Search Results (1,055)

Efficient development of karst geothermal resources relies on the accurate identification of thermophysical and hydrogeological parameters. In this paper, the integrated wellbore–reservoir model of fluid and heat transport is applied to identify hydrothermal parameters of the karst geothermal system in Tianjin, China, based on multi-type field test data. A natural state model is conducted by fitting steady-state borehole temperature measurement results to identify formation thermal conductivity, while reservoir permeability is determined via the Gauss–Marquardt–Levenberg optimization algorithm based on dynamic temperature and pressure data from pumping tests. The parameter identification results indicate a reservoir permeability of 5.25 × 10⁻¹⁴ m² and a corrected bottom-hole temperature of 109 °C. Subsequently, productivity optimization for actual heating demands (1.33 × 10⁵ m²) yields an optimal heat extraction efficiency of 6.17 MW, with a flow rate of 80 m³/h, an injection well perforated length of 388 m, and an injection temperature of 30 °C. Additionally, addressing reservoir heterogeneity, the study finds that high-permeability zones between wells significantly shorten the safe operation duration of geothermal doublets, and reducing flow rate can mitigate thermal breakthrough risk to a certain extent. Full article

This study investigates the mechanisms behind strong water sensitivity in some low-clay-mineral-content tight conglomerate reservoirs in China’s Mahu Sag. Using core-scale water sensitivity tests, mineral analysis, in situ micro-CT scanning, and digital core techniques, we analyzed how water sensitivity alters pore structures across cores of varying permeability. Key findings include the following: (1) Water sensitivity damage increases as initial gas permeability decreases. (2) Despite low clay content, significant water sensitivity arises from the combined effect of water and velocity sensitivity, driven mainly by illite and kaolinite concentrated in gravel-edge fractures and key flow channels. (3) Water sensitivity causes non-uniform pore structure changes—some macropores and throats enlarge locally, reflecting heterogeneity. (4) Structural responses differ by permeability: medium–low permeability cores suffer from clay mineral swelling and particle migration, whereas high-permeability cores resist overall damage and may even have main flow paths enhanced by flushing. (5) Water sensitivity mainly degrades smaller pores but can improve larger ones, with the critical pore-size threshold between macro- and micro-pores inversely related to permeability. This work clarifies the pore-scale mechanisms of water sensitivity in some low-clay-mineral-content tight conglomerates, and can provide guidance for the optimization of water types injected into similar conglomerate reservoirs. Full article

The Cenomanian Bahariya Formation exposed at Gebel El Dist in the Western Desert of Egypt provides valuable surface analogues for evaluating the reservoir quality of subsurface Bahariya sandstones. The formation was analyzed using 27 oriented samples and 91 core plugs from quartz arenite (QA) and quartz wacke (QW) facies. Analyses included XRD, petrography, SEM, helium porosity–permeability, and capillary tests, as well as measurements of pore-throat radii (R) at 35% and 36% mercury saturation. X-ray diffraction analyses reveal a heterogeneous mineral composition dominated by quartz, feldspars, dolomite, pyrite, siderite, goethite, hematite, clay minerals, glauconite, and gypsum. QA displays higher porosity and permeability than QW, along with larger pore radii, and lower specific surface area per unit pore volume (Spv) and per unit grain volume (Sgv). Multivariate regression equations, specific to each facies, were developed to convert standardized XRD mineral percentages directly into pore-system and flow attributes (ϕ, k, r, Spv, Sgv, R35, R36), quantifying capillary-based recovery contrasts between facies. Across both facies, regressions linking mineralogy to ϕ, k, r, Spv, Sgv, R35, and R36 are strong (R² = 0.78–1.00). The established predictive equations provide a low-cost method to estimate reservoir quality from mineralogy alone, enabling rapid screening of Cenomanian Bahariya analogues and similar clastic reservoirs where core data are limited. Full article

Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a representative large-scale bank slope in the Three Gorges Reservoir area known for its significant deformation responses to rainfall and reservoir-level fluctuations. The landslide’s behavior, characterized by notable hysteresis and nonlinear trends, poses a significant challenge to accurate prediction. To address this, we derived high-precision time-series deformation data by applying atmosphere-corrected Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to Sentinel-1A imagery, with validation from GNSS measurements. A systematic analysis was then conducted to uncover the correlation, hysteresis, and spatial heterogeneity between landslide deformation and key influencing variables (rainfall, water level, temperature). Furthermore, we proposed a Spatio-Temporal Enhanced Convolutional Neural Network (STE-CNN), which innovatively converts influencing variables into grayscale images to enhance spatial feature extraction, thereby improving prediction accuracy. The results indicate that: (1) From June 2022 to March 2024, the landslide showed an overall downward displacement trend, with maximum settlement and uplift rates of −49.34 mm/a and 21.77 mm/a, respectively; (2) Deformation exhibited significant correlation, hysteresis, and spatial variability with environmental factors, with dominant variables shifting across seasons—leading to intensified movement in flood seasons and relative stability in dry seasons; (3) The improved STE-CNN outperforms typical prediction models in forecasting landslide deformation.This study presents an integrated methodology that combines InSAR monitoring, multi-factor mechanistic analysis, and deep learning, offering a reliable solution for landslide early warning and risk management. Full article

The Middle Jurassic Lianggaoshan and Shaximiao Formations are the primary crude oil reservoirs in the central Sichuan Basin, offering significant resource potential. However, studies on reservoir characterization across different lithologies remain limited. This study focuses on fluvial–deltaic sandstones, siltstones, and lacustrine shales, analyzing pore types, structures, pore size distribution, and connectivity using various methods, including X-ray diffraction (XRD), thin-section analysis, scanning electron microscopy (SEM), high-pressure mercury injection, low-temperature nitrogen adsorption, and nuclear magnetic resonance (NMR) spectroscopy. The results show that sandstones exhibit the largest pore space, followed by siltstones, while shales have the smallest pore space. These reservoirs are relatively tight, with poor connectivity and high heterogeneity. Sandstone reservoirs, with their high quartz content, represent high-quality reservoirs because of their relatively good connectivity. Therefore, areas with well-developed natural fractures in sandstone are considered high-quality targets. For nanoscale reservoirs in siltstone and shale, horizontal fracturing is essential to improve reservoir properties, provided that source–reservoir matching is adequate. This study offers a detailed reservoir characterization across different lithologies, providing new insights for the optimization of favorable crude oil zones in the central Sichuan Basin. Full article

As an effective energy storage solution, gas reservoir compressed air energy storage (CAES) can efficiently utilize curtailed wind power to meet urban electricity demands. Well pattern optimization enables rational design and adjustment of well layouts to maximize productivity, efficiency, and economic benefits while reducing energy losses and operational costs. To address limitations in conventional optimization methods—including oversimplified constraints, neglect of reservoir heterogeneity, and insufficient consideration of complex flow regimes—this study proposes an innovative multi-constraint well pattern optimization method incorporating productivity, energy conversion efficiency, drainage area, and economic performance for quantitative evaluation of well configurations. First, the reservoir flow domain was partitioned based on two flow regimes (Darcy and non-Darcy flow) near wells. Mathematical flow equations accounting for reservoir heterogeneity were established and solved using the rectangular grid method to determine productivity and formation pressure distributions for vertical and horizontal wells. Second, a drainage radius prediction model was developed based on pressure drop superposition principles to calculate gas drainage areas. Finally, an optimization function F, integrating productivity models and drainage radius calculations through ratio optimization criteria, was formulated to quantitatively characterize well pattern performance. An optimization workflow adhering to inter-well interference minimization principles was designed, culminating in a comprehensive CAES well pattern optimization framework. Case studies and sensitivity analyses on the depleted Mabei Block 8 CAES reservoir demonstrated the following: The quantitative optimization metric w decreases with increasing reservoir heterogeneity. w exhibits a unimodal relationship with production pressure differential, peaking at approximately 2.5 MPa. Optimal configuration was achieved with 3 horizontal wells and 23 vertical wells. Full article

Continental high water-cut reservoirs commonly exhibit strong heterogeneity, high viscosity, and insufficient reservoir drive, which has motivated the deployment of polymer-based composite chemical flooding, such as surfactant–polymer (SP) and alkali–surfactant–polymer (ASP) processes. However, conventional experimental techniques have limited ability to resolve intermolecular forces, and the coupled mechanism linking “formulation composition” to “microstructural evolution” remains insufficiently defined, constraining improvements in field performance. Here, scanning electron microscopy (SEM), backscattered electron (BSE) imaging, and molecular dynamics (MD) simulations are integrated to systematically investigate microstructural features of polymer composite systems and the governing mechanisms, including hydrogen bonding and electrostatic interactions. The results show that increasing the concentration of partially hydrolyzed polyacrylamide (HPAM) promotes hydrogen bond formation and the development of network structures; a moderate amount of surfactant strengthens interactions with polymer chains, whereas overdosing loosens the structure via electrostatic repulsion; the introduction of alkali reduces polymer connectivity, shifting the system toward an ion-dominated dispersed morphology. These insights provide a mechanistic basis for elucidating the behavior of polymer composite formulations, support enhanced chemical flooding performance, and ultimately advance the economic and efficient development of oil and gas resources. Full article

