## 3 Data Set and Methodology
### Methodology
Meanwhile, the intercept of Class IV was negative and the gradient was positive [21; 22; 23] (Figure 5). Figure 5a showed the character of gas sand on the well log which had high resistivity responses. The amplitude of the gas sands were expected to increase with offset and give negative value for intercept and gradient (Figure 5b,c). Figure 5: (**a**) Characteristic of resistivity log for gas sand reservoir at study area, which was highlighted by red rectangles, (**b**) AVO response for class 1, 2, 3, and 4, and (**c**) AVO classes of a seismic reflection based on AVO cross plot. The geological model or petrophysical analysis in a basin was an essential element in the interpretation of amplitudes. AVO analysis was facilitated by cross plotting AVO intercept (A) and gradient (B). AVO was classified under a variety of reasonable geologic conditions. AVO technique produced a direct hydrocarbon indicator (DHI), lithology separation, and fluid types characterization using angle-stack seismic data based on amplitude contrast in interfaces [20]. A seismic DHI was characterized as a seismic anomaly pattern that was explained by the presence of hydrocarbons [24]. A DHI had many characteristics; AVO anomalies, bright spots, and flat spots in general. A DHI interpretation should be suitable with geological interpretation. Angle seismic-stack was used to create seismic gather as an input of AVO analysis [25]. Real seismic data, pre-stack seismic data including near, mid, and far, then well log data were integrated to get suitable identification on facies and fluid. The AVO analysis was created by extracting intercept (A) and gradient (B) to be applied as Scaled Poisson's Ratio change and the new AVO attributes (SQp, and SQs), which created cross plots to classify reservoir fluids and lithological units [5]. The seismic gather was used as an input for AVO common attributes, including intercept (A) and gradient (B), which was applied by two-terms of Aki-Richards (Figure 6). The common method in gas identification was Scaled Poisson's Ratio attribute [20]. Scaled Poisson's Ratio AVO attribute identification was based on the reservoir fluid content. To enhance the higher quality of separation for hydrocarbon detection and facies separation, the latest methods, SQp and SQs, were also applied. New AVO attributes methods, SQp and SQs, were applied in each interest interval area, to provide a better understanding of facies distribution and hydrocarbon identification. Figure 6: Xline 5078 intersection for (**a**) Intercept, (**b**) gradient. SQp and SQs methods in well log scale have been successfully patented which was derived from elastic properties parameters [9]. In the well log scale, SQp attribute response was equivalent to the gamma-ray log and SQs attribute resembled the resistivity for facies and hydrocarbon identification [12]. SQp and SQs also could be applied in pre-stack stack seismic inversion. In this research, the SQp and SQs were intended to be directly applicable on a seismic scale as new AVO attributes. These methods were proposed to get better separation in facies and hydrocarbon prediction. The study has started with the concept of AVO attributes to understand the SQp and SQs in AVO domain. In AVO method, the potential hydrocarbon reservoirs were defined as an AVO anomaly. The parameters compressional velocity, shear velocity, and density were highly corelated with the anomaly of hydrocarbon and lithologies detection. These correlations defined relationships between the angular reflection coefficients A and B [26]. As the products of intercept (A) and gradient (B), SQp and SQs have been derived to get more sensitive AVO attributes from the equations:  
\[\text{SQp }=\frac{5}{6}\text{A}\frac{\left(-2+\left(3(\text{A}+\text{B}) \right)\right)^{2}}{2(3-(\text{A}+\text{B}))} \tag{1}\]  
\[\text{SQs }=\frac{10}{3}\text{A}\frac{\left(\left(\left(3(\text{A}+\text{B}) \right)-2\right)\right.}{\left(\left(3(\text{A}+\text{B})\right)+2\right)} \tag{2}\]