Using Improved Decline Curve Models for Production Forecasts in Unconventional Reservoirs

Abstract

The flow behavior in nano-darcy shales neighbored by high conductivity induced natural fractures violates the assumptions behind Arps' decline models that have been successfully used in conventional reservoirs for decades. Current decline curve analysis models such as Logistic Growth Analyses, Power Law Exponential and Duong's model attempt to overcome the limitations of Arps' model. This study compares the capability of these models to match the past production of hundred shale oil wells from the Eagle Ford and investigate how the choice of residual function affects the estimate of model parameters and subsequently the well life, pressure depletion and ultimate recovery. Using the proposed residual functions increased the tendency of deterministic models to have bounded estimates of reserves. Results regarding well performance, EUR, drainage area and pressure depletion are obtained quickly and show realistic distributions supported by production hindcasts and commercial reservoir simulators. Overall, the PLE and Arps' hyperbolic models predicted the lowest/pessimistic and highest/optimistic remaining life/reserves respectively. The newly proposed residual functions were thereafter used with the Arps' hyperbolic and LGA models. We found that the use of rate-time residual functions increased the likelihood of the value of hyperbolic exponent being less than 1 by 87.5%. The proposed residual functions can be used to provide optimistic and conservative estimations of remaining reserves and remaining life using any of the above decline models for economic analysis. The key results provided by the modified DCA models help in long-term planning of operations necessary for optimal well completions and field development, accomplished in a fraction of the time currently required by other complex software and workflows.

Methods of Decline Curve Analysis for Shale Gas Reservoirs

“…Meyet et al [28] illustrated that the PLE model varies the least with respect to length of production history for all wells, and the PLE and LGA models usually return similar results. Paryani et al [29] showed that the PLE model fits the historical production data 67% of the time, and provided the lowest forecasts among all the DCA models in their study and thus the most conservative forecast.…”

“…Zuo et al [35] pointed out that if m and a in Equation (17) are within certain ranges, the gas flow rate should decrease monotonically, and q i determination in the model may lead to unreliable results if the production history is shorter than 18 months. Paryani et al [29] fitted well with 51% of the historical production data, and the Duong model fits better with longer and less noisy historical production data. In Wang et al [37], the authors proposed a new empirical method and compared it with the SEPD model and Duong model, and concluded that the SEPD underestimates EUR and the Duong model overestimates the ultimate recoveries.…”

“…The authors also showed that the EUR forecasts by MHD are often greater than those of other DCA methods. Paryani et al [29] also showed that the MHD model provides over-optimistic estimates of reserves and longer remaining life of shale oil wells.…”

“…However, an upward inflection in the curve would occur if n > 1 [28]. The main assumption in this model is that the entire reservoir can be drained by a single well over a sufficiently long period [29].…”

“…Equation (29) will be used to estimate the possible ranges of parameters λ and α . Four steps are needed to reconstruct the FDC model:…”