## 2 _Introduction_  
The continental sedimentary basin occupies a big part of the world's exploration and development block, and it has the characteristics of multi-source, rapid phase change,and mixed sediment development. The nonhomogeneity of the reservoir is the main cause of the multi-solution and uncertainty in petroleum geology. Oil and gas exploration and development intelligence has been a hot topic. Many researchers have applied AI technology to logging, physical exploration, drilling and completion, reservoir engineering, CCUS, etc. [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. However, there are still some problems, such as the small sample size, which cannot meet the requirements of deep learning, the difficulty of algorithm exploration, and application [12; 13; 14]. Starting from this century, the superiority of multi-point geostatistics is more and more obvious than the two-point geostatistics, which is based on variation. A lot of researchers have optimized the multi-point geostatistics algorithm, which has been used in many oil fields. Furthermore, the importance of training images as an important source of prior knowledge in multi-point geostatistics is becoming more and more obvious. Training images are one of the key factors to determine the effectiveness of simulation [15]. Training images are two- or three-dimensional data, which can be used to represent the structure of a target simulation model in multi-point geostatistics. Therefore, the training image includes nonhomogeneous geoid features, including geometry, connectivity, spatial distribution, and relation of the geoid [16]. [PERSON] further explains how important it is to build a training image library when using the multi-point geostatistical method. The training image is a quantitative representation of the a priori geological model. It is also proposed that constructing a multi-typed training image library will facilitate the advancement of multidirectional geostatistics based on this training image [17]. [PERSON] mainly explains the workflow and construction of training images in multi-point geostatistics [18]. More and more researchers pay attention to the acquisition of training images in multi-point geostatistics and their actual applications. It is believed that the acquisition of training images is a key factor in the success of multi-point geostatistics modeling [19]. Moreover, to satisfy the smooth hypothesis, it is necessary for the training image to have the following features: the pattern in the entire training image space must be uniformly distributed [20]; there must be repetitive patterns in the training pictures; and the structural characteristics of the sedimentary facies are not confined to a particular position in the training image, but are distributed across the entire training image grid, i.e., they do not need to be faithful to the well point constraints [21]. In recent years, there has been a lot of research on training images, which has led to a variety of methods to generate and evaluate images. The methods for constructing training images mainly include satellite images from modern deposition, theoretical deposition models for specific deposition systems [22; 23; 24; 25], target-based simulation algorithms [26; 27; 28], and deposition process-based methods [29; 30; 31; 32; 33; 34]. However, traditional training image construction methods have many disadvantages. Traditional methods using real-world data cannot reflect complex sedimentary facies and have a single type, making them unsuitable for batch training image generation. Other methods require repeated calibration of simulation parameters, so practical application and modeling are still quite difficult. The traditional training image library is built for a particular type or even a particular reservoir. In order to address the drawbacks of traditional methods, we implemented a method that can rapidly build up the training image library of several typical reservoirs.