Reservoir Flood Research Articles

Displacement Mechanism and Flow Characteristics of Polymer Particle Dispersion System Based on Capillary Bundle Model

During the development of oil reservoirs, a rapid increase in water cut following reservoir flooding leads to inefficient or ineffective circulation of injected water, rendering a significant portion of the remaining oil in the reservoir inaccessible. The displacement method using polymer particle dispersion systems effectively solves the issue of rapid water breakthrough in oil reservoirs. Owing to the particle phase separation phenomenon, polymer particles can selectively penetrate into the larger pores where water circulation is inefficient, enhance their flow resistance, and thereby achieve equilibrium displacement along with an increased swept volume. This paper investigates the heterogeneous distribution of polymer particles within a porous medium, incorporates the red blood cell dendrite concentration distribution theory from biological fluid mechanics, and develops a mathematical model to delineate the viscosity characteristics of polymer particle dispersion systems, taking into account the phase separation phenomenon. Building on this foundation, it formulates a capillary bundle model for the polymer particle dispersion system specifically designed for oil displacement and proceeds to determine its relative permeability curve. Simulation outcomes reveal that at a water saturation level of 0.063, the concentration of polymer particles in fractured large pore capillaries is markedly elevated, yet capillaries with a pore size under 26 μm remain devoid of polymer particles. With the increase of water saturation, the concentration of polymer particles in large pore capillaries reduces, whereas it progressively augments in medium pore capillaries. Upon reaching a peak water saturation of 0.751, capillaries smaller than 18 μm are entirely free of polymer particles. These findings suggest that the heterogeneous distribution of polymer particles markedly inhibits the percolation capabilities of the dispersed system following a water phase breakthrough, facilitating the entry of more dispersion into oil-laden capillaries and thus enhancing the flow capacity of the oil phase.

Quantitative characterization of crude oil mobilization in microscopic pores during forward and reverse flooding in tight sandstone reservoirs using various fracturing fluid systems

In the transition from conventional to unconventional oil and gas exploration, the focus on tight sandstone reservoirs has grown due to their complex microscopic pore structures, narrow pore throats, and high capillary pressures. These characteristics often lead to the retention of fracturing fluids post-fracturing, hindering fluid recovery and reducing crude oil extraction efficiency. To address these issues, this study employed a combination of microscopic displacement experiments and nuclear magnetic resonance (NMR) analyses to investigate oil displacement mechanisms at the microscale in tight sandstone reservoirs. The experimental setup involved testing three core samples using distinct fracturing fluid systems: a guar gum-based system (System I), an EM30+ + guar gum water-based system (System II), and a CNI (Carbon Nanotechnologies Inc) nano variable-viscosity slick water-based system (System III). The study aimed to assess the impact of these different fracturing fluid systems on oil displacement efficiency in both forward and reverse directions. Results revealed that forward oil displacement efficiencies for Systems I to III were 26.86%, 33.3%, and 33.43%, respectively. In contrast, reverse displacement efficiencies were slightly higher at 28.9%, 37.5%, and 38.34%. Overall, Systems II and III outperformed System I in terms of oil displacement efficiency. The study also found that in cores with large pores, forward displacement predominantly mobilized crude oil, while reverse displacement was more effective in smaller pores. This trend was consistent in cores exhibiting bimodal pore distributions. However, in unimodal cores, small pores were significantly impacted in both directions. Notably, System III demonstrated a remarkable efficiency in mobilizing crude oil within smaller pores.

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

AbstractInforming reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

Molecular simulation study on interfacial microstructural changes of CO2 flooding in tight porous environment

In this research, the interfacial microstructure dynamics during CO2 flooding in tight porous reservoirs were explored through molecular dynamics simulations. Initially, the study focuses on the structural attributes of the gas–liquid interface in a static state, noting a transition from dissolution to swelling as tightness increases. Subsequently, the research diverges into two distinct areas: the quasi-static and dynamic displacement processes. The analysis of diffusivity and dissolution in the oil phase under quasi-static conditions indicates that the interaction energy between components is influenced by the degree of tightness. In contrast, during dynamic displacement at a constant pressure, tight reservoirs exert an anchoring effect, hindering the movement of the oil phase at 3 MPa. However, at 10 MPa, CO2 is likely to achieve a gas breakthrough in less tight reservoirs, while the adsorption strength of residual oil in the reservoirs concurrently escalates. The study concludes that the stability of the interfacial region plays a dual role in determining the sustainability of liquid exchange in these processes.

