基于AERF模型的油井结蜡预测

油井结蜡是一种在开发以及开采油田时对油井正常产出造成了负面影响的现象,该现象会引起油流通道堵塞,导致油井开采过程中出油量降低.对油井结蜡状况做出智能预警,完成油井设备提前修复,对油田提高产能效率、降低维护成本及智能化管理有非常关键的价值.为了解决油井正常数据和结蜡数据严重不平衡问题,本文引入了自适应合成抽样法(ADASYN)和最近邻规则欠抽样法(ENN)两种非均衡样本处理方法,分别对类别为结蜡的样本和非结蜡的样本进行处理,最终使用随机森林算法对新构成的数据集训练,构造出AERF智能模型来预测油井结蜡.实验结果表明,提出的AERF模型在油井的结蜡数据集中预测效果更佳,明显地提高了预测精度.

Prediction of Oil Well Wax Deposition Based on AERF Model

Wax deposition in oil wells seriously affects the normal production of oil wells during the development and production of oilfields. This phenomenon will block oil flow channels and reduce oil production during the production of oil wells. Wax deposition prediction in oil wells and advance maintenance of oil well equipment are pivotal to higher production capacity, lower maintenance cost and more intelligent management. To solve the problem of serious imbalance between the normal data and wax deposit data of oil wells, this study introduces two processing methods of non-equilibrium samples, ADASYN and ENN, which deal with the non-paraffin and paraffin samples separately. Finally, the random forest algorithm is used to integrate the training data set, and the intelligent AERF model is constructed to predict the wax deposition in oil wells. The experimental results show that the AERF model proposed in this study has a better prediction effect in the wax deposition data set of oil wells, greatly improving the prediction accuracy.