Search Results (44)

Under the dual challenges of energy supply demand imbalance and the efficient operation of underground gas storage (UGS) facilities, this study investigated the mechanical behavior of reservoir rocks and optimal production pressure differential in a depleted gas reservoir in China under multi-cycle injection-production. For the first time, we reveal the mechanical degradation mechanism of hydration and cyclic fatigue for three typical lithologies in depleted sandstone reservoirs. Rock mechanics tests were conducted to analyze the effects of lithology, water saturation, and cyclic loading on mechanical properties, and appropriate failure criteria were evaluated. The main findings are as follows: (1) Under a confining pressure of 45 MPa, the peak strength of fine sandstone was the highest at 160.13 MPa, and the peak strength of argillaceous sandstone was the lowest at 114.92 MPa. The strength increased approximately linearly with confining pressure. (2) Increasing water saturation significantly weakened rock strength, particularly in argillaceous sandstone due to hydration effects. At 45% water saturation, its strength decreased by 37.38%. while Young’s modulus and Poisson’s ratio remained relatively unaffected. (3) Rock strength progressively degraded with the number of loading cycles. Siltstone showed the most significant degradation, with a strength reduction of 28.50% after 200 cycles. The damage induced by cyclic loading was less severe than that caused by hydration. (4) Among five failure criteria evaluated, the Mogi–Coulomb criterion demonstrated superior predictive capability by incorporating three-dimensional principal stress effects, showing closest agreement with the experimental data. We further established a depth-dependent production pressure differential profile and proposed a lithology-specific injection-production strategy. These findings provide theoretical foundations for optimizing injection-production strategies and sand control measures in depleted reservoir UGS systems. Full article

To clarify the characteristics of CO₂–formation water–rock reactions in tight sandstones and their effects on CO₂-enhanced oil recovery (EOR) efficiency and storage efficiency, this study takes the tight oil reservoirs of the Changqing Jiyuan Oilfield as the research object. A variety of experimental techniques, including ICP-OES elemental analysis, powder X-ray diffraction, and scanning electron microscopy, were employed to systematically investigate the mechanisms and main influencing factors of water–rock reactions during CO₂ geological storage. The study focused on analyzing the roles of mineral composition, reservoir pore structure, and formation water chemistry in the reaction process. It explored the potential impacts of reaction products on reservoir properties. Furthermore, based on the experimental results, a coupled reservoir numerical simulation of CO₂ injection for EOR and storage was conducted to comprehensively evaluate the influence of mineralization processes on CO₂ EOR performance and long-term storage efficiency. Results show that the tight sandstone reservoirs in Jiyuan Oilfield are mainly composed of calcite, quartz, and feldspar. The dominant water–rock reactions during CO₂ formation–water interactions are calcite dissolution and feldspar dissolution. Among these, calcite dissolution is considered the controlling reaction due to its significant effect on the chemical composition of formation water, and the temporal variation in other elements shows a clear correlation with the calcite dissolution process. Further analysis reveals that water–rock reactions lead to permeability reduction in natural fractures near injection wells, thereby effectively improving CO₂ EOR efficiency, enhancing sweep volume, and increasing reservoir recovery. At the end of the EOR stage, mineralized CO₂ storage accounts for only 0.53% of the total stored CO₂. However, with the extension of time, mineralized storage gradually increases, reaching a substantial 31.08% after 500 years. The study also reveals the effects of reservoir temperature, pressure, and formation water salinity on mineralization rates, emphasizing the importance of mineral trapping for long-term CO₂ storage. These findings provide a theoretical basis and practical guidance for the joint optimization of CO₂ EOR and geological sequestration. Future research may further focus on the dynamic evolution of water–rock reactions under different geological conditions to enhance the applicability and economic viability of CO₂ storage technologies. Full article

Residual oil zones (ROZ) arise under the oil–water contact of main pay zones due to diverse geological conditions. Historically, these zones were considered economically unviable for development with conventional recovery methods because of the immobile nature of the oil. However, they represent a substantial subsurface volume with strong potential for CO₂ sequestration and storage. Despite this potential, effective techniques for assessing CO₂-EOR performance coupled with CCUS in ROZs remain limited. To address this gap, this study introduces a machine learning framework that employs artificial neural network (ANN) models trained on data generated from a large number of reservoir simulations (300 cases produced using Latin Hypercube Sampling across nine geological and operational parameters). The dataset was divided into training and testing subsets to ensure generalization, with key input variables including reservoir properties (thickness, permeability, porosity, Sorg, salinity) and operational parameters (producer BHP and CO₂ injection rate). The objective was to forecast CO₂ storage capacity and oil recovery potential, thereby reducing reliance on time-consuming and costly reservoir simulations. The developed ANN models achieved high predictive accuracy, with R² values ranging from 0.90 to 0.98 and mean absolute percentage error (MAPRE) consistently below 10%. Validation against real ROZ field data demonstrated strong agreement, confirming model reliability. Beyond prediction, the workflow also provided insights for reservoir management: optimization results indicated that maintaining a producer BHP of approximately 1250 psi and a CO₂ injection rate of 14–16 MMSCF/D offered the best balance between enhanced oil recovery and stable storage efficiency. In summary, the integrated combination of reservoir simulation and machine learning provides a fast, technically robust, and cost-effective tool for evaluating CO₂-EOR and CCUS performance in ROZs. The demonstrated accuracy, scalability, and optimization capability make the proposed ANN workflow well-suited for both rapid screening and field-scale applications. Full article