Investigation of the processes of additional oil displacement by nanosuspension silicon oxide from a model core

The use of nanosuspensions during reservoir flooding is an alternative to chemical methods for enhancing oil recovery. In this work, the effectiveness of using nanosuspensions as a post-displacement agent after the base agent (water) was shown. Filtration tests for additional oil displacement from model rock samples were carried out. Suspensions of nanoparticles were used for post-displacement. The mass concentration of spherical silicon oxide nanoparticles (SiO2) varied from 0.01 to 0.25%wt, and their size varied from 10 to 35 nm. The permeability of the model core was 50 mD. An experimental measurement of the interfacial tension and the contact angle of wettability was performed. It is shown that with an increase in the concentration of nanoparticles, the interfacial tension of the “oil — suspension” decreases. It has been established that at a fixed concentration of nanoparticles (0.1%), with an increase in the size of nanoparticles, the interfacial tension decreases. It was revealed that when using suspensions, the contact angle of rock wetting with oil changes significantly. As a result of filtration tests, dependences of the oil displacement efficiency on the concentration and size of nanoparticles were obtained. It has been shown that additional volume of oil can be recovered after filtering nanosuspensions.

Sustainability: study on rural properties affected by the construction of a hydroelectric power plant in northern region of the state of Mato Grosso/Brazil

The northern region of the State of Mato Grosso (MT), in Brazil, nationally recognized as a significant grain producer in the country, is undergoing transformations regarding key aspects of sustainability in certain areas near the construction site of the Hydroelectric Power Plant - HPP. In this scenario, the objective of this research was defined as studying the sustainability aspects perceived in the rural properties affected by the construction of a hydroelectric power plant in the municipality of SINOP (MT). To achieve the stated objective, a study was conducted on a sample of 15 rural properties that had a portion of their land affected by the construction of the power plant and in the process of reservoir flooding. Data collection occurred through the application of questionnaires, semi-structured script interviews with rural property owners, on-site and documentary observation, whose results will be presented through qualitative argumentative analysis, supported when necessary by quantitative parameters. The obtained results reveal that properties located in this region were directly impacted by the construction of this project, undergoing changes in environmental, economic, and social aspects, causing irreversible damage to the properties and hindering sustainable development in the region. In this context, sustainability assessment processes are considered important allies for managing sustainability in the agriculture sector and decision-making, as farmers in this region express concern about maintaining sustainability criteria and seek a balance between increased productivity and sustainable development.

Mathematical modeling of mass transfer in CO2 injection for confined fluids at near-miscible condition

Complex interaction between fluid and solid in tight formations leads to capillary confinement and interface slip. Mass transfer between oil and CO2 in CO2 flooding is essential to understand the flow complexity. Therefore, the objective of this work is to propose a comprehensive model to analyze the mass transfer during CO2 injection in the confined fluids. The modified Peng-Robinson equation of state considering the fluid-wall interaction and adsorption was established to calculate the properties of hydrocarbons and CO2. Relative permeability for the near-miscible state was then estimated using the new relative permeability model with corner flow effect. Predicted results agree well with the experimental observations. Afterwards, it was integrated with a one-dimension mathematical model to analyze the physical mechanisms of CO2 flooding in tight reservoirs. Results indicate that nanoconfinement increases the relative permeability at near-miscible condition, and confined fluid is more likely to be miscible than the bulk fluid. Additionally, nanoconfinement can reduce the velocity of CO2 front and increase the breakthrough time of CO2, which is favorable to improve the efficiency of CO2 flooding. This model provides a deep understanding of mass transfer between CO2 and hydrocarbons, and it can be integrated with compositional simulators to solve the field-scale cases including the microscale mechanism of tight reservoirs.

Analyzing the Microscopic Production Characteristics of CO2 Flooding after Water Flooding in Tight Oil Sandstone Reservoirs Utilizing NMR and Microscopic Visualization Apparatus

The microscopic pore structure of tight sandstone reservoirs significantly influences the characteristics of CO2 flooding after water flooding. This research was conducted using various techniques such as casting thin sections, high-pressure mercury injection, scanning electron microscopy, nuclear magnetic resonance (NMR) testing, and a self-designed high-temperature and high-pressure microscopic visualization displacement system. Three types of cores with different pore structures were selected for the flooding experiments and the microscopic visualization displacement experiments, including CO2 immiscible flooding, near-miscible flooding, and miscible flooding after conventional water flooding. The characteristics of CO2 flooding and the residual oil distribution after water flooding were quantitatively analyzed and evaluated. The results show the following: (1) During the water flooding process, the oil produced from type I and type III samples mainly comes from large and some medium pores. Oil utilization of all pores is significant for type II samples. The physical properties and pore types have a greater impact on water flooding. Type I and II samples are more suitable for near-miscible flooding after water flooding. Type III samples are more suitable for miscible flooding after water flooding. (2) In CO2 flooding, oil recovery increases gradually with increasing pressure for all three types of samples. Type II core samples have the highest recovery. Before miscibility, the oil recovered from type I and type II samples is primarily from large pores; however, oil recovery mainly comes from medium pores when reaching miscibility. As for the type III samples, the oil produced in the immiscible state mainly comes from large and medium pores, and the enhanced oil recovery mainly comes from medium and small pores after reaching the near-miscible phase. (3) It can be seen from the microscopic residual oil distribution that oil recovery will increase as the petrophysical properties of the rock model improve. The oil recovery rate of near-miscible flooding after water flooding using the type II model is up to 68.11%. The oil recovery of miscible flooding after water flooding with the type III model is the highest at 74.57%. With increasing pressure, the proportion of flake residual oil gradually decreases, while the proportion of droplet-like and film-like residual oil gradually increases. Type II samples have a relatively large percentage of reticulated residual oil in the near-miscible stage.

