## 1 Introduction  
Reservoir geological models are capable of quantitatively describing the spatial distribution and change trends of reservoirs and their attribute parameters, thereby providing an important basis for reservoir evaluation, numerical simulation, and exploitation management [1]. High-quality reservoirs are distributed in one or more sedimentary microfacies, and the attribute parameters contained in different sedimentary microfacies tend to show different distribution trends; therefore, a good reservoir model needs to reproduce the complex geological patterns that characterize the study area [2; 3]. For areas with shallow exploration, the acquisition of observation data is limited. However, most of the reservoir units exhibit strong nonstationarity and heterogeneity, and the corresponding geometric and tectonic patterns are complex. With limited prior data, traditional modeling methods struggle to achieve the fine characterization of geological bodies, resulting in inaccurate reconstructions [4]. Thus, achieving the efficient and automatic construction of reservoir models using limited geological data and combining certain expert knowledge is the current research hotspot in the field of geological information. Multiple-point geostatistics (MPS), as a typical stochastic simulation method, has been widely applied in the field of reservoir modeling [5]. With [PERSON] [6] proposing the SNESIM algorithm based on a search tree storage structure, the MPS became one of the mainstream methods widely used in both reservoir modeling and hydrogeological simulation. The core of this method involves scanning the training image (TI) through a data template, which contains rich prior geological knowledge. This process extracts the higher-order statistical relationships of the regionalized variables, enabling the reproduction of the geometric forms and attribute information of the target geological body within the simulation grid [7]. Prominent MPS methods include the SIMPAT algorithm, which is based on pattern classification, the DS algorithm, which directly captures similar data events through data templates, and the CCSIM algorithm based on the cross-correlation function [8; 9; 10]. However, the MPS has some limitations that can affect its effectiveness in simulating complex geological features. First, the MPS relies on the multi-point attribute information of local regions in training images to express the corresponding prior geological knowledge, but it lacks consideration for global spatial patterns. This can result in low connectivity in simulation results, especially when structures are complex or when well data are dense. Additionally, MPS typically assumes stationarity, which causes simulations to reproduce only the repeated patterns in TIs, leading to a lack of diversity and difficulty in capturing different spatial patterns [11]. Being nonstationary is a common characteristic of most reservoir geological bodies, and although MPS can incorporate soft data constraints and zoning strategies to address this, the process is complicated and inefficient, making it challenging to accurately reconstruct large-scale, intricate geological bodies in a short time. Moreover, the MPS is computationally intensive, and the large number of parameters generated during the simulation are typically discarded at the end of the process, necessitating their recalculation during re-simulation, which further increases the computational burden [12]. The powerful extraction of deep learning to extract complex features offers significant potential for advancements in geological modeling. Since reservoir modeling is typically a generative problem, it involves training a model to reproduce a probability distribution given the limited amount of prior observational data [13]. Consequently, deep learning can be combined with the reservoir modeling process to achieve the efficient and automatic construction of complex reservoir geological bodies.