A New Analytical Model for Predicting Steam-Assisted Gravity-Drainage Performance in the Plateau and Decline Stages

Analytical models have been widely used to study the steam- assisted gravity- drainage (SAGD) process. Existing analytical models for this process either underperform or are limited in their applicability. In this paper, a new analytical model is developed to address these shortcomings. The new model consists of three major components. First, an equation for predicting oil production rate during the plateau stage was developed. This equation allows for permeability anisotropy in the reservoir, which is neglected in the majority of existing analytical models. Second, a novel and concise equation for calculating oil production rate during the decline stage was derived. This equation for the first time provides a general relationship between oil production rate during the decline stage and the properties of the reservoir together with key operating conditions and simultaneously mitigates the shortcomings of the majority of existing models (e.g., inapplicable in the decline stage of the SAGD process). Third, new correlations representing heat loss from the steam chamber to the overburden and heat transfer to oil- undepleted zone ahead of the chamber interface were derived; new predictive equations for estimating steam/oil ratio (SOR) in the plateau and decline stages of SAGD were obtained. The new model has been validated against a 2D scaled laboratory experiment and a set of field data. The results of this validation show that the new model predicts the oil production rate and SOR over the lifetime of an SAGD operation (excluding the short steam rise period) reasonably well. Predictions from the new model were also compared with several existing analytical models: The new model provided a closer match to actual measurements than other models. This robust model is grounded in more physics and incorporates reservoir geology and well operating conditions. It enables better understanding of the SAGD process and significantly improves the prediction of SAGD performance. Hence, it can be used to design new SAGD projects, predict existing projects, and optimize existing projects.