An Investigation into Temperature Distribution and Heat Loss Rate within the Steam Chamber in Expanding-Solvent SAGD Process

Abstract

In order to save energy and to be more environmentally friendly, Expanding-Solvent SAGD (ES-SAGD) is proposed by adding solvents into the injection vapor. However, much heat may still be wasted due to early steam condensation, which is associated with heat transfer and phase behavior within the steam chamber during ES-SAGD process. The objectives of this paper are to study temperature distribution within the steam chamber and to evaluate the overall heat loss rate of ES-SAGD by using a semi-analytical model. In the mathematical model, temperature and mass profiles within the chamber are implicitly represented rather than explicitly treated as beyond the edge of the chamber. By considering early steam condensation and phase change within the chamber, the model dynamically couples heat transfer equation and mass transfer equation. This approach allows us to clearly observe the heat loss rate within the chamber as well as to the overburden. The model is solved iteratively after the steam velocity at the chamber edge is determined by introducing theory of steam front stability. Finally, the proposed model is validated by comparing calculated temperature profile with the numerical simulation results. The results of the analytical study reveal that, for solvents with smaller molecular mass, the temperature starts dropping deeper from chamber edge to the injection end, and the temperature gap between the two ends is larger than that of heavier solvents. Based on the calculated temperature profiles, it is considered that heavier solvents may not improve thermal efficiency, while lighter solvents can help to reduce heat loss rate to the overburden especially in the late stage of ES-SAGD. Moreover, although lighter solvents are always used to relieve the heat losses to the overburden, the heat wasted inside the steam chamber is severer than that of heavier solvents. Therefore, considering the total heat losses to the overburden and inside the steam chamber, the steam/oil ratio (SOR) of ES-SAGD in some ultra-thick reservoirs may be unfavorable even compared with that of SAGD. On the basis of analysis for heat loss rate to the overburden and inside the chamber, several guidelines are presented to select solvent for ES-SAGD process. The proposed guidelines will help to better predict and design the future ES-SAGD heavy oil recovery projects.

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

“…Machine learning is such a fast developing and compelling approach to perform well production performance, which automatically learns and improves from the experience to output the expected results without being explicitly programmed [35]. Meanwhile, this method is also based on big data, ignoring a lot of ideal scenario restrictions, such as constant work conditions and boundarydominated flow regimes in the DCA model [36,37]. Zhong et al [38] proposed a deep learning-based method for reservoir production forecast under uncertainty and concluded that their method can predict the reservoir pressure and fluid saturation with high accuracy.…”