摘要
及时准确地掌握油井的工况,对于油田安全高效生产和提高采收率具有重要意义。随着油田信息化建设的不断深入,示功图等油井生产动态监测数据实现了实时采集,并积累了海量数据,亟待进一步挖掘利用。基于“大数据+深度学习”的新一代人工智能技术,有望突破现有技术的局限,引领油井工况诊断技术升级。为此,依托4000余万组涵盖不同油藏类型油井的历史动态监测数据,制备了涵盖5大类37种工况类型的油井工况诊断样本集,在此基础上,选择卷积神经网络算法,个性化设计了面向油井工况诊断问题的卷积神经网络(OWDNet),包含26层5900余万个可学习参数。使用油井工况诊断样本集对OWDNet进行训练,10轮次后,训练准确率达99.7%,验证准确率达98.9%。利用开发的油井工况智能诊断系统,在现场完成500余万次工况诊断,准确率达90%,报警推送及时,借助该系统开展油井生产管控更加合理高效,油井工况持续改善,连续稳定生产井比例由68%上升到88%,为油田大数据的高价值应用提供了有益示范。
Timely and accurate monitoring of the working conditions of oil wells is of great significance to the safe and efficient production of oilfields and the enhanced oil recovery.With the continuous deepening of oilfield informatization construction,real-time collection of dynamic monitoring data regarding oil well production such as indicator diagrams has been realized,and massive amounts of data have been accumulated and urgently need to be further explored and utilized.A new generation of artificial intelligence technology based on“big data+deep learning”is expected to break through the limitations of existing technologies and lead the upgrade of working condition diagnosis technology for oil wells.To this end,first,relying on more than 40 million sets of historical dynamic monitoring data covering oil wells in the different reservoirs,we prepared a large-scale dataset for working condition diagnosis of oil wells,which covered 5 categories and 37 different types of working conditions.On this basis,we selected the convolutional neural network algorithm and designed a personalized convolutional neural network(OWDNet)for working condition diagnosis of oil wells which contained more than 59 million learnable parameters in 26 layers.The OWDNet was trained using the above-mentioned working condition diagnosis dataset.After 10 epochs,the training accuracy was up to 99.7%,and the verification accuracy reached 98.9%.Furthermore,an intelligent working condition diagnosis system for oil wells was developed,and more than 5 million working condition diagnoses have been completed on site.The application accuracy of working condition diagnosis is 90%,and timely alarms are achieved. With this system,oil well production management and control were more reasonable and efficient,and working conditions of oil wells continued to improve. The proportion of continuous and stable production wells increased from 68% to 88%. The research provided a useful demonstration for the high-value application of oilfield big data.
作者
王相
杨耀忠
何岩峰
王振
窦祥骥
WANG Xiang;YANG Yaozhong;HE Yanfeng;WANG Zhen;DOU Xiangji(School of Petroleum Engineering,Changzhou University,Changzhou City,Jiangsu Province,213164,China;Information Management Center,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257000,China;Luming Oil and Gas Exploration and Development Co.,Ltd.,Dongying City,Shandong Province,257000,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第1期181-189,共9页
Petroleum Geology and Recovery Efficiency
基金
中国石化科技攻关项目“大数据技术在油田开发中的应用研究”(P20071)。
关键词
油井
工况诊断
深度学习
大数据
卷积神经网络
oil well
working condition diagnosis
deep learning
big data
convolutional neural network
