Abstract:

Pumping motor with large electrical horsepower for small power-consuming equipment is commonly seen in beam pumping units, as it is difficult to understand exact relationship between motor working efficiency and complex dynamic loads. An online hybrid model for motor load torque under dynamic load changes was proposed by online sequential-extreme learning machine (OS-ELM) with a consideration of polished rod loads as the system's loads. First, mechanism models of each part in the pumping unit were separately built according to working principles of the system. Then, OS-ELM-based online soft sensor model was built to obtain value for a critical uncertain variable, the underground friction. Original structure of the soft sensor model was first set by offline training with historical production data and then online updated by a sliding window method. Therefore, the underground friction is no longer a constant value, which was given by any subjective experience in most other studies, but a dynamic value following system changes, which is more in line with actual operation conditions. The simulation results of proposed method on a normal oil well demonstrated validness and effectiveness.

Key words: hybrid modeling, beam pumping units, motor load torque, OS-ELM model, underground friction, measurement, petroleum, model

摘要：

由于难以掌握电动机工作效率与复杂动态负载的准确关系，游梁式抽油机系统普遍存在“大马拉小车”的现象。针对这个问题，研究负载动态变化下电动机负载扭矩的建模新方法，将“驴头”悬点载荷看作系统的负载，提出了基于OS-ELM的在线混合模型。首先根据采油工作原理，建立系统各机构的机理模型；然后针对模型中的主要不确定参数——井下摩擦力，建立基于OS-ELM的在线软测量模型，首先由历史生产数据离线训练得到初始结构，其次采用滑动窗口方法指导模型的在线更新。通过研究，井下摩擦力不再是依赖主观经验给定的定值，而是跟随系统变化的动态值，这更加符合实际生产工况。由一口生产井进行实例验证，仿真结果表明本文所提出方法是合理有效的。

关键词: 混合建模, 游梁式抽油机, 电动机负载扭矩, OS-ELM模型, 井下摩擦力, 测量, 石油, 模型