## 5 Results and Discussion
### Fluid Replacement Modelling and AVO Attributes Extraction  
Fluid replacement modelling needs to be done to analyze the AVO attributes response through fluid changes. [PERSON]'s equation for fluid substitution is used to perform fluid replacement modelling where the initial fluid was 100% water into two final fluid situations, 80% oil, 20% water, and 80% gas, 20% water (see Figure 2).  
After the FRM model is created, the AVO attributes extraction can be conducted. The process started by calculating the intercept and gradient using Equation (2). From there, several AVO attributes can be derived, such as: product, sign of product, fluid factor, scaled Poisson ratio change, shear reflectivity, far minus near times far, SQp, and SQs (see Figure 3).  
Figure 2: Fluid replacement modelling result on synthetic wells.  
From the figure above, there is an attribute that could not differentiate between brine and hydrocarbons well: shear reflectivity. There are also several AVO attributes that can differentiate hydrocarbons very well such as: SQp, SQs, fluid factor, product. From this workflow feature, selection can be started by analyzing which AVO attributes will be appropriate to delineate hydrocarbon visually. For example, shear reflectivity can be excluded for further analysis.  
Features Selection and Anomaly Detection Using Self-Organizing Maps at Well Data with Different Fluid Conditions  
In this part, self-organizing maps (SOM) is used to conduct features selection to select the best AVO attributes for hydrocarbon delineation and anomaly detection to determine at which depth the hydrocarbon is identified. Feature selection is completed by analyzing the 2D distribution maps for each feature generated from the SOM algorithm (see Figure 4).  
Figure 4: Feature map generated by SOM in oil & gas situation dataset.  
Figure 3: Several AVO attributes extracted from well log data.  
From the Figure 4, a distribution map is generated for each feature in the datasets. Each SOM feature map visualizes the area where the corresponding variables show high or low values. The similarities between the feature maps indicate that the features are strongly dependent on each other [21]. For example, the features map of SQp and intercept present the same distribution map. Therefore, having both to be used as inputs is redundant. Heatmap correlation is used to analyze the correlation between the feature maps quantitatively (see Figure 5). It is demonstrated by the correlation coefficient of the SOM feature map in Figure 5 that SQp and intercept have a high correlation between them.  
Hence, more features selection methods need to be conducted. In this research, the mean-median score is being applied due to its efficiency and being faster than embedded methods [20]. In this study, the mean-median score between the intercept and SQp attributes were calculated in two different fluid conditions and the highest score will be appointed as selected features (see Figure 6).  
From Figure 6, it can be determined that the mean-median score of SQp in the oil and gas condition is higher than the intercept since the main objective of this research to delineate hydrocarbons. Therefore, SQp is selected as a tool for the further analysis.