Streamflow prediction using support vector regression machine learning model for Tehri Dam

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study

The utilization of CO2 flooding is a widely applied enhanced oil recovery (EOR) technique in mature onshore oil fields. As well as being able to increase oil production and recovery it offers the potential to provide long-term, geological storage for carbon in subsurface reservoirs, thereby contributing to the mitigation of carbon emissions originating from human activities. Substantial research efforts have provided some insight into the uncertainties associated with CO2-EOR projects, but further understanding and the development of more reliable methods are required to accurately predict the outcomes of the complex processes involved in CO2 reservoir flooding. In this study, four machine learning (ML) algorithms were developed to predict CO2 storage mass and cumulative oil production, using the CMG-GEM three-phase flow compositional reservoir simulator with nine reservoir input variables covering a range of uncertainties associated with oil zones. The Mahalanobis distance technique was applied for identifying and excluding outlier data points from the target variable distributions of training data groups. 520 and 439 data records were excluded from the CO2 storage quantity and cumulative oil production dataset, respectively, to generate more reliable predictions. Meticulous training and testing of the ML models revealed that, of the models evaluated, the LSSVM model generated the lowest prediction errors with the test dataset (RMSE = 0.7811 million mt for CO2 storage mass; RMSE = 10.1245 million barrels for cumulative oil production) demonstrating excellent generalization capabilities.

Early warning methods of chemical agent channeling in polymer–surfactant flooding reservoirs

AbstractIn the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs.

Synthesis and emulsion stability of salt resistant viscosity reducer for heavy oil reservoirs with varying salinities

To address the inadequate salt resistance of viscosity reducers and their limited applicability to reservoirs with single salinity, we synthesized salt-resistant viscosity reducers PANxAn in this study. These compounds were derived from fatty alcohol polyoxyethylene ether acrylate, N-alkyl acrylamide, acrylamide, and 2-acrylamido-2-methylpropanesulfonic acid. The structures were identified using nuclear magnetic hydrogen spectroscopy. We investigated the effects of the hydrophilic group ethoxy-chain length (n = 14, 16, 18, 20 and 22) and hydrophobic group alkyl-chain length (x = 12, 14, 16, and 18) on the salt resistance, interfacial activity, wettability, interfacial viscoelasticity and emulsification viscosity reduction performance of the viscosity reducers. Futher, the structures of the viscosity reducers which can adapt to different salinities were determined. Based on the emulsification viscosity reduction effect, PAN14A16, PAN12A16, PAN12A18, PAN12A20/PAN16A16 and PAN12A22/PAN18A16 adapted to salinities range of 2 × 104-6 × 104, 4 × 104-8 × 104, 6 × 104-10 × 104, 8 × 104-12 × 104 and 10 × 104-14 × 104 mg/L, respectively. At the optimal salinity, PANxAn reduced the oil–water interfacial tension from 28.3 to 5.0 mN/m, dynamic contact angle from 130.0° to 61.0°, and heavy oil viscosity from 454.6 to 60.0 mPa·s with a viscosity reduction rate of 86.80 %. The synthesis of salt-resistant PANxAn provides theoretical guidance and application value for enhancing the recovery efficiency of viscosity-reducer flooding in high-salinity heavy oil reservoirs.

Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

Effects of high-quality elevation data and explanatory variables on the accuracy of flood inundation mapping via Height Above Nearest Drainage

Abstract. Given the availability of high-quality and high-spatial-resolution digital elevation maps (DEMs) from the United States Geological Survey's 3D Elevation Program (3DEP), derived mostly from light detection and ranging (lidar) sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIM extents at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution (NHDPlusHR) program. Additionally, we varied the spatial resolution of the 3DEP DEMs at 3, 5, 10, 15, and 20 m (meters), and the results showed no significant overall effect on FIM extent quality across resolutions. However, further analysis at coarser resolutions of 60 and 90 m revealed a significant degradation in FIM skill, highlighting the limitations of using extremely coarse-resolution DEMs. Our experiments demonstrated a significant burden in terms of the computational time required to produce HAND and related data at finer resolutions. We fit a multiple linear regression model to help explain catchment-scale variations in the four metrics employed and found that the lack of reservoir flooding or inundation upstream of river retention systems was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management (InFRM) Base Level Engineering (BLE)-produced FIM extents and streamflows at the 100- and 500-year event magnitudes in a sub-region in eastern Texas.

