Whole-time scenario optimization of steam-assisted gravity drainage (SAGD) with temperature, pressure, and rate control using an efficient hybrid optimization technique. (15th January 2022) - Mir, Hamed; Siavashi, Majid
Abstract: Whole-time (simultaneous) scenario optimization of oil recovery over a long time necessitates control of many parameters which is very time-consuming. Usually, to reduce the decision variables, scenarios are optimized in separate intervals. As a new work, a whole-time scenario optimization of the steam-assisted gravity drainage (SAGD) process with several decision parameters (steam injection rate, temperature, and pressure of all intervals) is performed in a 3D reservoir defining an 8-year scenario. Two relatively fast optimization algorithms, i.e. particle swarm optimization (PSO) and pattern search (PS), are utilized to maximize the net present value (NPV). First, through different optimizations, appropriate population size is selected for PSO. Next, the performance of four different scenarios is investigated, and the excellence of whole-time scenario optimization is proved. It is concluded that a higher NPV is obtained by increasing the number of time-intervals. Furthermore, conducting the whole-time optimization with 8 time-intervals can reduce the SAGD process by 1 year. Finally, a new hybrid PSO-PS algorithm is proposed that uses the advantages of both PSO and PS algorithms and reduces the number of function calls. The hybrid algorithm could result in the same results as PSO, while remarkably improved the convergence speed (about 84%). Graphical abstract: Image 1 Highlights: Steam injection temperature, rate & pressure are opted to optimize the SAGD process. PSO, PS & PSO-PS are used to compare performance of different optimization scenarios. Simultaneous whole-time optimization excels over other approaches. CO-8TI scenario could reduce the SAGD process by 1 year compared to BO-1TI case. PSO-PS led to same results as PSO, while highly improved the convergence speed (84%).