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基于AERF模型的油井结蜡预测
引用本文:常益浩,李庆云,李克文. 基于AERF模型的油井结蜡预测[J]. 计算机系统应用, 2021, 30(9): 138-144. DOI: 10.15888/j.cnki.csa.008060
作者姓名:常益浩  李庆云  李克文
作者单位:中国石油大学(华东)计算机科学与技术学院, 青岛 266580
基金项目:国家自然科学基金重大项目(51991361); 国家自然科学基金(61673396)
摘    要: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(ADAS...

关 键 词:结蜡预测  不平衡数据  样本均衡  抽样
收稿时间:2020-11-27
修稿时间:2021-01-04

Prediction of Oil Well Wax Deposition Based on AERF Model
CHANG Yi-Hao,LI Qing-Yun,LI Ke-Wen. Prediction of Oil Well Wax Deposition Based on AERF Model[J]. Computer Systems& Applications, 2021, 30(9): 138-144. DOI: 10.15888/j.cnki.csa.008060
Authors:CHANG Yi-Hao  LI Qing-Yun  LI Ke-Wen
Affiliation:College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
Abstract: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.
Keywords:wax deposition prediction  unbalanced data  sample equilibrium  sampling
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