Nano-silica is widely used to enhance gel properties, but its size, concentrations, and aggregation behaviors all matter. The influencing rules of these factors remain unclear especially in harsh reservoir conditions. This study presented a comprehensive investigation into the gelation, rheological, and plugging properties of phenolic polymer gels reinforced by modified nano-silica (GSNP) of different sizes and concentrations in harsh reservoir conditions. Specifically, the nano-silica was modified with a highly soluble silane, and gel properties were evaluated through rheological, differential scanning calorimetry (DSC), and sandpack flooding tests. The results showed that the incorporation of GSNP prolonged the gelation time, enhanced gel strength, and improved stability, allowing the gelation solution to enter deeper into the formation while maintaining long-time effectiveness. The optimal gel system was obtained with 0.4 wt.% GSNP-30, under which condition the storage modulus increased by approximately 14 times, and the content of non-freezable bound water more than doubled. This system exhibited plugging efficiency exceeding 80% in formations with permeabilities ranging from 1000 to 6000 millidarcy and enhanced the oil recovery factor by over 25%. The reinforcement mechanisms were attributed to the adsorption of GSNP onto polymer chains and its role in filling the gel matrix, which enhanced polymer hydrophilicity, suppressed polymer aggregation/curling, prevented ion penetration, and promoted the formation of a more uniform gel network. Careful optimization of nanoparticle size and concentration was essential to avoid the detrimental effects due to nanoparticle overfilling and aggregation. The novelty of this study lies in the practicable formulation of thermal and salt-tolerant gel systems with facile modified nano-silica of varying sizes and the systematic study of size and concentration effects. These findings offer practical guidance for tailoring nanoparticle parameters to cater for high-temperature and high-salinity reservoir conditions. Full article

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

Predicting water losses and final storage in reservoirs has become increasingly relevant in the efficient control and optimization of water provided to agriculture, livestock, industry, and domestic consumption, aiming to mitigate the risks associated with flash floods and water crises. This research aims to develop a new Machine Learning (ML) algorithm based on a water balance model to simulate the outflow in the Cuchoquesera reservoir in the Ayacucho region. The method uses TensorFlow (TF), a powerful interface for graphing and time series forecasting, for data analysis of hydrometeorological parameters (HMP), inflow (QE_obs), and outflow (QS_obs) of the reservoir. The ML water balance model is fed, trained, and calibrated with daily HMP, QE_obs, and QS_obs data from the Sunilla station. The results provide monthly forecasts of the simulated outflow (QS_sim), which are validated with QS_obs values, with significant validation indicators: NSE (0.87), NSE-Ln (0.83), Pearson (0.94), R² (0.87), RMSE (0.24), Bias (0.99), RVB (0.01), NPE (0.01), and PBIAS (0.14), with QS_obs being slightly higher than QS_sim. Therefore, it is important to highlight that water losses due to evaporation and infiltration increased significantly between 2019 and 2023. Full article

Reservoir operations play a pivotal role in shaping the flow regime of the Chao Phraya River Basin (CPRB), where two major reservoirs exert substantial hydrological influence. Despite ongoing efforts to manage water resources effectively, current operational strategies often lack the adaptability required to address the compounded uncertainties of climate change and increasing water demands. This research addresses this critical gap by developing an optimization model for reservoir operation that explicitly incorporates climate variability. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed using four fundamental inputs: reservoir inflow, storage, rainfall, and water demands. Daily resolution data from 2000 to 2012 were used, with 2005–2012 selected for training due to the inclusion of multiple extreme hydrological events, including the 2011 flood, which enriched the model’s learning capability. The period 2000–2004 was reserved for testing to independently assess model generalizability. Eight types of membership functions (MFs) were tested to determine the most suitable configuration, with the trapezoidal MF selected for its favorable performance. The optimized models achieved Nash-Sutcliffe efficiency (NSE) values of 0.43 and 0.47, R² values of 0.59 and 0.50, and RMSE values of 77.64 and 89.32 for Bhumibol and Sirikit Dams, respectively. The model enables the evaluation of both dam operations and climate change impacts on downstream discharges. Key findings highlight the importance of adaptive reservoir management by identifying optimal water release timings and corresponding daily release-storage ratios. The proposed approach contributes a novel, data-driven framework that enhances decision-making for integrated water resources management under changing climatic conditions. Full article

