A Semianalytical Forecasting Method for Unconventional Gas and Light Oil Wells: A Hybrid Approach for Addressing the Limitations of Existing Empirical and Analytical Methods

Abstract

Summary The rapid pace of exploitation of unconventional gas and light oil plays in North America has necessitated the development of new production-forecasting methodologies to aid in reserves assessment, capital planning, and field optimization. The generation of defendable forecasts is challenged not only by reservoir complexities but also by the use of multifractured horizontal wells (MFHWs) for development. In this work, a semianalytical method (SAM) is developed to provide a solid theoretical basis for forecasting. The technique is analytical in that it uses the methods of Agarwal (2010) to calculate contacted oil in place and contacted gas in place (COIP/CGIP) from production rates, flowing pressures, and fluid properties. The rate-normalized pressure (RNP) derivative (RNP′) is a key component of the calculation; pseudopressure is used for gas cases. The technique is also empirical in that an empirical function is fitted to the resulting COIP/CGIP curve vs. time. Although the method is flexible enough that any equation can be used to represent the COIP/CGIP curve, and hence, the sequence of flow regimes exhibited by MFHWs, the equation must be capable of being integrated to allow the extraction of RNP. The stabilized COIP/CGIP during boundary-dominated flow (BDF) must be specified for forecasting—thereafter, the method uses a material-balance simulator to model BDF. Hence, if the well is still in transient flow, a range in forecasts may be generated, depending on the assumed stabilized COIP/CGIP. The new SAM addresses some of the current limitations of empirical and fully analytical (modeling) approaches. Empirical methods, which have been adapted to account for long transient and transitional flow periods associated with ultralow-permeability reservoirs, lack a theoretical basis, and therefore input parameters may be difficult to constrain. However, empirical methods are simple to apply and require a minimum amount of data for forecasting. Analytical models, while representing the physics better, nonetheless require additional reservoir and hydraulic-fracture data that may not be available on every well in the field. The SAM proposed herein is intended to bridge the gap between empirical and modeling-based approaches—it is more rigorous than purely empirical methods, while requiring a lesser amount of data than fully analytical techniques. The new method is tested against simulated and field cases (tight oil and shale gas). Although a simple power-law function is used in the current work to represent the COIP/OGIP curve, which appears adequate for the cases studied, one should note that wells exhibiting long transitional flow periods (e.g., elliptical/radial) will likely require a different functional form.