## 1 Introduction  
The extraction of hydrocarbons from a tight formation has attracted considerable attention in recent years. Tight formations are characterized by having low porosity and low permeability. The production of crude oil trapped in such low porosity and low permeability formations require stimulation by hydraulic fracturing [1; 2]. Hydraulic fracturing creates fracture networks that are vital for efficient production from such formations. To predict the long- and short-term production performances of the stimulated well, an accurate model of the induced fracture networks is crucial. However, owing to difficulties arising from explicitly modeled fracture networks and transient flow at the stimulated reservoir volume, forecasting the production is complex [3; 4]. Multiple studies have proposed empirical and analytical models and workflows that range from simple to more complex ones to predict production from a tight formation such as a shale formation [3; 5; 6; 7; 8; 9; 10]. Well test analysis methods and numerical reservoir simulation are two of the well-established techniques employed to predict production. Despite the successful application of well test analysis techniques to the model drainage area of a hydraulic fractured well [11], analytical models developed for well test analysis may not provide a reliable long-term prediction. This is due to the underlining assumptions used to find an analytical solution of the celebrated diffusivity equation [12]. On the other hand, numerical reservoir simulation, which is based on the first principle models, is capable of long-term prediction [13]. However, the difficulty in modeling fracture networks and the high cost of computation calls for a concerted and demanding effort. The cornerstone of a numerical reservoir model is a geological model which comprises the physical appearance and static properties of the actual reservoir. Hence, to build the geological model (geocellular model), data obtained from seismic, well logs, routine and special core analysis are applied in multimillion grid blocks that represent the heterogeneity and anisotropy of the structure. In addition, discrete fracture network models are included in the generated grid block system via statistical means. These fractures are clustered, and for each cluster, fracture half-length, fracture height, fracture width, and fracture conductivity are estimated. Despite having an irregular shape, the induced fractures and fracture networks are represented by a well-rounded geometry, which reduces the accuracy of the model [14]. Moreover, the integration of fracture characteristics in the geocellular model is a rather meticulous task that requires the modification of the starting geological model. Often, local grid refinements are utilized to account for the hydraulic fracture characteristics [15; 16; 17]. The numerical computation of fluid flow in fine-tuned grid blocks tends to consume a significant amount of computational time. Explicit hydraulic fracture (EHF) modeling [18], microseismic mapping [15], and stimulated reservoir volume (SRV) [19; 20] methods are commonly used to incorporate the impact of fracture and fracture networks. In contrast to other traditional practices such as decline curve analysis and rate transient analysis, a numerical model integrated with the EHF model is most comprehensive and robust [21]. It is descriptive of the impact of hydraulic fracturing during numerical simulation studies [22]. However, the limitation in EHF modeling has led to the development of the SRV [18; 20]. The SRV modeling technique lends itself to a simple yet efficient technique for handling the effect of complex and multicluster hydraulic fractures in numerical reservoir modeling and simulation. In the SRV method, a 3D volume is assumed around the hydraulic fractured well [20]. This volume is then assigned with enhanced permeability to account for the impact of the hydraulic fractures.