Despite their increased role in the global energy supply, liquid-rich unconventional gas (LRG) resources present a number of technical challenges. Among them, traditional production data analysis methods fail to successfully estimate and forecast production behavior in these systems. This failure is directly related to the extensive early-transient infinite-acting behavior exhibited by these systems, and the added complexities involved with liquid dropout and ensuing multi-phase flow of gas and condensate. Traditional approaches are strongly biased toward single-phase and boundary-dominated analysis; and when multiphase flow is considered, required input data often include laboratory-estimated pressure-saturation data and/or producing gas-oil-ratio data. In the present work, a novel extension of the similarity variable transformation method is developed to forecast production behavior in these LRG systems. The methodology uses the black-oil fluid formulation and considers linear and radial flow regimes under constant bottom hole pressure (BHP) and constant gas flow rate well conditions. Using this method, the system of governing partial differential equations is reduced to a system of ordinary differential equations solved by well-known Runge-Kutta techniques without the need for linearization. It is demonstrated that reservoir pressure and saturation behavior can be forecast simultaneously, thereby eliminating the need for pressure-saturation relationship or producing gas-oil-ratio data as inputs to the model. In all cases explored, the similarity results compared well to numerically generated reservoir data for a variety of well BHP and gas flow rate specifications. Calculated well production metrics also successfully matched data sets, indicating that this approach can be straightforwardly extended to estimate production metrics of interest during early transient conditions, such as liquid and gas production rates or gas-oil-ratio. Results strongly suggest that the method developed here provides a rapid and robust alternative to numerical simulation for forecasting of LRG reservoir systems.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology