An evolution-based algorithm is applied to comprehensively optimize a field scale reservoir developing process, spanning from the primary production to the waterflooding and then the miscible water-alternating-CO 2 (C...
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An evolution-based algorithm is applied to comprehensively optimize a field scale reservoir developing process, spanning from the primary production to the waterflooding and then the miscible water-alternating-CO 2 (CO 2 WAG) process. Effects of 98 operational parameters on the net present value (NPV) are analyzed and quantified, including the primary production duration, water injection rates during the waterflooding process, process onset time, gas and water injection rates of CO 2 WAG process and producer bottomhole pressure (BHP) during each production stage. The impacts of geological uncertainty are evaluated using multiple reservoir realizations. It has been found that durations of the primary production and waterflooding processes have the most pronounced impact on the final NPV. The oil recovery of the comprehensive optimization scenario has been enhanced by 23.37% compared to that of the conventional WAG optimization. This is mainly due to the shorter durations of the primary and waterflooding processes.
Harris hawks optimization (HHO) is one of the leading optimization approaches due to its efficacy and multi-choice structure with time-varying components. The HHO has been applied in various areas due to its simplicit...
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Harris hawks optimization (HHO) is one of the leading optimization approaches due to its efficacy and multi-choice structure with time-varying components. The HHO has been applied in various areas due to its simplicity and outstanding performance. However,the original HHO can be improved and evolved in terms of convergence trends, and it is prone to local optimization under certain circumstances. Therefore, the performance and robustness of the algorithm need to be further improved. In our research, based on the core principle of evolutionary methods, we first developed an elite evolutionary strategy (EES) and then utilized it to advance HHO's convergence speed and ability to jump out of the local optimum. We named such an enhanced hybrid algorithm EESHHO in this paper. To verify the effectiveness and robustness of the EESHHO, we tested it on 29 numerical optimization test functions, including 23 classic basic test functions and 6 composite test functions from the IEEE CEC2017 special session. Moreover, we apply the EESHHO on resource-constrained project scheduling and QoS-aware web service composition problems to further validate the effectiveness of EESHHO. The experimental results show that proposed EESHHO has faster convergence speed and better optimization performance by comparing it with other mainstream algorithms.
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