抽油机井参数优化的粒计算方法

针对油气生产中的抽油机井参数优化问题，开展了一种基于抽油机井生产调控、维护措施数据的抽油机井生产参数优化的粒计算方法研究，研究中采用了粒计算、代价敏感粗糙集及推荐系统等机器学习方法。首先，利用决策树建立基于时间、空间及业务层次等抽油机井数据的多粒度融合模型；然后，利用代价敏感粗糙集定义与抽油机井业务相适应的代价敏感评价模型；最后，在代价约束条件下，设计基于域感知因子分解机的抽油机井生产核心参数及维护措施推荐模型。在实际的油气生产数据上进行不同粒度的对比实验，可以发现由粗粒度到细粒度调整抽油机井的生产参数，其生产核心参数优化的推荐准确度先是逐渐增加，后逐渐下降。说明在参数优化中，需要进行合适的粒度选择。

Granular Computing for Pumping Well Parameter Optimization

Aiming at the problem of optimization of pumping unit parameters in oil and gas production, a granular computing method is proposed based on the data of pumping unit production control and maintenance measures. In this paper, machine learning methods such as granular computing, cost-sensitive rough sets and recommendation systems are used. Firstly, a decision tree is employed to build a multi-granular fusion model based on the time, space, and business level of the pumping well data. Secondly, a suitable evaluation model is defined for the pumping well business with the cost-sensitive rough set. Finally, a recommended model of core parameters and maintenance measures is designed based on field-aware factorization machine under cost constraints. Based on real oil-gas production data, we have designed comparative experiments with different granularities. We adjusted the production parameters of the pumping well from coarse granule to fine granule, and found that the recommendation accuracy for optimizing the core parameters of production is gradually increased first and then gradually decreased. We can conclude that in parameter optimization, appropriate granularity selection is required.