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Effects of Sulfate Ions on Crude Oil Adsorption/Desorption on Carbonate Rocks: Experimental and Molecular Simulations.

In the background of the strong oil wettability and low production by water flooding in carbonate reservoirs, low-salinity water containing sulfate ions can significantly change the surface wettability of carbonate rocks and thus increase the sweeping area; however, the absorption and desorption mechanisms of the oil film in the carbonate rock surface remain unclear. This paper analyzed the wettability alternation of carbonate rocks' surface in pure water and sodium sulfate solution. At the same time, MD (Materials Studio) software was used to simulate the formation process of the oil film and the effect of sulfate ions on the desorption of the oil film on the surface of carbonate rocks. The experimental results showed that sodium sulfate solution could accelerate the rate from oil-wet to water-wet and the final contact angle (49°) was smaller than that in pure water. The simulation results showed that dodecane molecules moved to the surface of calcite to form a double layer of the oil film and that the oil film near the calcite surface had a high-density stable structure under the van der Waals and electrostatic action. The hydrating sulfate ions above the oil film broke through the double oil film to form a water channel mainly under the action of electrostatic force and a hydrogen bond and then adsorbed on the calcite surface. A large number of water molecules moved down the water channel based on a strong hydrogen bonding force and crowded out the oil molecules on the surface of the calcite, resulting in the oil film detachment. This work aims to explain the interaction of oil molecules, water molecules, and SO42- ions at the molecular scale and guide the practical application of low-salinity water flooding in carbonate reservoirs.

Analysis of seepage failure probability for high core rockfill dams during rapid drawdown of reservoir water level

Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions.

Channel-forming discharge based on the extreme value identification of sediment-carrying capacity index

Channel-forming discharge (Dcf) is an important parameter in river management and reservoir flood regulation. Applying the methods for calculating Dcf to reaches downstream reservoirs characterized by drastic changes in water and sediment conditions and long-term scouring status is difficult. Based on the riverbed-shaping principle of sediment-laden water flow, while simultaneously considering the active action of water flow and response of the riverbed, this study proposes a new method for calculating Dcf by identifying the extreme value of the suspended sediment-carrying capacity index. The application of this method to the middle and lower reaches of the Yangtze River showed that after the impoundment of the Three Gorges Reservoir, Dcf in this section was reduced by an amplitude between 2500 and 4700 m3/s. The results can be used to guide the operation of the Three Gorges Reservoir and the management of the middle and lower reaches of the Yangtze River, thus providing reference for other river channels downstream of the reservoir.

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO2 flooding for tight oil reservoir development, establishes a numerical model for CO2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

Hydrological Changes and Sediment Dynamics in the Inner Mongolia Section of the Yellow River: Implications for Reservoir Management

The Inner Mongolia section of the Yellow River is a primary alluvial segment of the main channel. The variations in water and sediment not only alter the cross-sectional morphology and flow capacity of the river but also impact the scheduling of upstream cascade reservoirs. Based on runoff and sediment load data and topographic information from typical hydrological stations, the characteristics of runoff and sediment load variations and the evolutionary pattern of siltation in the Inner Mongolia River section were analyzed via trend analysis methods, Mann–Kendall test methods, the sediment load transport rate method, and the water level–flow relationship. The results showed that the water and sediment loads at the hydrological stations in the Inner Mongolia River section significantly changed from the 1960s to after 2000, with runoff decreasing by approximately 22% to 32% and the sediment load decreasing by approximately 65% to 73%. Sedimentation in the river section generally increased, and the average annual siltation amount reached 0.144 billion t. The joint utilization of the Longyangxia and Liujiaxia reservoirs in 1987 was the main reason for the rapid increase in siltation, and siltation in the Inner Mongolia River section was slightly reduced after 2005. In addition, the critical sediment load coefficients of the Bayangaole–Sanhuhekou and Sanhuhekou–Toudaoguai River sections were 0.0073 and 0.0051 kg·s/m6, respectively, from 1952 to 1968, and 0.0053 and 0.0037 kg·s/m6, respectively, from 1969 to 2020. This study could provide technical support for river flood control and reservoir water sediment regulation in Inner Mongolia.