## 1 Introduction  
Small scale faults (SSFs) are responsible for a minor portion of all seismic activity in an active region and have an offset of about 25-50 m [1] that can extend to about 2-4 km with small throws of about 40 m [2]. The study on the assessment of the small scale faults and fractures in heterogeneous sandstone reservoirs is a global issue [3]. However, it is challenging to accurately categorize fractures due to their heterogeneous nature and complicated features [4; 5]. SSFs and fractures control the storage and impact distribution of the natural gas storage in reservoirs [6] and provide necessary information related to tectonics, overpressure, burial history, and diagenesis [7]. SSFs and fractures tend to make gas reservoirs very complicated, particularly in those fields that show a heterogeneity and poor continuity of lateral facies distribution [8].Research of SSFs and fractures plays a vital role in establishing the porosity and permeability of these heterogeneous and tight sandstone reservoirs. The evaluation of SSFs and fractures provides controls and guidance for the prospect evaluation and development of heterogeneous sandstone reservoirs [9]. Interpreters usually manually mark faults on seismic sections to carry out fault interpretation, which can be problematic and time consuming. The uncertainty in manually interpreting the faults and fractures is very high, particularly in basins with sparse seismic data [10]. Several authors have illustrated the importance of classifying faults through various techniques [11; 12; 13] and fractures through an ant-tracking algorithm [14; 15; 16; 17]. Consequently, an effective and reliable method is required to characterize these faults and fractures. Contemplating past studies, the application of seismic attributes has played a vital role in aiding structural interpretation. Numerous authors have successfully applied structural attributes for a comprehensive analysis of the structural interpretation of seismic data [18; 19; 20; 21; 22]. Seismic edge-detection technology is the most commonly used method in the oil and gas industry to deduce the structural and stratigraphic features of seismic data [23]. However, this method is very challenging and makes it difficult to trace discontinuities that are concealed in the seismic sections, which may appear as slight changes in seismic waveforms, which makes it very challenging to interpret SSFs and fractures [24]. Recently, several authors have used curvature attributes to identify the orientations of faults and fractures [25; 26; 27; 28; 11; 23]. The coherency-based similarity attribute enhances the seismic edge of the seismic data [29]. The use of conventional attributes such as dip [11], curvature [23], coherency [30], dip and azimuth [31], chaos [32], and variance [33; 34] can be used to successfully detect the subtle faults and fractures of the seismic amplitude volume in sedimentary basins. In our study, we also adopted unconventional geometrical attributes that are not routinely used for characterizing faults and fractures. These include thinned-fault likelihood (TFL) [35; 36], fracture density [37; 38; 39; 40; 41; 42], fracture proximity, and fault-enhancement filter (FEF) similarity [43], the last of which can be further observed through a voxel connectivity filter (VCF) to yield a 3D fracture network; interpretation was performed to rank these fracture geobodies. Afterwards, artificial neural networks (ANNs) were applied to these attributes to characterize the fracture network. These seismic attributes provided the basis for detecting small scale reflection continuities in the seismic data, which is difficult to do when using conventional interpretation techniques. Contemporary studies of multi-attribute analysis via artificial neural networks have been widely acknowledged because of their effective approaches of identifying structural features [44; 45; 46; 47; 48; 49].