In response to the development challenges caused by the high initial water saturation, low porosity, low permeability, and strong heterogeneity in C tight sandstone reservoirs, a comprehensive study was conducted on the optimization of development methods using a fuzzy model, core flooding experiments, and reservoir numerical simulations. The initial evaluation indicates the good adaptability of CO₂ flooding for improving oil recovery in a C reservoir; the experimental result of the CO₂ displacement method also performs the best, with a recovery rate of 68.38% at a connate water saturation of about 30%, compared with surfactant flooding and water flooding. However, higher water saturation inhibits the CO₂ development effect. The oil recovery factor of pure CO₂ huff-n-puff is 32.24% lower than the CO₂ displacement method, while surfactant-assisted CO₂ huff-n-puff can increase the recovery rate by 0.85% compared to pure CO₂. Based on actual geological models, numerical simulations were conducted on Well Block A and B. The results showed that the optimized production pressure is above the Minimum Miscibility Pressure (16.44 MPa); with consideration of the fracture pressure limitation, the CO₂ injection rate in Block A should be less than 3000 m³/d, and the recovery rate after 10 years is only 0.48% (oil change ratio is 0.07 t/t), while the CO₂ displacement rate of Block B should not exceed 7500 m³/d, and the recovery rate after 10 years can reach 27.39% (oil change ratio is 0.2 t/t). CO₂ displacement is an effective development method for a C reservoir, but due to a high water content the oil change ratio is very low, indicating a low potential for further development. The research provides important references for the development of similar oil reservoirs. Full article

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams. Full article

Climate change mitigation demands scalable, technologically mature solutions capable of addressing emissions from hard-to-abate sectors. Carbon Capture and Storage (CCS) offers one of the few ready pathways for deep decarbonization by capturing CO₂ at large point sources and securely storing it in deep geological formations. The long-term viability of CCS depends on well control strategies/injection schedules that maximize storage capacity, maintain containment integrity, ensure commercial deliverability and remain economically viable. However, current practice still relies heavily on manual, heuristic-based well scheduling, which struggles to optimize storage capacity while minimizing by-products such as CO₂ recycling within the high-dimensional space of interdependent technical, commercial, operational, economic and regulatory constraints. This study makes two contributions: (1) it systematically reviews, maps and characterizes these multidimensional constraints, framing them as an integrated decision space for CCS operations, and (2) it introduces an industry-ready optimization framework—Automated Optimization of Well control Strategies through Dynamic Time–Space Discretization—which couples reservoir simulation with constraint-embedded, hierarchical refinement in space and time. Using a modified genetic algorithm, injection schedules evolve from coarse to fine resolution, accelerating convergence while preserving robustness. Applied to a heterogeneous saline aquifer model, the method was tested under both engineering and financial objectives. Compared to an industry-standard manual schedule, optimal solutions increased net stored CO₂ by 14% and reduced recycling by 22%, raising retention efficiency to over 95%. Under financial objectives, the framework maintained these technical gains while increasing cumulative cash flow by 23%, achieved through leaner, smoother injection profiles that minimize costly by-products. The results confirm that the framework’s robustness, scalability and compatibility with commercial simulators make it a practical pathway to enhance CCS performance and accelerate deployment at scale. Full article

