control charts were constructed to analyze oil production, water cut, injected water volumes, and well performance across multiple reservoir horizons, including Aghjagil, PS03, and PS06. This study further integrates production analysis with horizon-specific enhanced oil recovery (EOR) recommendations. Polymer flooding is proposed for horizon III to improve sweep efficiency, micellar waterflooding for the Aghjagil horizon, with an estimated recovery increase of 10–20%, and in situ combustion for horizon VI, with potential recovery improvements of up to 20%. Additional analysis of production fluctuations, water breakthrough processes, and reservoir heterogeneity was incorporated to improve the interpretation of abnormal production behavior. The results demonstrate that the proposed approach enhances hydrocarbon recovery efficiency, improves understanding of mature reservoir behavior, and supports data-driven optimization of production systems. The developed framework provides practical implications for long-term field management, reservoir monitoring, and production forecasting in mature oil fields. Full article

The Ordos Basin contains abundant deep coalbed methane (CBM) resources, whose efficient development largely depends on the effective implementation of large-scale volumetric fracturing technologies. To comprehensively evaluate the fracability of deep coal reservoirs in this basin, this study focuses on the No. 8 coal seam of the Benxi Formation. Based on rock mechanical experiments and well-logging data, multivariate linear regression models were established to predict Young’s modulus (E) and Poisson’s ratio (μ). The Huang model was applied to determine the three principal in situ stresses of the coal seam. Furthermore, a comprehensive fracability evaluation model was constructed by integrating three key indicators, namely brittleness index (BI), horizontal stress difference (Δσ_h), and tensile strength (S_t). The entropy evaluation method was used to determine the weights of these indicators, and the fracability index (F) of deep coal reservoirs was calculated. The results show that the weights of the factors controlling fracability decrease in the following order: tensile strength (0.434), brittleness index (0.332), and horizontal stress difference (0.234). The No. 8 coal seam in the northern and southern parts of the basin, including the Daning–Jixian, Shenfu, Jiaxian, northern Yulin, and southern Yanchuan areas, exhibits relatively favorable fracability, whereas northern Liulin and southern Yulin show comparatively poor fracability. In addition, the fracability index shows a clear positive correlation with the peak gas production of vertical CBM wells. Based on this relationship, the deep coal reservoirs were classified into three categories: Class I reservoirs (F > 0.55), characterized by high fracability and high production potential; Class II reservoirs (0.50 ≤ F ≤ 0.55), characterized by moderate fracability and moderate production potential; and Class III reservoirs (F < 0.50), characterized by low fracability and low production potential. These findings provide a scientific basis for identifying fracturing sweet spots and for the classification evaluation of deep CBM resources in the Ordos Basin. Full article

This study proposes a hybrid machine learning model based on full-spectrum pulsed neutron logging data to address the monitoring challenges of Carbon Capture, Utilization, and Storage (CCUS) under complex geological conditions. Traditional interpretation models for sequestered CO₂ saturation (e.g., macroscopic capture cross-section model, characteristic peak count model, and ratio model) heavily rely on prior parameters such as porosity, formation water salinity, and lithology. Acquiring these parameters in real time during practical engineering is often costly and difficult. To reduce the rigid dependence of accurate CO₂ saturation monitoring on complex prior parameters like porosity and salinity under heterogeneous geological settings, this research focuses on the Pearl River Mouth Basin, a core carbon sequestration target area in the Guangdong-Hong Kong-Macao Greater Bay Area, based on the evaluation results of offshore carbon sequestration macro-regions in China. Taking the primary reservoirs of the Enping and Wenchang Formations as typical geological prototypes, a high-fidelity, full-spectrum neutron–gamma response database was constructed using Monte Carlo simulations. Two machine learning strategies are proposed: a direct regression model (NMF+SVR) and a joint model (NMF+SVC/KMeans+SVR). Based on Monte Carlo simulated data, experimental results demonstrate that, compared with traditional petrophysical baseline models and simple machine learning models, the proposed joint learning method effectively reduces the dependence of CO₂ saturation monitoring on lithology and porosity. Furthermore, it is proven that even with a single-detector tool configuration, the method exhibits high prediction accuracy under complex lithological conditions. Notably, the two-step joint model achieves a Root Mean Square Error (RMSE) as low as 4.200%, significantly outperforming traditional physics-based models and single machine learning models such as MLP and RF. This study provides a physically interpretable and accurate technical reference for applying machine learning to pulsed neutron-logging-based CO₂ geological sequestration monitoring. Full article

Hydraulic fracturing in naturally fractured shale reservoirs commonly generates complex mesh-like fracture networks governed by hydraulic fracture–natural fracture interactions, which strongly affect stimulated volume, fracture connectivity, and early-time production. Existing simulation and monitoring-based methods often cannot simultaneously capture interaction mechanisms, rapidly generate field-scale fracture networks, and validate production responses. This study proposes a mechanics-constrained recursive propagation framework. A field-constrained stochastic natural-fracture model is first constructed, an explicit hydraulic fracture–natural fracture interaction criterion is incorporated to identify penetration, opening, and shear slipping, and a fully vectorized bidirectional recursive algorithm is developed to efficiently generate complex fracture networks. The method is applied to a 40-stage fractured horizontal well in the Changqing Oilfield, where the target interval has a porosity of 6.1%, a permeability of 0.1 mD, and a horizontal stress contrast of 7.0 MPa. The simulated network reproduces crossing, arrest, unilateral diversion, and bilateral diversion, and agrees well with microseismic observations. EDFM-based fully implicit flow simulation further shows early-time production deviations of 2–10%. These results demonstrate that the proposed framework can efficiently generate physically plausible field-scale fracture networks for fracturing design, post-fracturing evaluation, and short-term production forecasting. Full article