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Abstract Unconventional plays have moved to the forefront of the energy industry in the U.S. over the last five years due to advancements in technology and the overall abundance of producible hydrocarbons discovered near existing infrastructure. In the present economic climate, there is an increased interest in liquid rich plays, and because of the relatively limited historical production data available for these resources, there is a lot of industry discussion regarding future decline performance and estimated ultimate recovery (EUR) per well. The objective of this paper is to discuss the uncertainty associated with estimating reserves in U.S. unconventional plays using common decline curve analysis (DCA) methods in comparison to analytical modeling. Broadly speaking, there are five common methods for estimating: use of analogs, volumetric analysis combined with an estimate of recovery efficiency, decline curve analysis (DCA), analytical models and numerical simulation. Among theaforementioneds, DCA is the simplest and often fastest way to estimate volumes. However, the theoretical basis for most DCA approaches does not apply to unconventional reservoirs, which introduces some uncertainty into estimation of volumes. Nevertheless, it is commonly applied because of its perceived simplicity. Different unconventional DCA methods were compared with results of an analytical model generated using commercial software: the power law model (PLE), the logistic growth model (LGM), and Duong's method. The analysis was performed on various unconventional plays based on reservoir type and well geometry. All historical production data is gathered from public documents. The application of the DCA methods was also extended to various fluid types to determine their suitability for application in oil as well as gas reservoirs. The results of the study show that comparing multiple DCA methods with an analytical model aids in the understanding of the range of uncertainty associated with the EUR of unconventional wells. The study also helps establish the most appropriate DCA methods for various reservoir types, well geometry, and fluid types. The results also suggest approaches for avoiding violating the SEC's guidelines for categorizing proven reserves.