A Simple Normalized Analytical Model for Oil Production of SAGD Process and Its Applications in Athabasca Oil Sands

Abstract

Summary Since the late 1980s, when the Alberta Oil Sands Technology and Research Authority Underground Test Facility project first demonstrated the feasibility of the steam-assisted gravity drainage (SAGD) technology, many commercial SAGD projects were brought online in Western Canada. Now, many of these projects have late-life SAGD wells approaching their ultimate SAGD recovery factors. Although these projects have demonstrated highly variable production performance, there is an opportunity to use the industry production data to find what they have in common and develop a normalized SAGD model. For this paper, we collected oil production history from several leading SAGD projects with late-life production in the Athabasca oil sands area and confirmed the three stages in an SAGD project lifespan: chamber rising, chamber spreading, and chamber falling stages. By normalizing the field data, all SAGD projects converged to one type curve, regardless of reservoir quality and operating conditions. Based on this observation, a new simple normalized model is derived to model the bitumen production in a typical SAGD process for Athabasca oil sands. The new model bridges the gap between the existing SAGD analytical model and conventional decline analysis and provides oil production forecasts based on the inputs for the five-component recovery factor method defined in the Canadian Oil and Gas Evaluation Handbook(Society of Petroleum Evaluation Engineers 2018). The model has been applied to one of the thermal projects to history match the field production. By running a Monte Carlo simulation, this model further demonstrates its capability to capture the uncertainty of the production forecast for the project at different stages of SAGD operation. In addition, by properly modifying the type curve of the analytical model, a similar workflow can be used to model cases with special reservoir quality or different operational limitations.