# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 1 Introduction  
With the expansion of unconventional oil and gas exploration and development in recent years, shale oil has shown broad energy prospects and development trends [1]. Shale oil in China mainly comes from land-phase basins, with the Songliao Basin currently being a hotspot for land-phase shale oil exploration. Thick layers of dark mud shale are developed in the basin, which has the advantages of wide distribution, large reserves, and high hydrocarbon potential. The Cretaceous Qingshankou Formation shale reservoir of the Gulong Sag, which formed during the subsidence period of the Central Depression's lake basin development, is currently the shale oil production layer with the most potential in the Songliao Basin [2, 3]. Terrestrial shale oil belongs to petroleum resources aggregated within the source, and the development of shale rich in total organic carbon (TOC) content is the material basis for shale oil formation [4]. Organic matter in the Qingshankou Formation is mainly type I, and its source is mostly lacustrine stratiform algae. A total organic carbon content greater than 2% is the predicted limit of effective hydrocarbon source rock for shale oil. Therefore, the Qing 1 member can be considered a potential shale oil formation section with good oil-bearing characteristics. By contrast, the Qing 2 and Qing 3 membershave hydrocarbon source rock conditions only in layers with high organic carbon content, presenting localized oil-bearing characteristics. Meanwhile, when the burial depth is shallow and the vitrinite reflectance is generally less than 0.75%, the organic matter is in the immature to low-mature stage, the hydrocarbon generation of organic matter is minimal, and the oil content in the shale is low. As the burial depth gradually increases, the vitrinite reflectance reaches 0.75-1.30%, the organic matter is in the mature stage, and a large amount of hydrocarbon is discharged. With this increase in hydrocarbon discharge, the oil content in the mud shale gradually increases [5; 6; 7]. In general, the depositional environment of the Qingshankou Formation provides good geological conditions for the preservation and aggregation of its organic matter. The TOC content reflects the amount of organic matter and hydrocarbon potential in shale and is a key parameter for shale reservoir evaluation [8]. Therefore, the accurate acquisition of TOC content is indispensable in the exploration and development of shale oil. Recently, geochemical methods (e.g., Rock-Eval pyrolysis) have been used to directly determine the TOC content in coal and shale. However, in practical applications, there have been disadvantages, such as the misinterpretation of programmed pyrolysis results, the high cost of coring, and time-consuming experimental testing. Therefore, the effective use of existing data to construct predictive models has become an important means of acquisition [9; 10]. Many scholars have used logging data to predict TOC content, which is mainly divided into conventional methods (\(\Delta\)logR method, multiple regression method, etc.) and machine learning methods [11]. Due to the complex relationship between TOC content and logging responses, machine learning methods have been widely applied in different stratigraphic and depositional environments compared to conventional methods [12]. When using machine learning methods to predict TOC content, multiple algorithms are usually established to compare and analyze the prediction results and to select the best prediction method for the study area. [PERSON] et al. [13] established back-propagation neural networks and support vector machine prediction models by collating logging data and laboratory core analysis results after selecting logging parameters, such as natural gamma and density, as input features. Finally, the back-propagation neural network was determined to be the optimal prediction model in their comparative analysis. [PERSON] et al.