الفهرس 
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Abstract
Among different reservoir forecasting methods, decline curve method stands as the simplest, least time consuming and least data requirement method. This is more proper for tight and unconventional reservoirs. Production from these unconventional reservoirs have grown dramatically around the world for past few years. Conventional decline curve methods are still applied in forecasting these unconventional resources due to the lack of new methods fit these reservoirs. So, still one of the biggest challenges is to predict long-term performance of Unconventional Reservoirs. Various decline curve models have been proposed to model the time-rate behaviour of early transient and transitional flow of massive fractured unconventional reservoirs, but most are limited in the ability to properly model all flow regimes. In this study, various decline curve models developed to predict performance of Unconventional Reservoirs are studied, analysed , applied and validated for different reservoir scenarios, some of them are simulated data that present different scenarios of flow regimes (4-cases) others are real data for tight sand and shale unconventional reservoirs (2- cases). The models used in this thesis along with Arps Model are: • Stretched Exponential Decline Production Decline (SEPD). • Logistics Growth Model (LGM). • Duong’s Model. • Power Law Exponential Decline (PLE). Each model has its own parameters and equations. The main aim is to select the best applicable model/s in terms of simplicity of application, degree of fit and accuracy of EUR calculation. In addition, these methods are compared at various production times to investigate the effect of production time on the prediction performance. As a part of validation process, all methods are benchmarked against simulation. This work shows that all the methods predict various recovery and some fit certain simulation cases better than others. In addition, no single method could predict EUR precisely without reaching BDF. Using this work, engineers can select the best applicable model to predict EUR after identifying the simulation case that is most analogous to their field wells