Fractured-vuggy carbonate reservoirs primarily have pores and caves as their main storage spaces with poor fracture development, resulting in low reservoir connectivity and strong heterogeneity. During nitrogen injection developments, the fluidity of the medium is poor, and gas tends to form dominant flow channels, leading to a short response time. Consequently, the displacement of crude oil in such reservoirs is limited, leaving a large proportion of residual oil trapped within the pore and vug systems. Based on the Tarim fractured-vuggy carbonate reservoir, a two-dimensional visualized physical model of the fractured-vuggy body was designed and constructed to conduct a foam-assisted gas displacement physical experiment. The research shows that foam has good oil recovery efficiency and dominant channel-blocking ability, which can effectively mobilize the residual oil in the fractures and vugs after gas displacement. In the vertical direction, the foam-assisted gas flooding mechanism primarily involves gravity segregation and interfacial tension reduction between oil and water; horizontally, it operates by selectively blocking large fractures and main channels, redirecting gas into smaller and more tortuous pathways, thus enhancing overall sweep efficiency. Once dominant flow channels develop, injecting salt-sensitive foam at a 2:1 gas–liquid ratio and 0.3 pore volume can raise the recovery factor from around 3% to nearly 19%, representing an improvement of about 16%, thereby boosting both gas flooding performance and overall field development efficiency. Full article

As a cornerstone of recoverable reserve prediction in water flooding projects, characteristic curve analysis has proven to be critical for reservoir management in the G Oilfield. This study introduces an enhanced methodology that significantly improves prediction accuracy through three key innovations: (1) development of a modified Type A curve with correction factor c to address early-stage nonlinear deviations, reducing prediction errors from 12.7% to 4.3% across 35 wells; (2) establishment of phase-specific model selection criteria demonstrating Type C curve superiority (>80% water cut) versus Zhang/Yu-type curves’ effectiveness in heterogeneous reservoirs (water cut ≥ 50%, errors < 5%); and (3) implementation of an integrated workflow incorporating linear segment optimization and economic threshold standardization. Field validation through 15-year production data (2008–2023) confirms <6% error in recovery factor predictions, significantly enhancing development strategy formulation. The technical framework provides novel insights into the water flooding curve theory while offering practical solutions for mature field management, particularly in complex continental reservoirs. Full article

The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment. Full article

In gas reservoirs, high gas velocity causes significant inertial effects, leading to a nonlinear relationship between pressure gradient and velocity, especially near wellbores or fractures. In such cases, Darcy’s law is inadequate, and the Forchheimer equation is commonly used to model nonlinear flow behavior. Although the Forchheimer equation improves simulation accuracy for high-velocity flow in porous media, incorporating it into conventional numerical simulations greatly increases computational time, as nonlinear flow equations must be solved over the entire reservoir. This difficulty is exacerbated in heterogeneous fractured reservoirs, where complex fracture–matrix interactions and localized high-velocity flow complicate solving nonlinear equations. To address this, this work proposes a fast numerical simulation method based on diffusive time of flight (DTOF). By using DTOF as a spatial coordinate, the original three-dimensional flow equations incorporating the Forchheimer equation are reduced to a one-dimensional form, enhancing computational efficiency. DTOF represents the diffusive time for a pressure disturbance from a well to reach a specific reservoir location and can be efficiently computed by solving the Eikonal equation via the fast marching method (FMM). Once the DTOF field is obtained, the three-dimensional problem is transformed into a one-dimensional problem. This dimensionality reduction enables fast and reliable modeling of nonlinear high-velocity gas transport in complex reservoirs. The proposed method’s results show good agreement with those from COMSOL Multiphysics, confirming its accuracy in capturing nonlinear gas flow behavior. Full article

The Shunbei 42X well group belongs to fractured-vuggy carbonate condensate gas reservoirs. This type of reservoir exhibits extreme heterogeneity, differing significantly from conventional reservoirs and posing considerable challenges for exploitation. Research on fractured-vuggy carbonate condensate gas reservoirs can begin with modeling and numerical simulation. By using historical data fitting to refine parameters such as pressure, production, and reserves, we can deepen our understanding of the reservoir and the movement patterns of water and oil. Combined with a geological and reservoir engineering analysis of residual oil distribution, this approach enables the evaluation of steady-state production technology feasibility. This study employs numerical simulation to conduct single-well injection production modeling for well SHB42X. First, a numerical model was created in simulation software, defining parameters such as grid spatial location and reservoir temperature. Second, the numerical model was established, and historical production dynamics were fitted using the software’s PVT module. Finally, after successful fitting, subsequent production parameters were set. By summarizing previous studies on gas injection huff-and-puff mechanisms and analyzing changes in parameters like recovery rates after actual injection, the simulation results for natural gas, nitrogen, water, and depleted reservoir development were compared. Further comparisons are made on the throughput effects of nitrogen under varying injection rates, production rates, injection volumes, and well-killing durations. Optimal parameters are selected to provide reference for enhancing subsequent development efficiency. Full article