## 4 Dataset and Methodology
### Methodology  
Fluid replacement modelling was used to prepare the dataset for hydrocarbon delineation simulation. A model's performance on various hydrocarbon fluid conditions must be evaluated. Analyze the performance of each AVO attribute to differentiate hydrocarbon later. [PERSON]'s equation for fluid substitution is used to describe fluid replacement if the ultimate fluid state is 80/20 oil/water or 80/20 gas/water where the initial fluid condition is 100% water. To extract AVO attributes from datasets created using fluid replacement modelling, click here. First, use Equation (1) to determine intercept and gradient using P-wave velocity log (Vp log), S-wave velocity log (Vs log), and density log. Other AVO properties can be generated using Intercept and Gradient after they have been constructed. There are also SQp and SQs. First, we need to generate near and far reflectivity using Equation (1), with near at 10\({}^{\circ}\) and far at 35\({}^{\circ}\). This allows us to calculate FNLY characteristics using Equation (12). Finally, the AVO attributes must be wavelet convolved to simulate seismic conditions. Convolution is performed using ricker wavelet in this study. The next step is to analyze feature selection. Prior to hydrocarbon delineation, selecting relevant AVO features is an important process. This step reduces the number of attributes that offer the same information to the anomaly detection process. The model can produce a 2D features map for each attribute using self-organizing maps. The feature map shows where the associated variables are high or low. The similarity of the feature maps indicates substantial interdependence [21]. To measure it quantitatively, a heatmap correlation analysis measures the correlation between produced attributes. The higher the correlation, the more redundant the traits are. The mean-median approach is used to choose the best features for further study. Mean-median is used to examine attribute dispersion. That is,the higher the dispersion, the better the abnormality may be identified. The attributes with the biggest dispersion will be used for further study, while the other can be omitted to reduce computational time on delineating hydrocarbon. After selecting the attributes, hydrocarbon delineation is performed. Before applying the model to seismic scale, the analysis is performed at well log scale. In this case, self-organizing maps are used. The presence of hydrocarbon discovered by AVO characteristics is considered anomalous in this study. Quantization error is used as a statistical measure to discover anomalies using self-organizing maps. Quantization error is the distance between data samples and its best matching units. Meaning the quantization error increases as the distance between a data sample and the dataset's overall distribution increase, causing it to be flagged as anomalous. The hydrocarbon delineation using self-organizing maps began with the training datasets. The datasets must be standardized before use to ensure that each attribute contributes equally to identifying hydrocarbon. Then the self-organizing maps model training can begin. It calculates quantization error for each data sample. Next, an error threshold is specified, and any data sample with a quantization error greater than the threshold is considered an anomaly. For this purpose, local minima of the quantization error distribution are computed. In this research, the model recognizing the data sample is an anomaly or a hydrocarbon. Because this is unsupervised learning, it is difficult to analyze model performance since there is no label to train the model. Instead, for this research, water saturation log is utilized to examine model performance to detect hydrocarbons. Those data samples with water saturation values less than 0.5 are labelled as hydrocarbons, while those with water saturation values equal to or more than 0.5 are labelled as not Hydrocarbons. If the outcome is satisfactory, the analysis can be moved to real seismic data.