Field Scale Application of the SOS-FR (Steam-Over-Solvent Injection in Fractured Reservoirs) Method: Optimal Operating Conditions

摘要

Heavy oil reserves are considered to be the upcoming hydrocarbon resource. Yet, more efficient methods are needed as there are substantial economical and environmental drawbacks to sole injection of steam and solvents. A combined application of these yielded promising results in the laboratory experiments. But, optimal application conditions and cost lowering options need to be determined. Steam-over-solvent injection in fractured reservoirs (SOS-FR) is a recently proposed method which consists of an alternate injection of steam and hydrocarbon solvents to improve oil recovery over steam injection and accelerate the solvent retrieval rate. The initial tests were done for hot-water conditions instead of steam and liquid solvents for simplicity (Al- Bahlani and Babadagli, 2008; 2009a-b; 2011b). In our modification too this method, w introduced CO2 as an alternative to we hydrocarbon solvents for only one pressure and temperature condition (Naderi and Babadagli, 2012). Initial results out of this study showed a moderate recovery of 50% OOIP in average for unfavorable matrix conditions (oil wet). In the present study, the SOS-FR applications with CO2 were tested at various conditions numerically and with different timings to improve the recovery. First, the effect of different parameters was studied to obtain the best match between the simulation and experimental results. This exercise not only provided data for field scale simulations (relative permeability and diffusion coefficients) but also clarify the impact of different rock and fluid properties on the mechanics of the proposed EOR technique. Finally, an optimization scheme was suggested for field scale applications. In this exercise, a field scale numerical model of experiments was performed based on experimentally validated core scale model and the optimal conditions (solvent type, application pressure and temperature, duration of cycles) were determined to maximize the recovery.