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.

Tight sandstone reservoirs have the characteristics of poor physical properties, fine pore throats, and strong microheterogeneity compared with conventional reservoirs, which results in complicated movable fluid occurrence laws and difficult mining. Taking the tight sandstone gas reservoir of He 8 formation in Sulige gas field as an example, based on physical property test analysis, constant velocity mercury injection, and nuclear magnetic resonance experiments, an optimized gray correlation calculation model is established by improved gray correlation theory, which quantitatively characterizes the influence of microscopic pore structure parameters of different types of tight sandstone gas reservoirs on the occurrence of movable fluids, and the main controlling microgeological factors for the occurrence of movable fluid in tight sandstone gas reservoirs with close/similar physical properties are selected. The results show that the occurrence of movable fluid in Type I reservoirs is mainly affected by the effective pore-throat radius ratio, the saturation of mercury in the total throat, and the effective pore radius, and the occurrence of movable fluid in Type II reservoirs is mainly affected by the effective throat radius per unit volume and total throat mercury saturation and mainstream throat radius. Moreover, the occurrence state of movable fluids in Type II reservoirs is controlled by the throat radius stronger than that of Type I reservoirs. It has important guiding significance for the efficient development of tight sandstone gas reservoirs.

Nanoscale pore-throat systems are widely developed in the pore-throat of tight reservoirs. The pore-throat structures of different microscales are complex and diverse with obvious microscale effects. Taking the Chang 6₃ tight sandstone reservoir of the Huaqing area in Ordos basin as an example, under the guidance of information entropy theory, the quantitative characterization model of pore-throat micro-scale heterogeneity in a tight oil reservoir is established based on casting thin sections, physical properties analysis, constant velocity mercury injection, and NMR technology. Moreover, the correlation between pore-throat heterogeneity and porosity, permeability and movable fluid saturation is analyzed. The results show that there are obvious differences in pore-throat heterogeneity between different reservoirs, and the throat uniformity of macro pore-fine-throat reservoir, macro pore–micro throat reservoir, and macro pore–micro throat reservoir becomes worse, successively. There is a negative correlation between porosity uniformity and porosity, permeability and movable fluid saturation. However, there is a positive correlation between throat uniformity and combined pore throat uniformity and porosity, permeability and movable fluid saturation. Therefore, the uniformity of the throat controls the seepage capacity and fluid mobility in the pore system of the Chang 6₃ tight sandstone reservoir in the study area. This has important theoretical and practical significance to enhance oil recovery and promote the efficient development of a tight oil and gas reservoir.

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 0.010.1μm, 0.1μm, and 0.01μm 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 0.1μm and 0.010.1μm, 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 0.01μm. Therefore, pore throats of 0.1μm and 0.010.1μm 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.

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, , 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.

It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, there are fewer experimental samples available, and data collected from relevant research are thus worth paying attention to. In this study, based on the experimental results of high-pressure mercury injection of 25 rock samples from Chang 6 reservoir in the Wuqi area, Lasso dimensionality reduction was used to reduce the dimensionality of 14 characteristic parameters to 6, which characterize the microscopic pore structure, while a combination of different kernel functions was used to construct the multi-kernel function of the multi-kernel model to be determined. A multi-kernel support vector machine (MK-SVM) model was established for unsupervised learning of microscopic pore structure characteristic parameters that affect reservoir quality. By optimizing the hyperparameters of the model and obtaining the optimal decision function, the tight sandstone reservoirs were classified into classes I, II and III, and the classification results were verified. The results show that the accuracy of the proposed reservoir classification method is as high as 86%, which can effectively reduce the time loss and save labor costs. It provides an effective method for the comprehensive evaluation of tight sandstone reservoirs.