## 1 Introduction  
The oil and gas discovered in the South Turgay Basin are mainly located in the Aryskum Depression in the south, across an area of about \(3\times 10^{4}\) km\({}^{2}\). The main sedimentary strata in the Aryskum Depression are Jurassic-Cretaceous fluvial sandstone and mudstone, the main source rocks are Middle-Lower Jurassic dark mudstone, and the main reservoir rocks are Mesozoic sandstone and buried hill. The entire basin contains four sets of reservoir-forming assemblages: K\({}_{1}\), J\({}_{3}\), J\({}_{1-2}\), and P\({}_{\mathrm{z}}\). At present, the main development strata are J\({}_{3}\) and K\({}_{1}\). Since the basin has undergone more than 40 years of exploration, the basic drilling of large-scale structural traps is nearly at an end. Therefore, searching for stratigraphic lithologic traps has become a new target for increasing reserves. Particularly in recent years, the Aryskum Depression has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research, and lithologic stratigraphic reservoirs have gradually become some of the main focuses of oil and gas exploration. The determination of sedimentary facies plays an important role in the exploration of lithologic reservoirs, especially in the prediction of residual oil production. In addition, coring and logging are seldom carried out in development wells. Therefore, increasing attention has been paid to the method of studying sedimentary facies by logging curves. However, the process of manual identification often incurs many problems, such as an intense workload of data statistics and mapping, low work efficiency, poor quantification, and non-uniformity of the criteria for manual identification of sedimentary facies in uncored wells. These problems highlight that there are many shortcomings in the artificial identification of multi-well and multi-layer logging sedimentary facies. With the popularization of digital logging technology, digital logging curve technology, and computer technology, the machine learning method in particular has the advantages of dealing with non-linear mapping relations. In recent years, machine learning has been preliminarily applied in processing logging data and predicting reservoir pore and permeability properties. International geologists are currently conducting research on the identification of sedimentary facies by machine learning. This mainly includes the KNN (k-nearest neighbor) [1], ANN (artificial neural network) [2; 3; 4; 5], Bayesian [6], fuzzy clustering [7; 8], and support vector machine (SVM) algorithms [9; 10; 11]. However, some of these machine learning algorithms have limitations and, in many cases, may not meet the requirements for the identification of sedimentary facies [12; 13]. KNN requires a large amount of computation and there is an issue of sample imbalance; that is, the number of samples in some categories is large, while the number in other categories is very small. Although the artificial neural network algorithm has a strong ability to deal with the problem of non-linear classification, it is easy to cause model over-fitting due to its strong non-linear ability, and it requires a large number of parameters. Besides, as the learning process cannot be observed, the output is difficult to understand [14]. When using a Bayesian algorithm, it is necessary to assume that the experimental samples are distributed independently, and the assumptions of the independent distribution should be known first [15]. Fuzzy clustering analysis is sensitive to noise and outliers. Additionally, its results are unstable [16]. The theory of SVM was first proposed by [PERSON] in 1995 [17]. SVM is a machine learning algorithm that relies on statistical theory and Vapnik-Chervonenkis (VC) dimension theory, and is based on the structural risk minimization principle, which is to seek the best balance between the complexity of the model and the learning ability of the model through limited sample information.