## 1 Introduction
The performance of the reservoir model with modified permeability is then matched with historical data to calibrate the permeability and supposed stimulated reservoir volume. However, it is essential to recognize that the SRV is only the reservoir volume affected by the stimulation; hence, it does not honor the details of the fracture structure and spacing. [PERSON] 2014, compared the performance of the SRV method with Arp's analytical decline curve analysis [23]. It was found that when the hyperbolic decline curve model is systematically used in segments according to flow regimes (by appropriate choice of decline exponent constant), decline curve analysis provides the same result as the SRV method. However, the decline curve analysis requires less effort and time compared to the SRV method. Hence, the SRV method may not be a good option to be used in reservoir simulation. Moreover, the misinterpretation of the stimulated reservoir volume and its aspect ratio, defined by the ratio of height to width and to length, may result in a significant discrepancy of simulation output and the actual performance. In general, capturing important reservoir properties and fully integrating fracture properties in reservoir modeling and simulation by using EHF and SRV is painstaking. [PERSON] 2016, showed that well test analysis using data acquired during production could provide more detail and reliable information than SRV or EHF methods [24]. The stimulated reservoir volume method should be used only if a single method such as micro-seismic monitoring or pump-off drawdown test is available. Important fracture properties such as reservoir permeability, fracture conductivity, and fracture half-length can be better estimated by well-test analysis. Nevertheless, the analytical model from well testing interpretation alone may not adequately provide long-term prediction because of the underlining assumptions made during the development of the analytical solution to the diffusivity equation upon which well test analysis is based. On the other hand, reservoir simulation has been successfully employed for long-term prediction, in spite of difficulties to describe hydraulic fracture well behavior [9; 25] and high computational time [26]. One of the difficulties in using numerical simulation is the representation of hydraulic fracturing effects [13; 27]. In EHF modeling, extremely fine grid blocks are set up near the fractured well, and the fracture properties are distributed based on fracking job characteristics, such as fluid injection rate and proppant volume [28]. On a similar note, well test analysis is efficient in providing critical information such as fracture half-length, effective permeability, and conductivity of a hydraulic fracture well [29; 30]. One of the ways to present hydraulic fractured well behavior via well test analysis is by employing high negative skin. While half or quarter slopes in the log-log plot are not captured during well test analysis, negative skin and fracture half-length can be estimated using large-scale grid sizes. This is because, in hydraulically fractured wells, the effective wellbore radius is big. In this study, a method that combines well test analysis and reservoir simulation is proposed for the efficient and long-term production forecasting of a hydraulically fractured well. The method optimizes gridding of a numerical reservoir simulator by incorporating interpretations obtained from well test analysis. In this study, the grid size is set for the main stress direction with fracture half-length and optimized for the minor stress direction based on simulation results. This allows to effectively utilize the capability of the numerical model with reduced computational time.