## 5 Verification  
Based on the outcomes of the rock physics experiments, we employed the Waxman-Smits (W-S) model to compute the water saturation, incorporating the irreducible water saturation to assess the immobile fluids. This led to a reevaluation of the wells in the study area [25]. Due to the existence of a high immobile water saturation, the resistivity difference of the formation was small, but it had a great influence on the reservoir fluid production and its fluid evaluation, so carrying out the evaluation model of bound water saturation is of great significance for recognizing the reservoir and fluid identification. Bound water is mainly caused by fine lithology and a high mud content, which overall reflects the magnitude of permeability, so this study used a binary regression model with a single correlation analysis using the permeability (Figure 14) and quality factor (Figure 15) subsets, and a computational model. Figure 14: Monocorrelation analysis of bound water saturation and permeability. Figure 13: The relationship between the resistivity index and the irreducible water saturation. Using binary regression, the fitted bound water saturation was calculated as follows:  
\[Sw_{i}=-0.8761\times K^{-0.077}+1.5945\times Q^{-0.185} \tag{7}\]  
where \(Sw_{i}\) is the bound water saturation; \(K\) is the permeability in md; and \(Q\) is the quality factor, calculated as follows:  
\[Q=\sqrt{\frac{K}{\phi}} \tag{8}\]  
where \(\phi\) is the porosity. There is good agreement between the calculated and measured values of bound water saturation (Figure 16). For the fluid identification of effective reservoirs, several synthetic curves reflecting the reservoir quality and fluid saturation are first defined here (Table 2). Considering the influence of four factors, namely the reservoir thickness, reservoir water saturation, reservoir bound water saturation, and reservoir porosity, on the reservoir's oil-production capacity, and using the reservoir fluid production coefficient \(P_{f}\) and reservoir  
Figure 16: Analysis of measured values of bound water saturation and relationship with calculated values. Figure 15: Monocorrelation scores between bound water saturation and quality factors. oil production \(P_{o}\) coefficient to draw rendezvous diagrams and compare them with the test oil data (Table 3), the conformity rate of the interpreted results to the conclusions of the test oil reached 82.05%. Finally, a method of reservoir oil-water division with the output oil saturation \(PS_{o}\) as the standard was obtained [26]. Using well XXX03 as an illustration, Figure 17 depicts that the resistivity of reservoirs 36 and 37 was approximately 10 ohm.m. The well-testing data revealed a daily oil production of 3.638 t and a daily water production of 0.85 m\({}^{3}\) for this well, leading to the conclusion of a low-water-producing industrial oil layer that exhibited low-resistivity characteristics. The initial interpretation categorized it as a water reservoir. The application of the W-S model in the computation produced the results presented in Table 4. In Reservoir 36, the water was predominantly irreducible water, interpreted as an oil layer. For Reservoir 37, movable water constituted 8%, and was interpreted as an oil-water coexisting layer. The interpretation findings align with the well-testing conclusion. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Formulas** \\ \hline Movable fluid volume fraction, \(V_{mx}\) & \(V_{mx}=\phi\times(1-Sw_{sk})\) \\ Movable fluid oil saturation, \(PS_{o}\) & \(PS_{o}=\dfrac{1-Sw}{1-Sw}\) \\ Reservoir fluid production factor, \(P_{f}\) & \(P_{f}=D\times\phi\times(1-Sw_{sk})\) \\ Reservoir oil production factor, \(P_{o}\) & \(P_{o}=D\times\phi\times(1-Sw)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Defining parameters and calculating formulas. Figure 17: Interpretation results of Well XXX03.