## 6 Ant-Tracking Computation
### Automatic Fault and Fracture Extraction Using ACO  
Fracture detective attributes generate responses along the surfaces of a fracture and help to detect automatic fracture interpretations. These attributes may incorporate noises and residual responses,  
Figure 16: Ant-tracking using an east-west azimuthal filter; (**a**) ant-tracking using a east-west azimuthal filter (**b**) ant-tracking using a north–south azimuthal filter; and (**c**) ant-tracking results without an azimuthal filter. Figure 15: (**a,e**) Original seismic at inline 665 and crossline 972, respectively; (**b,f**) structural smoothing applied on the original seismic; (**c,g**) variance attribute; and (**d,h**) ant-tracking attribute. The green arrows highlight the faults, and yellow arrows show fractures. which makes it very challenging to interpret fractures. To overcome these challenges to map the fractures, attributes conditioning was applied through an innovative approach of the ACO algorithm. The 2D (bird's-eye view) (Figure 17a) and the 3D view (Figure 17b) of the extracted fractures show the orientation of fractures. Fracture surfaces generated in the ACO method are commonly fractured segments instead of fracture surfaces [87]. The results of the extracted fracture network were displayed on the stereonet, which showed that 607 visible patches (fractures) were present, and these were scattered in different orientations. The results of the dip azimuth showed that most fractures were clustered in the NW-SE direction (Figure 18a). The histogram of the fracture dip angle showed that the most fracture patches lie between 16\({}^{\circ}\) and 32\({}^{\circ}\) (Figure 18b). The histogram of the fracture length suggested that most fractures have fracture lengths between 200 and 500 m (Figure 18c). The histogram of the fracture surface area indicated that many fracture patches are present between surface areas of about 10,000-30,000 m\({}^{2}\) (Figure 18d). Figure 17: **(a)** 2D view of extracted fracture surfaces and **(b)** 3D view of the automatic fracture network by ACO. The inserted time slice (pink color) at 2176 ms was used to differentiate the fractures above and below this horizon. Figure 18: **(a)** Stereonet showing the distribution of azimuth of the extracted fractures from seismic volume; **(b)** histogram of fracture dip angle; **(c)** histogram of fracture length; and **(d)** histogram of the fracture surface area. All the results of the histograms showed that the frequency of the number of patches decreased with increasing fracture angle, fracture length, and fracture surface area. The results showcased the presence of small-to-medium scale fracture segments with an average length of about 625 m and that were gently dipping to about average 38\({}^{\circ}\). The corollary is that the overall results of the workflow showed that these numerous fractures played an important role in the development of the reservoir potential in the Sawan gas field. The current study also revealed a fault in the study area that had been previously overlooked. The fault is a small scale fault, and the orientation of the fault is NNW-SSE (Figure 19a-c). The throw and heave of the fault are minor and are present in the Upper Goru Formation of Upper Cretaceous age. This NNW-SSE oriented fault distributes the study area into east and west parts and is named the Sawan fault [65]. The generated steering cube for the dip and azimuth estimation, as well as the application of structural filtering to remove the noise, considerably enhanced the sharpness and continuity of the structural features such as SSFs and fractures. Hence, the current study provided a workflow that put forward an effective data conditioning method for fault and fracture identification. Moreover, the adopted workflow also presented an advanced and novel approach of attribute computation using ANN-UVQ for the identification of the fracture network. The current study proved helpful in identifying the fault and fracture probabilities of the study area.