Tight sandstone reservoirs have small pore throat sizes and complex pore structures. Taking the Chang 6 tight sandstone reservoir in the Huaqing area of the Ordos Basin as an example, based on casting thin sections, nuclear magnetic resonance experiments, and modal analysis of pore size distribution characteristics, the Chang 6 tight sandstone reservoir in the study area can be divided into two types: wide bimodal mode reservoirs and asymmetric bimodal mode reservoirs. Based on the information entropy theory, the concept of “the entropy of microscale pore throats” is proposed to characterize the microscale pore throat differentiation of different reservoirs, and its influence on the distribution of movable fluid is discussed. There were significant differences in the entropy of the pore throat radius at different scales, which were mainly shown as follows: the entropy of the pore throat radius of 0.01~0.1 μm, >0.1 μm, and <0.01 μm decreased successively; that is, the complexity of the pore throat structure decreased successively. The correlation between the number of movable fluid occurrences on different scales of pore throats and the entropy of microscale pore throats in different reservoirs is also different, which is mainly shown as follows: in the intervals of >0.1 μm and 0.01~0.1 μm, the positive correlation between the occurrence quantity of movable fluid in the wide bimodal mode reservoir is better than that in the asymmetric bimodal mode reservoir. However, there was a negative correlation between the entropy of the pore throat radius and the number of fluid occurrences in the two types of reservoirs in the pore throat radius of <0.01 μm. Therefore, pore throats of >0.1 μm and 0.01~0.1 μm play a controlling role in studying the complexity of the microscopic pore throat structure and the distribution of movable fluid in the Chang 6 tight sandstone reservoir. The above results deepen the understanding of the pore throat structure of tight sandstone reservoirs and present guiding significance for classification evaluation, quantitative characterization, and efficient development of tight sandstone reservoirs.
Abstract:The power graph of a finite group is the graph whose vertex set is the group, two distinct elements being adjacent if one is a power of the other. The enhanced power graph of a finite group is the graph whose vertex set consists of all elements of the group, in which two vertices are adjacent if they generate a cyclic subgroup. In this paper, we give a complete description of finite groups with enhanced power graphs admitting a perfect code. In addition, we describe all groups in the following two classes of finite groups: the class of groups with power graphs admitting a total perfect code, and the class of groups with enhanced power graphs admitting a total perfect code. Furthermore, we characterize several families of finite groups with power graphs admitting a perfect code, and several other families of finite groups with power graphs which do not admit perfect codes.
As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE, R 2 , and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.
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