A Working Condition Diagnosis Model of Sucker Rod Pumping Wells Based on Deep Learning

抽油杆 卷积神经网络 联营 油井 人工智能 深度学习 测功机 蜗壳 计算机科学 数据集 模式识别(心理学) 集合(抽象数据类型) 工程类 石油工程 机械工程 程序设计语言 入口
作者
Xiang Wang,Yanfeng He,Fajun Li,Zhen Wang,Xiangji Dou,Hanlin Xu,Lipei Fu
出处
期刊:SPE production & operations [Society of Petroleum Engineers]
卷期号:36 (02): 317-326 被引量:6
标识
DOI:10.2118/205015-pa
摘要

Summary Monitoring the working conditions of sucker rod pumping wells in a timely and accurate manner is important for oil production. With the development of smart oil fields, more and more sensors are installed on the well, and the monitored data are continuously transmitted to the data center to form big data. In this work, we aim to utilize the big data collected during oil well production and a deep learning technique to build a new generation of intelligent diagnosis model to monitor working condition of sucker rod pumping wells. More than 5×106 of well monitoring records, which covers information from about 1 year for more than 300 wells in an oilfield block, are collected and preprocessed. To show the dynamic changes of the working conditions for the wells, the overlay dynamometer card is proposed and plotted for each data record. The working conditions are divided into 30 types, and the corresponding data set is created. An intelligent diagnosis model using the convolutional neural network (CNN), one of the deep learning frameworks, is proposed. By the convolution and pooling operation, the CNN can extract features of an image implicitly without human effort and prior knowledge. That makes a CNN very suitable for the recognition of the overlay dynamometer cards. The architecture for a working condition diagnosis CNN model is designed. The CNN model consists of 14 layers with six convolutional layers, three pooling layers, and three fully connected layers. The total number of neurons is more than 1.7×106. The overlay dynamometer card data set is used to train and validate the CNN model. The accuracy and efficiency of the model are evaluated. Both the training and validation accuracies of the CNN model are greater than 99% after 10 training epochs. The average training elapsed time for an epoch is 8909.5 seconds, and the average time to diagnosis a sample is 1.3 milliseconds. Based on the trained CNN model, a working condition monitoring software for a sucker rod pumping well is developed. The software runs 7 × 24 hours to diagnosis the working conditions of wells and post a warning to users. It also has a feedback learning workflow to update the CNN model regularly to improve its performance. The on-site run shows that the actual accuracy of the CNN model is greater than 90%.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
2秒前
3秒前
3秒前
CR7应助越啊采纳,获得20
4秒前
李健应助123采纳,获得10
5秒前
Jun完成签到,获得积分10
6秒前
大反应釜发布了新的文献求助10
6秒前
淳于安筠发布了新的文献求助30
9秒前
柳叶洋完成签到,获得积分10
9秒前
下一秒发布了新的文献求助10
12秒前
13秒前
14秒前
无花果应助科研通管家采纳,获得10
14秒前
Akim应助科研通管家采纳,获得10
14秒前
852应助科研通管家采纳,获得10
14秒前
SYLH应助科研通管家采纳,获得30
14秒前
斯文败类应助科研通管家采纳,获得10
14秒前
Owen应助科研通管家采纳,获得10
14秒前
orixero应助科研通管家采纳,获得10
14秒前
Orange应助科研通管家采纳,获得10
14秒前
情怀应助科研通管家采纳,获得30
14秒前
Jasper应助科研通管家采纳,获得10
15秒前
英俊的铭应助科研通管家采纳,获得10
15秒前
15秒前
Ava应助科研通管家采纳,获得10
15秒前
情怀应助科研通管家采纳,获得10
15秒前
15秒前
15秒前
15秒前
15秒前
SYLH应助shensiang采纳,获得20
16秒前
专一的从波完成签到 ,获得积分10
16秒前
领导范儿应助msjs采纳,获得30
16秒前
量子星尘发布了新的文献求助10
18秒前
安静的幼旋完成签到,获得积分10
19秒前
19秒前
白石溪发布了新的文献求助10
19秒前
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989444
求助须知:如何正确求助?哪些是违规求助? 3531531
关于积分的说明 11254250
捐赠科研通 3270191
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174