## 1 Introduction
Fitting the transient period makes it favorable for unconventional ultra-low permeability formations, given that most of a given well's life in these formations is in the transient flow regime period [7]. When it comes to unconventional reservoirs, there are several newly developed decline curve analysis models that are adjusted to be suitable for unconventional reservoirs. [PERSON] et al. (2008) introduced the power law exponential decline (PLED) method for future performance forecasting and estimated ultimate recovery (EUR) prediction of tight gas and shale production. They presented the \"power-law loss-ratio\" rate/time relation, which assumes that the decline rate follows a power law function on a log-log scale at early time and becomes constant at late time. This constant makes the late-time behavior similar to Arps exponential decline with a smooth transition [8]. Another DCA model is the Duong decline model [9], which is a rate-decline approach developed specifically for fractured shale-gas reservoirs. It assumes that the connected fracture density of the fractured area increases over time possibly because of local stress changes with pressure depletion in a linear-flow regime. It proposed a decline curve method for predicting EURs from shale gas wells in which fracture flow is dominant and matrix contribution is negligible (i.e., long-duration linear flow). This method was developed on the basis that for fracture flows, regardless of fracture types, production rate and time have a power law relation at a constant flowing bottomhole pressure. This means that rate/time plot on a log-log scale forms a straight line. Furthermore, [PERSON] et al. (2011) developed the logistical growth model (LGM) decline model to forecast production from a single well in unconventional reservoirs. The model is based on the concept that growth is possible only to a certain size. The maximum possible growth size is the one at which the growth variable stabilizes and the growth rate terminates, which is referred to as the carrying capacity. This model is adapted from another logistic growth model that models liver regrowth hyperbolically [10]. In 2015, [PERSON] et al. presented an empirical extended exponential form of the production decline analytical equation as an alternate decline curve approach. The authors suggested using the mechanism of \"growing drainage volume\" to conceptualize and model the performance of shale wells. This approach does not require the analyst to guess when to switch to a BDF model nor to force a switch to exponential decline [11]. To capture the physical aspects controlling the production decline, [PERSON] et al. (2018) introduced a physical-based decline curve model that accounts for the transition from transient to BDF, allows an initial delay and build up in the production rate, and has a bounded EUR. This model was implemented in an automated framework and applied to 992 gas wells from the Barnett. The authors concluded that at least 18 months of production history must be available for using this model with lower uncertainty compared to the Arps hyperbolic model. Additionally, they mentioned that the model is more conservative than Arps, Duong, and SEPD models for reserves estimation [12]. Furthermore, [PERSON] et al. (2018) developed an early-late decline model to better analyze the whole production life of shale gas wells based on the similar characteristics between shale gas production and self-diffusion of dense gas/surface diffusion of adsorbed gas [13]. Additionally, a detailed explicit four-step forecasting procedure applying this novel early-late decline model was proposed. The comparison of this method with the Arps hyperbolic and the Duong methods showed that it can lead to more confident forecasts [14]. Several of the common DCA model have been compared in the literature, for instance, [PERSON] (2015) compared different DCA methods (Arps, Power law exponential (PLE), Duong, and SEPD) with four unconventional gas and oil plays: the Barnett, the Woodford, the Bakken, and the Eagle Ford.