基于PSO-ELM模型的潜油柱塞泵冲次优化方法

为改进油井后期泵效低下、能耗高等缺陷，提出了一种基于PSO-ELM模型的潜油柱塞泵冲次优化方法.采用粒子群（PSO）算法与极限学习机（ELM）相结合的方式来实现动液面软测量建模；根据动液面及潜油柱塞泵工作电流变化，以油井运行经济性最优为目的建立目标函数得到潜油柱塞泵冲次，解决了在油井生产时不能准确调节抽油机冲次问题；最后以目标函数关系建立模糊控制器模型，根据输入参数调整潜油柱塞泵冲次.实验结果表明，建立的软测量模型预测动液面精度高，模糊控制器能够更加合理地调整抽油机冲次，最终达到智能调整冲次大小、提高油井采油率及节能的目的.

Submersible Oil Plunger Pump Impulse Optimization Study Based on PSO-ELM Model

We propose a submersible oil plunger pump stroke frequency optimization method based on a particle swarm optimization-extreme learning machine (PSO-ELM) model to improve low pump efficiency, high energy consumption, and other defects. We establish the soft measurement model of a working dynamic liquid level by combining partical swarm optimization (PSO) arithmetic with extremc learning machine (ELM). According to the change of the working current of the working dynamic liquid level and submersible oil plunger pump, we solve the problem that the pumping frequency cannot be adjusted accurately during oil well production by establishing the objective function to obtain the pumping frequency that will result in the optimal economic performance of oil well production. Finally, we establish a model of fuzzy controller by employing the relationship of objective function to input parameter to adjust the pumping frequency of a submersible oil plunger pump. The experiment result shows that the established soft measurement model has high precision of moving liquid level, and the fuzzy controller can adjust the pumping frequency more reasonably to intelligently adjust the size of pumping, thereby improving oil recovery and saving energy.