Total organic carbon (TOC), a critical geochemical parameter of organic shale reservoirs, can be used to evaluate the hydrocarbon potential of source rocks. However, getting TOC through core analysis of geochemical experiments is costly and time-consuming. Therefore, in this paper, a TOC prediction model was built by combining the data from a case study in the Ordos Basin, China and core analysis with artificial intelligence techniques. In the study, the data of samples were optimized based on annealing algorithm (SA) and genetic algorithm (GA), named SAGA-FCM method. Then, back propagation algorithm (BPNN), least square support vector machine (LSSVM), and least square support vector machine based on particle swarm optimization algorithm (PSO-LSSVM) were built based on the data from optimization. The results show that the intelligence model constructed based on core samples data after optimization has much better performance in both training and validation accuracy than the model constructed based on original data. In addition, R² and MRSE in PSO-LSSVM are 0.9451 and 1.1883, respectively, which proves that models established with optimal dataset of core samples have higher accuracy. This study shows that the quality of sample data affects the prediction of the intelligence model dramatically and the PSO-LSSVM model can present the relationship between well log data and TOC; thus, PSO-LSSVM is a powerful tool to estimate TOC.

Porosity is an important parameter for the oil and gas storage, which reflects the geological characteristics of different historical periods. The logging parameters obtained from deep to shallow strata show the stratigraphic sedimentary characteristics in different geological periods, so there is a strong nonlinear mapping relationship between porosity and logging parameters. It is very important to make full use of logging parameters to predict the shale content and porosity of the reservoir for precise reservoir description. Deep neural network technology has strong data structure mining ability and has been applied to shale content prediction in recent years. In fact, the gated recurrent unit (GRU) neural network has further advantage in processing serialized data. Therefore, this study proposes a method to predict porosity by combining multiple logging parameters based on the GRU neural network. Firstly, the correlation measurement method based on Copula function is used to select the logging parameters most relevant to porosity parameters. Then, the GRU neural network is used to identify the nonlinear mapping relationship between logging data and porosity parameters. The application results in an exploration area of the Ordos basin show that this method is superior to multiple regression analysis and recurrent neural network method, which indicates that the GRU neural network is more effective in predicting a series of reservoir parameters such as porosity.

Since Chinas reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO₂) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO₂ emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO₂ emissions, this study establishes a hybrid intelligent algorithm model suitable for CO₂ emissions prediction based on Chinas CO₂ emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5 in the advance prediction of CO₂ emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.

As for estimating the cost and planning the process of the rock sawing plants, it is significant to predict the production rate of ornamental stones sawing. To promote the efficiency in planning these rock sawing projects, scholars have been trying to find a high‐accuracy method of production rate estimation. Moreover, targeting at the 28 granite and carbonates stone in the nature, this study examined the connection between two various brittleness indexes in statistics, including the ratio of compressive strength to tensile strength (B₁) and places below the line of compressive strength and the line of tensile strength (B₂) in rocks and production rate had been studied. Through the results of cross plots analysis, it was indicated that there existed a strong connection between production rate and the brittleness B₁ and B₂. Finally, in this thesis, through adding B₁ factor, it has improved the estimation model for production rate which Mikaeil et al. (2013) have established. What’s more, by virtue of brittleness about B₁ and B₂, this production rate estimation model has been established successfully for natural stone sawing. Actually, the way of estimating the production rate of 28 rock samples is to utilize the two kinds of models described before. Through the result, it is showed that the production rate estimated by the improved model corresponds to the value of production rate of rock testing. Meanwhile, the precision has been greatly improved with comparison to the model of estimating the production rate designed by Mikaeil et al. (2013). Thus, on the basis of the new model, a dependable prediction for ornamental stones production is put forward in this paper. And it is required to do a further study involving different rock types since limited rock types were used in this study.