Search Results (2,667)

This study evaluates seepage behavior and hydraulic gradient conditions at the Lam Phra Phloeng Earthfill Dam in Nakhon Ratchasima, Thailand, by integrating long-term instrumentation records, updated geotechnical data, and deterministic numerical modeling. Piezometer and observation-well data collected between 2007 and 2023 were screened for reliability, revealing that several sensors exhibited abnormal or non-responsive behavior, limiting direct interpretation of phreatic surface variations in critical zones. Reliable datasets were incorporated into SEEP/W seepage simulations using representative dam cross-sections and soil parameters derived from recent drilling and laboratory testing. The results indicate that under normal reservoir operation, the phreatic surface remains within the core–drainage system and hydraulic gradients are well below estimated critical thresholds for the clayey foundation. Elevated reservoir levels lead to increased pore-water pressures and higher hydraulic gradients, particularly near the downstream zones and the deep central section of the dam. Rapid drawdown produces the most unfavorable hydraulic condition, generating steep transient pore-pressure gradients that approach critical values and reduce hydraulic safety margins. Although no immediate evidence of piping or uncontrolled seepage was identified, malfunctioning instrumentation creates monitoring blind spots that increase uncertainty in real-time seepage assessment. This study demonstrates that hydraulic gradient-based interpretation of deterministic seepage modeling provides a practical screening tool for dam safety evaluation under data-limited conditions. The findings emphasize the importance of enhanced monitoring redundancy and conservative operational control to support risk-informed management of aging earthfill dams under increasing hydrological variability. Full article

Fracture–vuggy carbonate reservoirs exhibit strong heterogeneity, spatial discontinuity, and highly variable porosity, which limit the effectiveness of traditional seismic attributes and conventional inversion. Focusing on the XX well block in the Tarim Basin, this study develops a rock-physics-constrained Physics-Constrained TransUNet method for intelligent porosity prediction. A carbonate-specific rock-physics model is first established, considering mineral composition, pore type, and water saturation, ensuring physical consistency between porosity, elastic parameters, and seismic responses. On this basis, a deep-learning framework integrating U-Net multi-scale feature extraction and Transformer global modeling is constructed. By embedding rock-physics priors, regularization constraints, and log-derived porosity labels, the method forms a unified physics- and data-driven inversion scheme. Applications to multiple deep wells and 3D post-stack seismic data from the FI7 fault zone demonstrate stable training, rapid convergence, and strong capability in capturing nonlinear porosity–seismic relationships. Compared with conventional inversion, the proposed approach significantly improves prediction accuracy in cavern-dominated intervals, fractured zones, and areas with abrupt porosity changes, while maintaining robust lateral continuity. Inter-well sections and target-layer slices further verify its effectiveness in identifying fracture–dissolution–vug composite reservoirs. The method provides a practical and reliable workflow for porosity prediction in ultra-deep carbonate reservoirs, supporting fine reservoir characterization and sweet-spot evaluation. Full article

Serbia’s energy sector is undergoing structural transformation driven by European climate policies, market volatility, and the need for long-term energy security. In this context, geological storage of energy carriers represents a strategically important option. Depleted gas reservoirs, particularly within the Pannonian Basin, constitute a technically validated subsurface infrastructure suitable for repurposing as multifunctional storage systems for natural gas, CO₂, and green hydrogen. This study analyzes trends in European and Serbian natural gas markets, EU decarbonization targets, and Serbia’s energy balance to assess the feasibility of carbon capture and storage (CCS) and underground hydrogen storage. Key geological parameters governing long-term containment—lithology, effective porosity, permeability, caprock integrity, and structural stability—are evaluated, with emphasis on reservoirs combining favorable properties and proximity to existing infrastructure. Quantitative screening based on reservoir depth (approximately 1000–2500 m), effective porosity (15–25%), permeability (typically >50 mD), verified caprock integrity, and estimated geological storage capacities ranging from 0.17 to 1.25 billion m³ demonstrates that several depleted gas reservoirs in Serbia meet explicit fit-for-purpose criteria for underground storage applications. A comparative analysis of the physical and molecular behavior of H₂, CH₄, and CO₂ in porous media indicates that hydrogen storage is the most sensitive to reservoir integrity. The reported results provide quantitative and qualitative evidence that selected depleted gas reservoirs in Serbia satisfy essential requirements for project-level screening, including reservoir capacity, petrophysical suitability, caprock integrity, and infrastructure accessibility. These findings support the technical readiness of such reservoirs for staged deployment of natural gas storage, CO₂ sequestration, and underground hydrogen storage in the post-2035 energy system. Overall, combined subsurface storage of natural gas, CO₂, and hydrogen in Serbia is technically feasible, economically justified, and strategically relevant within the national energy transition framework. Full article

