Production Forecasting and History Matching of Hydraulically Fractured Reservoirs Using a Pressure Depletion Volume (PDV) Method

Abstract

Abstract The production rate and cumulative production of hydraulically fractured shale wells can be estimated using the pressure depletion volume (PDV) method. The Gaussian Pressure Transient (GPT) is used to compute the pressure depletion in the drainage region of single or multiple hydraulically fractured wells, and the pressure depletion is then translated to production performance. This new approach does not involve Darcy’s Law, and therefore provides an independent method to evaluate well performance. The pressure depletion in reservoir volume between hydraulic fractures is computed by integrating the normalized GPT for the fractured reservoir region, accounting for each individual fracture. Also included is the pressure drop in the nearby reservoir region from pressure changes initiated via the fracture tips. The total pressure depletion of the drained reservoir, can then be computed for each moment in time as an instantaneous analytical solution. The cumulative production is computed using from the comprehensive compressibility coefficient of the drained reservoir space. The daily production rate can then be computed from the time derivative of the cumulative production at any moment in time. To validate the PVD method, the production rate forecasts were history-matched to (1) real production data from the Eagle Ford shale formation, and separately, to (2) synthetic, noise-free CMG-IMEX production data. Both data sets could be satisfactorily matched. The PVD model can also quantify the relative contribution to production from the fracture tips and fracture box region, as well as determine how their relative importance switches over time. The PDV-method proposed in this paper is based on the GPT model, and can predict both the pressure depletion and production performance over the anticipated field life prior to drilling, which is helpful for optimizing completion designs and maximizing economic benefits.