# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 5 Discussion  
Under the guidance of geological knowledge, reservoir geological modeling aims to achieve a comprehensive three-dimensional quantification of oil and gas reservoirs by interpolating and extrapolating reservoir characteristics based on known control points. The reliability and accuracy of the model are not only heavily reliant on the chosen geological modeling method but also closely tied to the accuracy and precision of reservoir characterization through geological knowledge, well logging, and seismic data. Reservoir geological modeling has evolved from traditional deterministic modeling, two-point geostatistical modeling, multi-point geostatistical modeling, and depositional process modeling, and has now entered the realm of intelligent geological modeling [72]. Deterministic modeling relies on geological knowledge obtained from sedimentology, reservoir geology, and seismic stratigraphy, using methods such as Kriging interpolation to calculate reservoir parameter distributions between wells [13; 73]. However, the deterministic approach only provides certain results between control points, which may contradict the actual reservoir conditions, leading to limitations in the accuracy. Two-point geostatistical modeling overcomes this limitation by capturing the uncertainty of reservoir parameter predictions resulting from random variations. It enables multiple solutions for reservoir modeling outcomes and allows for the comprehensive analysis of result uncertainties, meeting the needs of risk decision making in oil and gas exploration and development [18; 74]. However, this method may struggle to accurately characterize complex spatial structures and target geometries [75]. In comparison, the multi-point statistical method synthesizes more morphological information, analyzes the statistical characteristics of geological variables using multiple points, and employs a \"training image\" to represent the spatial structure attributes of geological variables. It can effectively depict the shape and spatial distribution characteristics of target bodies, demonstrating advantages in geometric shape characterization and well data conditioning [3]. However, challenges remain, including the requirement for suitable training images, the limited reproduction ability of modes, and slow computation speed. Further research is needed in areas such as simulation target continuity and non-stationary data processing [76]. Geological process-based modeling techniques consider real geological processes as control functions and simulate the spatial distribution of reservoir parameters under these processes. By adjusting control parameters, different simulation results can be obtained to replicate different geological conditions and optimize a scheme that aligns with geological reality, ensuring consistency with geological laws [77; 78]. The integration of artificial intelligence with geological modeling technology, known as intelligent geological modeling, has gained attention. This approach aims to reduce the uncertainties stemming from human subjectivity, enhance the model accuracy, and achieve highly efficient, scientific, and intelligent modeling [79; 80; 81]. However, deep learning-based geological modeling methods require large training samples with a high information content and low redundancy. Thus, acquiring suitable training samples and enhancing the self-learning capabilities are crucial challenges for deep learning-based geological modeling techniques [82; 83]. The heterogeneity of deep and ultra-deep clastic rock reservoirs exhibits distinct structural characteristics, primarily controlled by sedimentary structures. These differences are evident in the rock and mineral composition, structural arrangement, and fluid permeability [25; 84; 85]. While reservoirs with different sedimentary facies in various regions exhibit notable variations in petrology and sedimentary structure, a classification of rock facies types can be applied to each microfacies [86; 87].