Unconventional low-permeability reservoirs present significant production challenges due to the poor imbibition and displacement efficiency of conventional polymer fracturing fluids. The injection of nanoparticle (NP) compounds into polymer fracturing fluid base systems, such as linear gels or slickwater, has garnered significant research interest due to their superior performance. However, previous studies have primarily focused on evaluating the fluid’s properties, while its imbibition and oil displacement mechanisms within reservoirs remain unclear. Herein, the imbibition mechanism of nanoparticle composite polymer fracturing fluid was systematically investigated from macro and micro perspectives using low-field nuclear magnetic resonance (LF-NMR), atomic force microscopy (AFM), interfacial rheology, and other technical means. The results showed that the imbibition recovery using polymer fracturing fluid was 10.91% higher than that achieved with conventional slickwater. Small and medium pores were identified as the primary contributors to oil drainage. Nanoparticles can be adsorbed on the rock wall in the deep reservoir to realize wettability reversal from oil-wet to water-wet, reducing crude oil adhesion. Furthermore, a strong interaction between the adsorbed NPs and cleanup agents at the oil–water interface was observed, which reduces interfacial tension to 0.95 mN·m⁻¹, mitigates the Jamin effect, and enhances interfacial film deformability. NPs increase the interfacial dilatational modulus from 6.0 to 14.4 mN·m⁻¹, accelerating fluid exchange and oil stripping. This work provides a consolidated mechanistic framework linking NP-induced interfacial modifications to enhanced pore-scale drainage, offering a scientific basis for designing next-generation fracturing fluids. We conclude that NP-compound systems hold strong potential for low-permeability reservoir development, and future efforts must focus on optimizing NP parameters for specific reservoir conditions and overcoming scalability challenges for field deployment. Full article

This article investigates the estimation of water quality parameters, specifically chlorophyll-a, applying machine learning techniques to Sentinel-2 images. This study focuses on five small reservoirs located in the Extremadura region (Spain), as these are the ones for which continuous daily records from automatic in situ sensors are available. Chlorophyll-a estimates are obtained from two sources: (1) From the C2RCC atmospheric correction of Sentinel-2 images using Sen2Cor and radiometric calibration to ensure temporal consistency, and (2) from in situ data obtained from the official website of the Guadiana Basin Automatic Network Information System. The machine learning (ML)-based methodology significantly improves the predicted results for inland water bodies, enabling enhanced continuous assessment of water quality in small reservoirs. Full article

This study introduces a mathematical framework for analyzing unsteady gas filtration in porous media with pressure-dependent porosity variations. The physical process is formulated as a nonlinear parabolic boundary value problem that captures the coupled interaction between pressure evolution and porosity changes during gas production. To solve the equation, a numerical strategy is developed by integrating the Alternating Direction Implicit (ADI) scheme with quasi-linearization iterations, employing finite difference discretization on a two-dimensional spatial grid. Extensive computational experiments are performed to investigate the influence of key reservoir parameters—including porosity coefficient, permeability, gas viscosity, and well production rate—on the spatiotemporal behavior of pressure and porosity during long-term extraction. The results indicate significant porosity variations near the wellbore driven by local pressure depletion, reflecting strong sensitivity of the system to formation properties. The validated numerical model provides valuable quantitative insights for optimizing reservoir management and improving production forecasting in gas field development. Overall, the proposed methodology serves as a practical tool for oil and gas engineers to assess long-term reservoir performance under diverse operational conditions and to design efficient extraction strategies that incorporate pressure-dependent formation property changes. Full article

