## 4 Results and Discussion
### AVO Analysis  
The AVO analysis was analyzed to confirm the hydrocarbon presence in the research area. [PERSON]'s fluid substitution modeling was generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. Based on Figure 10, the gas-sand had the lowest acoustic impedance followed by pure brine sand. The AVO synthetics from each synthetic showed different responses. AVO synthetics of top in situ and pure gas sand showed the strongest trough compare to AVO synthetics of top pure brine. The AVO responses of the in situ synthetic seismic should be the same, according to the AVO response from the original seismic to validate the other analysis scheme. Based on the results, the AVO response of the original seismic was similar to the AVO response of in situ synthetic. The top gas sand was classified into AVO class three, where the amplitude decreased along with the increases of angle and had both negative intercept and gradient. Based on the AVO classification, the gas sand has interpreted as the low impedance sand. Moreover, The AVO response of the pure brine sand was classified into class four, where the amplitude increased with increasing angle and had negative with a positive gradient (Figure 11). Figure 9: (**a**) Facies percentage in I-1, I-2, and I-8 logs, (**b**) facies discrimination compared to any logs such as gamma-ray, density, neutron, and resistivity. Figure 11: AVO analysis from some models, (**a**) original seismic, (**b**) in situ, (**c**) pure gas, and (**d**) pure brine. Figure 10: [PERSON]’s fluid substitution in one of the reservoir intervals (Top D34/36) from Well I-8. The red rectangle was the interval of hydrocarbon. The AVO attributes were generated to distinguish between the change in lithology and fluid content by the anomalies identified from AVO analysis. Figure 6 illustrated the AVO responses derived from the product of the intercept and gradient. By combining AVO anomaly and data logs such as density and resistivity, a compatibility between AVO response and the gas sand could be found. AVO attributes, such as Scales Poisson's Ratio change, SQp and SQs were generated to compare which the most sensitive attributes in detecting the reservoir sand. Figure 11 showed the application of some AVO attributes for D34/36 interval which was integrated with well log data. As the product of intercept (A) and gradient (B), Scaled Poisson's Ratio attribute was applied, which showed the negative values at the top of the reservoir and positive response at the reservoir base. Well log responses were integrated to seismic responses to reduce the uncertainties. The I-8 log showed that top D34/36 was gas sand and compared to seismic on D34/36 interval, which was penetrated by well I-8 showed the negative value or orange color. That value was indicated as the hydrocarbon accumulation. The I-3 well logs figured that top D34/36 as the gas sand with low gamma-ray, on the other hand, according to the seismic scale, the hydrocarbon potential was not figured. Comprised of the new attributes, SQp and SQs have been applied to give better separation in differentiate between facies and hydrocarbon separations. These attributes provided a better resolution compared to the Scaled Poisson's Ratio attribute (Figure 12). By using SQp and SQs attributes, the seismic attribute at I-3 well log position could be captured as hydrocarbon potential, which was shown as a strong negative value or orange color. Figure 13 showed the application of SQp AVO attribute in differentiating the facies. Scaled color from \(-1\) to \(1\) was considered as the best range to identify the facies. SQp attribute from the seismic scale was compared to log responses e.g., density and resistivity. Both data gave consistency results. In the well log scale, top D, D+49, and D34/36 on I-8 well log location captured the low-density values and had been interpreted as sand intervals.