基于改进粒子群算法的蒸汽驱注采方案优化

针对蒸汽驱注采方案优化问题，以累积油汽比为优化目标，建立了以注汽速率、蒸汽干度和注汽压力等为约束条件的蒸汽驱注采方案优化数学模型，采用改进粒子群算法对该模型进行了求解，并优化了蒸汽驱的主要注采参数。改进粒子群算法以进化停滞步数为依据，对个体历史最优值和邻域内粒子的最优值实施随机扰动，并且只接受使个体适应度增加的随机扰动操作，使记忆中的最优粒子跳出局部最优解，保证了种群的多样性，提高了算法的精度和稳定性。实例计算证明，该优化模型计算结果准确，优化算法有效。通过该优化方法可快捷准确地进行蒸汽驱动态优化和方案调整，以便于指导蒸汽驱高效运行。

Injection and production project optimization of steam flooding based on improved particle swarm optimization algorithm

A mathematic model for injection and production optimization of steam flooding project was established by taking cumulative oil steam ratio as the optimization objective, and steam injection rate, steam quality, and steam injection pressure as the constraint conditions. An improved particle swarm optimization algorithm was used to solve the proposed model and to optimize the major injection and production parameters of steam flooding. The optimal values of individual history and neighborhood particles were disturbed randomly using the optimization algorithm according to the evolutionary stagnation steps; only the operation of random disturbance with increased individual fitness was retained so that local optimal solution could jump out with the optimal particle in the memory, thereby ensuring the population diversity and improving the algorithm's precision and stability. The optimization model was proven accurate and the optimization algorithm was validated in a case study. The proposed methodology enabled real-time and accurate dynamic optimization and project adjustment of steam flooding, ultimately contributing to its efficient operation.