Monitoring water quality in large reservoirs is essential yet challenging, particularly in regions with limited in situ coverage. This study presents a robust methodology for integrating a decade-long in situ dataset (2014–2022) with Sentinel-2 multispectral imagery to develop and validate localized algorithms for water quality assessment in the Alqueva Reservoir, the largest artificial lake in Western Europe. Three atmospheric correction algorithms (C2RCC, C2X, C2X-COMPLEX) were evaluated, with C2RCC-COMPLEX identified as the most suitable for capturing the reservoir’s optical complexity, yielding the lowest RMSE for Total Suspended Solids (TSS: 2.4 g/m³) and Secchi Disk Depth (SDD: 0.85 m). Empirical models using Sentinel-2 bands 7 (783 nm), 6 (740 nm), and 8A (865 nm) demonstrated strong correlations (R² ≈ 0.69–0.71) for Chlorophyll-a (Chl-a) with a range data of 0.1–65 mg/m³, TSS with a range data of 2–13.1 g/m³, and SDD with a range data of 0.4–8 m. Spatially explicit water quality maps illustrate the models’ capacity to capture distinct gradients and seasonal dynamics, e.g., elevated Chl-a (>30 mg/m³) and TSS (>7.5 g/m³) in the reservoir’s nutrient-rich northern section during drought (August 2022), and more uniform conditions following winter recovery (March 2019), with SDD exceeding 2 m near the dam. These results underscore the utility of Sentinel-2 for resolving spatial and temporal variability in optically complex inland waters. The proposed workflow offers a transferable, cost-effective framework for monitoring eutrophication risks and sediment dynamics under increasing hydrological variability. Full article

Characterization of shale pore architecture forms the scientific basis for effective shale gas exploitation. Deep LMX FM shale from the Luzhou area was analyzed using SANS, LTPA, XRD, and SEM. This study quantitatively characterized the pore structure, focusing on closed-pore development and connectivity, and explored lithological controls. Pore-size distribution shows that micropores and small mesopores dominate the pore volume, with an average median pore diameter of 5.17 nm. Closed pores are abundant, indicated by a high average closed-pore ratio of 28.98%, reflecting generally poor connectivity. Pores smaller than 5 nm contribute 88.12% of the total SSA. Both pore volume and SSA correlate positively with TOC. In organic-rich and moderately organic-rich siliceous shales, these parameters also correlate positively with quartz content. In contrast, for organic-rich mixed shales, they correlate positively with clay mineral content. Among the lithofacies, organic-rich siliceous shale possesses relatively larger pore volume and SSA, along with better pore connectivity, making it the most favorable reservoir facies. Based on pore-structure characteristics and the regional structural setting, we recommend adopting close-spacing hydraulic fracturing with reduced cluster spacing in structurally stable areas to enhance stimulation. In structurally complex areas, engineering designs should prioritize risk mitigation to ensure operational success. Full article

Enhanced Geothermal Systems (EGS) extend geothermal energy beyond conventional hydrothermal resources but face challenges in creating sustainable heat exchangers in low-permeability formations. This review synthesizes achievements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a field laboratory advancing EGS readiness in 175–230 °C granitic basement. From 2017 to 2025, drilling, multi-stage hydraulic stimulation, and monitoring established feasibility and operating parameters for engineered reservoirs. Hydraulic connectivity was created between highly deviated wells with ~300 ft vertical separation via hydraulic and natural fracture networks, validated by sustained circulation tests achieving 10 bpm injection at 2–3 km depth. Advanced monitoring (DAS, DTS, and microseismic arrays) delivered fracture propagation diagnostics with ~1 m spatial resolution and temporal sampling up to 10 kHz. A data infrastructure of 300+ datasets (>133 TB) supports reproducible ML. Geomechanical analyses showed minimum horizontal stress gradients of 0.74–0.78 psi/ft and N–S to NNE–SSW fractures aligned with maximum horizontal stress. Near-wellbore tortuosity, driving treating pressures to 10,000 psi, underscores completion design optimization, improved proppant transport in high-temperature conditions, and coupled thermos-hydro-mechanical models for long-term prediction, supported by AI platforms including an offline Small Language Model trained on Utah FORGE datasets. Full article

