# Constructing of 3D Fluvial Reservoir Model Based on 2D Training Images
## 2 Three-Dimensional Condition Simulation of Fluvial Facies Reservoir  
Taking the fluvial reservoir of the P oilfield in Bohai Bay as an example, this working area is located in the south of Bohai Bay, about 216.0 km away from Tanggu of Tianjin and about 80.0 km away from Penglai City of Shandong, as shown in Figure 2. The formation is located in Neogene Minghuazhen Formation, with 12 wells in the working area. Figure 2: Geographical location map of the study area. According to the types and distribution characteristics of sedimentary microfacies in the study area in Table 1, a three-dimensional training image of the work area was established. In order to simplify the simulation process, rock facies except sand were divided into the mud for simulation. Based on the 3D training image of the reservoir in the working area, the well data are imported, and the 3D model is established using the method proposed in this paper based on three mutually perpendicular 2D training images. At the same time, in order to compare with other methods, Snesim [18], a relatively mature multipoint geostatistical modeling method, will be used to build a three-dimensional model with three-dimensional training images, and the DS method will be used to build a three-dimensional model with two-dimensional training images. Comparative analysis will be conducted from three aspects, namely, variation function, sand body connectivity, and style similarity. The specific operation steps of the Snesim algorithm are as follows: (1) Input the training image and the point to be estimated and select the appropriate data template. (2) Scan the training image with the data template. Data events that meet the data template are classified and stored to build a search tree. (3) Conditional data with known attributes are assigned to the nearest area to be estimated. (4) A random simulation path is defined. (5) Data events with the same geometric configuration as the estimated point are found in the search tree. The properties of the points to be estimated are randomly determined from the conditional probability distribution function (6), and the points to be estimated are added to the subsequent simulation as conditional data (7) to simulate the next point to be estimated along the simulation path. Repeat (4), (5), and (6) until all the attributes of the points to be estimated are obtained. Using the target-based algorithm, parameters such as model mesh size, phase type, channel wavelength amplitude, and width were set, and a fluvial facies model with a mesh size of 100 \(\times\) 100 \(\times\) 50 was generated as a 3D training image (Figure 3a). Slices along x, y, and z in three different directions are obtained from the 3D model of the work area as 2D training images, as shown in Figure 3. The slice mesh sizes in the x-, y-, and z-directions were 100 \(\times\) 100, 100 \(\times\) 50, and 100 \(\times\) 50, respectively (Figure 3b-d). The gray value is 0, indicating the background mud; a yellow value of 1 indicates channel sand. The well data distribution is shown in Figure 4.  
Using 2D channel training images in three directions in Figure 3b-d and the conditional data obtained in Figure 4, the maximum scanning score was set at 0.3, and the grid size of the output model was 100 \(\times\) 100 \(\times\) 30 to generate three-dimensional models, grid models, and hollowed-out models, as shown in Figure 5. As can be seen from the grid diagram and hollow diagram of the model, the geometric morphology and spatial structure of the reconstructed model are better, and the visual results show that the three-dimensional model can be generated in accordance with the geological understanding by using two-dimensional slices.