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
黄花完成签到 ,获得积分10
刚刚
刘珍荣完成签到,获得积分10
1秒前
1秒前
紫金之巅完成签到 ,获得积分10
1秒前
Gang完成签到,获得积分10
2秒前
3秒前
3秒前
4秒前
CYYDNDB完成签到 ,获得积分10
4秒前
粿粿一定行完成签到 ,获得积分10
5秒前
6秒前
战战完成签到,获得积分10
7秒前
xlk2222完成签到,获得积分10
10秒前
笨笨以莲完成签到,获得积分10
10秒前
YHX完成签到,获得积分10
11秒前
沐沐心完成签到 ,获得积分10
12秒前
12秒前
13秒前
哭泣笑柳发布了新的文献求助10
13秒前
轻松白桃完成签到,获得积分10
13秒前
JasVe完成签到 ,获得积分10
16秒前
wakkkkk完成签到,获得积分10
16秒前
含蓄听南完成签到,获得积分10
16秒前
HH给HH的求助进行了留言
16秒前
芋你呀完成签到,获得积分10
17秒前
西蓝花香菜完成签到 ,获得积分10
17秒前
无花果应助兔子采纳,获得10
17秒前
请勿继续完成签到,获得积分10
19秒前
搞怪的婴完成签到,获得积分10
20秒前
Loooong完成签到,获得积分0
20秒前
22秒前
fuguier完成签到,获得积分10
24秒前
大橙子发布了新的文献求助10
24秒前
王旭完成签到,获得积分10
26秒前
轻松白桃发布了新的文献求助10
26秒前
26秒前
Distance发布了新的文献求助10
27秒前
鲤鱼青雪完成签到,获得积分10
27秒前
耳机单蹦完成签到,获得积分10
28秒前
苻醉山完成签到 ,获得积分0
28秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038184
求助须知:如何正确求助?哪些是违规求助? 3575908
关于积分的说明 11373872
捐赠科研通 3305715
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022