Different Decline Curve Analysis (DCA) methods have been proposed to predict production performance of both conventional and unconventional shale reservoirs. These methods range from empirical to semi-empirical and theoretical. The different methods were developed on specific datasets, have their own assumptions and limitations and thus, are not applicable universally.

This study shows that a DCA method should be capable of simultaneously modeling the flow regime prevalent around the well and the changes in reservoir properties with time, to be able to successfully represent the production performance of the well and predict in future. In shales, flow regimes can be linear, bilinear, multi-fracture linear, post-linear, Stimulated Reservoir Volume (SRV) dominated boundary flow, compound linear, etc. The change in fracture conductivity due to fines migration, embedment, crushing, diagenesis and change in stresses due to production is another important phenomenon that a DCA method must account for.

This study critically analyzes various proposed historical DCA methods with respect to their capability to model fracture flow regimes and changes in fracture conductivity with time. On close examination, it was found that both the linear flow regimes and changes in fracture conductivity with time follow a power-law function. Thus, the reason for successful application of Arps Hyperbolic, Power-Law Exponential (PLE), Stretched-Exponential Decline (SEPD) and Duong methods rest in the fact, that decline rates in these methods are a power-law function or can be closely approximated by a power-law function.

A new simplified decline curve equation was also proposed by modifying the existing Arps exponential decline equation, where the constant decline rate was replaced by a power-law function variable decline rate. The application of this method was shown using production data from Haynesville and Eagle Ford. The average error in cumulative production prediction for 20 wells was found to be only 3% in Haynesville wells and 2% in Eagle Ford wells. This method is very robust and can account for different flow regimes and changes in fracture conductivity with time.


Production performance prediction in shale reservoirs is affected by many factors like geological complexities, presence of natural fractures, in-situ stress states, completion and fracture characteristics, multi-phase flow characteristics, etc. (Kabir et al. 2011). Complex fracture geometries and nano-darcy permeabilities of the matrix make it impractical to run numerical models to simulate the well production performance. Moreover, due to the cost, effort, and time required in numerical simulation, many operators resort to semi-analytical and empirical models. These methods require much less data than numerical models and are much faster.

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