Search

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

The development of coalbed methane (CBM) in China is susceptible to the influence of microstructure. Therefore, it is crucial to understand the extension laws of hydraulic fractures and engineering responses in coal reservoirs affected by microstructure development. Utilizing the Dahebian block in western Guizhou as the study area, this investigation examines the coal reservoir characteristics, fracturing, and drainage engineering analysis of the well DC1 group in the region. The aim is to discuss the spatial distribution characteristics of hydraulic fractures, geological controlling factors influenced by microstructure, and their corresponding engineering responses. The results indicate that, for coal reservoirs unaffected by microstructure, the extension laws of the fracture network in both longitudinal and planar directions are influenced by burial depth and the regional stress field. In the microstructural belt, tectonic stress dominates, causing changes in the ground stress field. Consequently, the hydraulic fracture network deviates from the direction of the maximum principal stress during the extension process. When a secondary fracture is nearby, the hydraulic fracture network extends towards the shortest path radial secondary fracture direction, leading to a rapid increase in fracture width per unit length until it intersects with the secondary fracture. Additionally, the presence of secondary joints near the microfault structure decreases fracturing pressure and results in a dense distribution of the fracture network. This promotes the formation of a complex fracture network, favorable for fracturing. The extension of the fracture network in complex structural development areas is influenced by the microfault structure between wells, which is reflected in the fracturing construction pressure and fluid output. This accounts for the significant variations in the early drainage performance of CBM wells. [ABSTRACT FROM AUTHOR]

To solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention. [ABSTRACT FROM AUTHOR]

Energy supply policy quality is an important factor that would impact national energy security. While existing research conducted policy evaluation from the lens of performance assessment, less study has been devoted to evaluating energy supply policy from the perspective of policy making. This study takes the energy supply policy documents issued by China's central government during the '13th Five‐Year Plan' period (2016–2020) as the research sample, and pioneers the use of the extended policy modelling consistency (PMC) index model combined with the text mining methodology to construct a policy evaluation index system with the characteristics of the energy supply policy which conducts a more in‐depth quantitative evaluation of the energy supply policy documents. The results show that the average PMC index value of China's energy supply policies is 7.26, and the overall quality is high. The concerns of existing policies are mainly focused on reform and development planning, safety production management and project engineering construction, but there is still room for improvement in policy predictability, coordination of policy areas, clarity of policy basis and goals and comprehensiveness of policy tool combinations. Based on this, China should improve the comprehensiveness of its energy supply policy tool combinations in terms of policy design norm; strengthen policy predictability and coordination of policy areas in terms of policy implementation guarantee; and clarify the basis and goals of policies in terms of policy orientation enhancement, to provide a reference basis for the formulation and improvement of future energy supply policies and to realise the sustainable development of energy supply. [ABSTRACT FROM AUTHOR]