Studying Modern Decline Curve Analysis Models for Unconventional Reservoirs to Predict Performance of Shale Gas Reservoirs

Among different reservoir forecasting methods, the 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 has grown dramatically around the world for the past few years. In this study, decline curve models that are developed to predict the performance of Unconventional Reservoirs are studied, analyzed, applied, and validated for different reservoir scenarios, some of them are simulated data that present different scenarios of flow regimes (4-cases) others are Field data for shale unconventional reservoirs. 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 to select the best applicable model/s in terms of the 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 prediction performance. As a part of the 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 could select the best applicable model to predict EUR after identifying the simulation case that is most analogous to their field wells.