CO₂ injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO₂ injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. The framework is applied to the Entrada Formation in the San Juan Basin, a laterally extensive sandstone unit with limited structural complexity across most of the basin, except for localized uplift in the Hogback region. A numerical model was calibrated to perform sensitivity analysis to identify the dominant parameters influencing injectivity. A dataset of these parameters generated through experimental design informs the development of several ML-based proxies and the best model is selected based on error metrics. These metrics include coefficient of determination (R²), mean absolute error (MAE), and mean squared error (MSE). The effective permeability-thickness product was obtained by the Peaceman’s well model, fractional flow slope, and Dykstra–Parsons coefficient were identified as the most influential parameters impacting the objective function. Train–test and blind test validation identified the Ridge model as the best, achieving an R² ≈ 0.994. The Ridge model which was used to map the Entrada Formation closely matches field-based correlations in the literature, confirming both its physical validity and the Entrada Formation’s strong injectivity potential, with slight deviations explained by the inclusion of additional parameters. This study reduces dependence on computationally intensive simulations while improving prediction accuracy. By benchmarking against established correlations, it enhances model reliability across diverse reservoir conditions. The proposed framework enables rapid, data-driven well placement and feasibility evaluations, streamlining decision-making for CO₂ storage projects. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

We develop a digitization-based analysis workflow for characterizing the entropy and correlation structure of truncated bosonic quantum fields after embedding them into small qubit registers, and illustrate it on the steady state of a coherently pumped micromaser. The cavity field is truncated to 32 Fock levels and embedded into a five-qubit register via a Gray-code mapping of photon number to computational basis states, with binary encoding used as a benchmark. On this register we compute reduced entropies, mutual informations, bipartite negativities and Coffman–Kundu–Wootters three-tangles for all qubit pairs and triplets, and use the resulting patterns to define information graphs. The micromaser Liouvillian naturally supports trapping manifolds in Fock space, whose structure depends on the choice of interaction angle and on thermal coupling to the reservoir. We show that these manifolds leave a clear imprint on the digitized information graph: multi-block trapping configurations induce sparse, banded patterns dominated by a few two-qubit links, while trapping on a single 32-dimensional manifold or coupling to a thermally populated cavity leads to more delocalized and collectively shared correlations. The entropy and mutual-information profiles of the register provide a complementary view on how energy and information are distributed across qubits in different parameter regimes. Although the full micromaser dynamics can in principle generate higher-order entanglement, we focus here on well-defined measures of two- and three-party correlations and treat the emerging information graph as a structural probe of digitized field states. We expect the workflow to transfer to other bosonic fields encoded in small qubit registers, and outline how the resulting information-graph view can serve as a practical diagnostic in studies of driven-dissipative correlation structure. Full article

Natural gas loss directly threatens the safety and economic viability of underground gas storage (UGS) facilities. The short-term, high-rate cyclic injection and withdrawal processes may cause fault reactivation, resulting in gas loss. Current assessment techniques are mostly concerned with overall storage performance, with few studies focusing on fault-related leakage. This study looks at a UGS facility developed from a difficult fault-block sandstone dry gas resource in China. Focusing on one of its border faults, we develop geological and numerical models to systematically examine the impacts of well-to-fault distance, gas injection rate, and gas withdrawal rate on fault leakage. The results show that numerical simulations can accurately estimate this gas loss. The well-to-fault distance, injection rate, and withdrawal rate are highlighted as critical regulating variables. There is an ideal range for the well-to-fault distance, and altering the injection/withdrawal rates of wells within this range is an effective loss mitigation approach. The crucial distance between injection–production wells and the fault in the X1 gas storage facility is 900 m. Notably, improving the gas withdrawal rate of wells close to the fault considerably minimizes leakage. Reducing the gas injection rate from 11 × 10⁴ m³/d to 7 × 10⁴ m³/d reduces natural gas loss by 353 × 10⁴ m³. Increasing the gas production rate from 9 × 10⁴ m³/d to 29 × 10⁴ m³/d reduces natural gas loss by 975 × 10⁴ m³. The findings provide a scientific basis for assessing and managing natural gas loss at boundary faults in similar UGS plants. Full article