This paper describes an innovatively designed experimental method for fracturing fluid energy storage to explore the energy storage mechanism during the well shut-in process of fractured shale reservoirs. By improving the existing core clamp and adding fracturing fluid cavities and large volume intermediate containers to simulate artificial fractures and remote shale reservoirs, the pressure changes in the core during the well shut-in process were monitored under the conditions of a real oil–water ratio and real pressure distribution to explore the energy storage law of the shut-in fluid in fractured shale reservoirs. Compared to the 0.62 MPa energy storage obtained from traditional energy storage experiments (without artificial fractures or remote shale reservoirs), the experimental scheme proposed in this paper achieved a 2.45 MPa energy storage, consistent with the field’s monitoring results. The energy storage effects of four fracturing fluids were compared, namely pure CO₂, CO₂ pre-fracturing fluid, slickwater pre-fracturing fluid, and pure slickwater fracturing fluid. Due to the characteristics of a high expansion coefficient and low interfacial tension of pure CO₂, the energy storage effect was the best, and the pressure equilibrium time was the shortest. Considering factors such as comprehensive economy and energy storage efficiency, the optimal range for CO₂ pre-injection is between 20% and 30%. Based on the optimization criterion of energy storage pressure balance, it is recommended that the optimal CO₂ shut-in time be 5 h and the slickwater be 12.8 h. Considering the economic, sand carrying, and energy storage effects, and other factors, CO₂ pre-storage has the best imbibition effect, and the optimal CO₂ pre-storage range is 20~30%. The research results provide theoretical support for energy storage fracturing construction in other shale oil reservoirs of the same type. Full article

Due to water scarcity in the Yellow River basin, the existing operations for the Longyangxia and Liujiashan cascade reservoirs are insufficient to meet the demands of multiple objectives. This study establishes a coupled coordination model considering hydropower generation, water supply, and storage capacity at different periods during the year. At the same time, the model quantifies the impact of scheduling strategies on multiple objectives and determines the optimal operation for reservoirs at different periods. The results indicate that the scheduling strategy of the Longyangxia reservoir dominates the changes in hydropower generation, water supply, and storage capacity. Specifically, during the ice flood control period, the scenario of continuous release from Longyangxia and continuous storage at Liujiaxia achieves 1.26 billion kWh of hydropower generation, with a water supply shortage rate of 8.67%; During the non-flood period, releasing water from Longyangxia in April and May and storing it in June while Liujiaxia continuously releases water results in 4.68 billion kWh of hydropower generation and a shortage rate of 1.61%. During the flood control period, continuous storage at Longyangxia and controlling the water level of Liujiashan within flood control limits, with storage in September and release in October, achieves 5.65 billion kWh of hydropower generation and a shortage rate of 0%. Full article

Efficient reservoir management is essential for ensuring water security and flood control, as well as hydroelectric power generation. Accurate volume measurements are key to optimizing these functions, but traditional methods—such as in situ measurements and physical surveys—are often time-consuming, costly, and unfeasible in many regions due to financial or geographical limitations. This study introduces a novel globally accessible remote sensing tool designed to overcome these challenges by providing a more effective approach to reservoir volume estimation. The tool leverages high-resolution satellite imagery from Sentinel-2 and integrates it with official storage capacity data and the GLOBAthy dataset to calculate surface area and reservoir volume across varying water levels over user-defined timeframes. Users can select reservoirs, date ranges, and cloud cover thresholds via an intuitive interface, which then generates time-series data of reservoir volumes. The tool employs machine learning algorithms to improve the precision of water surface delineation and volume calculations, accounting for complex environmental factors like cloud cover and built structures such as bridges. This remote sensing tool was tested on reservoirs of varying sizes and topographies in Portugal and California, USA, demonstrating a high accuracy with a Mean Absolute Percentage Error (MAPE) of 5.35% and a correlation coefficient (R²) of 0.90 when compared to official records. By offering a cost-effective, scalable, totally remote, and timely solution, the tool enables improved reservoir monitoring, particularly in remote or otherwise inaccessible areas. Ultimately, this research contributes to global water resources management, enhancing the sustainability and resilience of reservoir operations around the world. Full article

