A New Approach to Apply Decline-Curve Analysis for Tight-Oil Reservoirs Producing Under Variable Pressure Conditions

Abstract

Summary Decline-curve models inherently assume that the bottomhole flowing pressure (BHP) is constant. This is a poor assumption for many unconventional wells. For this reason, the application of decline-curve models might lead to incorrect flow regime identification and estimated ultimate recovery (EUR). This work presents a novel technique that combines variable BHP conditions with decline-curve models and compares its results with traditional decline-curve analysis (DCA) for both synthetic and tight-oil wells. Using superposition, we generate a synthetic rate example using the constant-pressure solution of the diffusivity equation for a slightly compressible fluid (decline-curve model) along with a BHP history. However, we validate the technique using bottomhole and initial reservoir pressures that contain errors. The algorithm consists of three sequential optimizations. In each optimization, the algorithm estimates (1) the decline-curve model parameters, (2) the BHP, and (3) the initial reservoir pressure. The result of the synthetic example leads to an accurate production history match and corrected estimates of the initial reservoir pressure and the BHP history. Finally, we compare the results of the technique with traditional DCA in terms of (a) the model parameters, (b) flow regime identification, (c) production history matches, and (d) EUR for tight-oil wells using three decline-curve models: 1D single-phase constant-pressure solution of the diffusivity equation for a slightly compressible fluid, logistic growth model, and Arps hyperbolic relation. For the synthetic case, the algorithm estimates the model parameters and the true initial reservoir pressure within a 2% error. In addition, the method regenerates the true BHP history and provides an excellent production history match. The analysis of the tight-oil wells shows that the new approach clearly identifies the flow regimes present in the well, which can be difficult to detect using traditional DCA when the BHP varies. In contrast, the application of traditional DCA shows considerable errors in the estimation of the model’s parameters and a poor history match of the production data. Finally, this work shows that incorporating variable BHP into the decline-curve models leads to more accurate production history matches and EUR values compared to using only rate-time data. This paper illustrates a workflow that incorporates variable BHP conditions for any decline-curve model. Moreover, the approach can handle errors in both the BHP and the initial reservoir pressure and provides corrected estimates of these variables. The technique is computationally fast and history matches and forecasts the production of unconventional wells more accurately than traditional DCA. The major contribution of this work is the remarkable simplicity yet robustness of our solution to variable-pressure DCA. Finally, we developed a web-based application to provide the readers with a hands-on experience of this new technique.