Declining recovery factors from mature oil fields, coupled with the technical challenges of recovering residual oil under harsh reservoir conditions, necessitate the development of advanced enhanced oil recovery (EOR) techniques. While promising, chemical EOR often faces economic and technical hurdles in high-salinity, high-temperature environments where conventional polymers like hydrolyzed polyacrylamide (HPAM) degrade and fail. This study presents a comprehensive numerical investigation that addresses this critical industry challenge by applying a rigorously calibrated simulation framework to evaluate a novel hybrid EOR process that synergistically combines an ionic liquid (IL) with HPAM polymer. Utilizing core-flooding data from a prior study that employed the same Berea sandstone core plug and Saudi medium crude oil, supplemented by independently measured interfacial tension and contact angle data for the same chemical system, we built a core-scale model that was history-matched with RMSE < 2% OOIP. The calibrated polymer transport parameters—including a low adsorption capacity (~0.012 kg/kg-rock) and a high viscosity multiplier (4.5–5.0 at the injected concentration)—confirm favorable polymer propagation and effective in -situ mobility control. Using this validated model, we performed a systematic optimization of key process parameters, including IL slug size, HPAM concentration, salinity, temperature, and injection rate. Simulation results identify an optimal design: a 0.4 pore volume (PV) slug of IL (Ammoeng 102) reduces interfacial tension and shifts wettability toward water-wet, effectively mobilizing residual oil. This is followed by a tailored HPAM buffer in diluted formation brine (20% salinity, 500 ppm), which enhances recovery by up to 15% of the original oil in place (OOIP) over IL flooding alone by improving mobility control and enabling in-depth sweep. This excellent history match confirms the dual-displacement mechanism: microscopic oil mobilization by the IL, followed by macroscopic conformance improvement via HPAM-induced flow diversion. This integrated simulation-based approach not only validates the technical viability of the hybrid IL–HPAM flood but also delivers a predictive, field-scale-ready framework for heterogeneous reservoir systems. The work provides a robust strategy to unlock residual oil in such challenging reservoirs. Full article

During the energy storage fracturing process of tight sandstone reservoirs, the pre-injection of fracturing fluid is used to supplement the formation energy, and the physical properties of rocks change under hydration. To reveal the damage mechanism of hydration on tight sandstone, the tight sandstone surrounding the Daqing Changyuan in the northern part of the Songliao Basin was taken as the research object. Through indoor static hydration experiments, combined with scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), Nano-indentation experiments, and other methods, the evolution laws of rock micro-pore morphology, microfracture parameters, Young’s modulus, hardness, and other mechanical indicators under different hydration durations and soaking pressures were systematically explored. The research results show that the water–rock interaction of acidic slick water fracturing fluid significantly changes the mineral composition and microstructure of mudstone and sandstone, controls the development of induced fractures, and degrades the micro-mechanical properties of rocks, with significant lithological differences. In terms of mineral evolution, the soaking time causes the clay minerals in mudstone to increase by up to 12.0%, while pressure causes the carbonate minerals in sandstone to decrease by up to 23.3%. In terms of induced fracture development, the induced fracture widths of sandstone and mudstone under 30 MPa of pressure increase by 122.4% and 85.7%, respectively. The fracture width of mudstone shows a trend of “increasing first and then decreasing” with time, while that of sandstone decreases monotonically. In terms of micro-mechanical properties, after soaking for 168 h, the Young’s modulus of mudstone decreases by up to 66.9%, much higher than that of sandstone (29.5%), while the decrease in hardness of both is similar (58.3% and 59.8%); the mechanical parameters at the induced fractures are only 53.0% to 73.6% of those in the matrix area, confirming the influence of microstructural heterogeneity. This research provides a theoretical basis and data support for optimizing hydraulic fracturing parameters, evaluating wellbore stability, and predicting the long-term development performance in tight sandstone reservoirs. Full article