This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water availability challenges. Effective reservoir management in these regions is critical for balancing water storage and downstream release and maintaining ecosystem health under variable hydrological conditions. The performance of the fuzzy method was compared to two meta-heuristic algorithms: gravitational search algorithm (GSA) and shuffled frog leaping algorithm (SFLA). System performance was assessed using key indices such as the reliability index as a measure of meeting water demands. The fuzzy method achieved the highest reliability index of 0.690, outperforming GSA (0.677) and SFLA (0.688), demonstrating its superior ability to ensure consistent water supply downstream. The fuzzy method, leveraging expert knowledge, not only enhanced downstream water supply reliability but also reduced computational time compared to the meta-heuristic approaches. The incorporation of expert opinions provides a practical, robust, and efficient framework for reservoir management in challenging climate conditions such as Cfa/Csa classes. Additionally, the fuzzy solution demonstrated superior adaptability to diverse hydrological conditions, balancing ecological and water supply needs effectively. These findings highlight the potential of using expert opinions to support sustainable reservoir operations by achieving optimal trade-offs between competing objectives and addressing challenges in water resource management under varying climatic conditions. Full article

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects. Full article

Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. This study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates artificial intelligence techniques with dynamic data and geological features to more accurately identify inter-well connectivity and its evolution. A comprehensive sensitivity analysis was performed on single-well pairs and multi-well groups regarding the permeability amplitude, the width of the high permeable channel, change, and lasting period of injection pressure. In addition, the production performance of multi-well groups, especially the development of ineffective circulation channels and their effects on reservoir development, are studied in-depth. The results show that higher permeability, wider permeable channels, and longer injection pressure maintenance can significantly enhance inter-well connectivity coefficients and reduce time-lag coefficients. Inter-well connectivity in multi-well systems is significantly affected by well-group configuration and inter-well interference effects. Based on the simulation results, the evaluation index of ineffective circulation channels is proposed and applied to dozens of well groups. These identified ineffective circulation channel changing patterns provide an important basis for optimizing oil fields’ injection and production strategies through data-driven insights and contribute to improving oil recovery. The integration of artificial intelligence enhances the ability to analyze complex datasets, allowing for more precise adjustments in field operations. This paper’s research ideas and findings can be confidently extended to other engineering scenarios, such as geothermal development and carbon dioxide storage, where AI-based models can further refine and optimize resource management and operational strategies. Full article

To enhance the efficiency of solving the optimal operation model for cascade reservoirs, this paper first constructed an optimal operation model of cascade reservoirs. The model comprehensively considered the ecological flow, the guaranteed output of hydroelectric power plants, and the relaxation constraints of the water level at the end of water supply and storage period. The relaxation constraints refer to two relaxation variable constraints, which are used to ensure that the water levels decline in the water supply period and rise in the water storage periods. At the same time, to avoid the challenges of “dimension disaster” and susceptibility to local optima commonly encountered in existing optimization algorithms when resolving the above model, a novel optimization algorithm, M-IWO-ODDDP, derived from the optimization principles of the Invasive Weed Optimization (IWO) and Discrete Differential Dynamic Programming (DDDP) algorithms, was proposed in this paper. The 11 cascade hydropower stations in the Wujiang River basin were used as a case study, and the results showed that the water-level dispatching process exhibited a high degree of conformity with the actual dispatching process during both the water supply and storage periods. Furthermore, the output calculation results based on the M-IWO-ODDDP algorithm were 3.94% and 0.30% higher than the actual output and ODDDP calculation results, respectively, while reducing water abandonment by 21.58% and 4.07%. Full article

Increasing the share of renewable energy sources (RESs) in the energy mix of countries is one of the main objectives of the energy transition in national economies, which must be established on circular economy principles. In the natural gas storage in geological structures (UGSs), natural gas is stored in a gas reservoir at high reservoir pressure. During a withdrawal cycle, the energy of the stored pressurised gas is irreversibly lost at the reduction station chokes. At the same time, there is a huge amount of produced reservoir water, which is waste and requires energy for underground disposal. The manuscript explores harnessing the exergy of the conventional UGS reduction process to generate electricity and produce hydrogen via electrolysis using reservoir-produced water. Such a model, which utilises sustainable energy sources within a circular economy framework, is the optimal approach to achieve a clean energy transition. Using an innovative integrated mathematical model based on real UGS production data, the study evaluated the application of a turboexpander (TE) for electricity generation and hydrogen production during a single gas withdrawal cycle. The simulation results showed potential to produce 70 tonnes of hydrogen per UGS withdrawal cycle utilising 700 m³ of produced field water. The analysis showed that hydrogen production was sensitive to gas flow changes through the pressure reduction station, underscoring the need for process optimisation to maximise hydrogen production. Furthermore, the paper considered the categorisation of this hydrogen as “green” as it was produced from the energy of pressurised gas, a carbon-free process. Full article