## 5 Establishment of 3D Geological Model
### Analysis of Variation Function
#### 5.2.1 Geological Probability Surface-Constrained Lithofacies Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Sub-** \\ **Layers** \\ \end{tabular} } & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Major Range**} & \multicolumn{3}{c}{**Minor Range**} & \multicolumn{3}{c}{**Vertical Range**} \\ \cline{3-10}  & & **Azimuth/\({}^{c}\)** & **Range/m** & **Nugget/m** & **Azimuth/\({}^{c}\)** & **Range/m** & **Nugget/m** & **Range/m** & **Nugget/m** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Chang \\ \(7_{2}^{2}\) \\ \end{tabular} } & Porosity & \multirow{2}{*}{45} & \multirow{2}{*}{2200} & \multirow{2}{*}{0} & \multirow{2}{*}{315} & \multirow{2}{*}{1750} & \multirow{2}{*}{0} & \multirow{2}{*}{7} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ \(7_{3}\) \\ \end{tabular} } & Porosity & \multirow{2}{*}{50} & \multirow{2}{*}{2600} & \multirow{2}{*}{0} & \multirow{2}{*}{320} & \multirow{2}{*}{2000} & \multirow{2}{*}{0} & \multirow{2}{*}{8} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ \(7_{1}^{2}\) \\ \end{tabular} } & Porseability & \multirow{2}{*}{30} & \multirow{2}{*}{1200} & \multirow{2}{*}{0} & \multirow{2}{*}{300} & \multirow{2}{*}{850} & \multirow{2}{*}{0} & \multirow{2}{*}{4} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ \(7_{1}^{2}\) \\ \end{tabular} } & Permeability & \multirow{2}{*}{35} & \multirow{2}{*}{1450} & \multirow{2}{*}{0} & \multirow{2}{*}{305} & \multirow{2}{*}{1200} & \multirow{2}{*}{0} & \multirow{2}{*}{5} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \multirow{2}{*}{\
\begin{tabular}{c} Chang \\ \(7_{2}^{2}\) \\ \end{tabular} } & Permeability & \multirow{2}{*}{40} & \multirow{2}{*}{1800} & \multirow{2}{*}{0} & \multirow{2}{*}{310} & \multirow{2}{*}{1450} & \multirow{2}{*}{0} & \multirow{2}{*}{6} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \multirow{2}{*}{
\begin{tabular}{c} Chang \\ \(7_{3}\) \\ \end{tabular} } & Permeability & \multirow{2}{*}{45} & \multirow{2}{*}{2100} & \multirow{2}{*}{0} & \multirow{2}{*}{315} & \multirow{2}{*}{1700} & \multirow{2}{*}{0} & \multirow{2}{*}{7} & \multirow{2}{*}{0} \\  & model & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Figure 4: Thickness map of the Chang 7 member of the target formation. Figure 5: Thickness map of sandstone in the Chang 7 member of the target formation. Figure 6: Probability map of sandstone in the Chang 7 member of the target formation. Figure 7: Mudstone thickness map of the Chang 7 member of the target formation. Figure 8: Probability map of mudstone in the Chang 7 member of the target formation. Figure 9: Lithofacies of each small layer in the Chang 7 member of the target formation. Utilizing logging data and prior geological insights to collate trends is a pivotal strategy for modelers to assimilate geological knowledge. However, the complexity and variability of subsurface geological phenomena present significant challenges. Conventional methods, such as variation function fitting and sequential indication of lithology data, often result in a channel sand body that exhibits poor regularity in the physical property changes at its center and edges (as shown in Figure 9). This simulation outcome suggests that the constraint of a single-phase zone boundary is insufficient to capture the distribution characteristics of the physical properties of channel sediments and may not fully represent the objective rationality of the reservoir. The employment of the geological probability surface constraint method offers an enhanced approach. It allows for a more vivid demonstration of the regularity in physical property changes at the center and edges of the sand body (Figure 10). This method more directly reflects the distribution characteristics of channel sediments.