已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Deep Learning–Assisted Parameter Monitoring and Optimization in Rotary-Percussive Drilling

石油工程 钻探 地质学 计算机科学 工程类 机械工程
作者
Wucheng Sun,Yakun Tao,Zhiming Wang,Songcheng Tan,Longchen Duan,Xiaohong Fang
出处
期刊:Spe Journal [Society of Petroleum Engineers]
卷期号:: 1-14
标识
DOI:10.2118/221497-pa
摘要

Summary As an efficient method for hard rock fracturing, rotary-percussive drilling has been widely used in various scenarios, especially deep drilling. Drilling parameter monitoring and control are necessary to ensure stable and efficient underground drilling processes. However, this may be more difficult in deep, harsh conditions. In this paper, our goal is to establish models based on deep learning for drilling parameter monitoring and optimization. Combining impregnated diamond bits and granite rock samples, we conducted rotary-percussive rock drilling experiments using a rock drilling test rig. Real-time acoustic signals during rotary-percussive drilling were recorded, segmented, and transformed as spectra, which made up a drilling acoustic signal data set. Drilling parameters, including rotational speed (revolutions per minute, RPM), pump flow rate, pump pressure, weight on bit (WOB), torque, and rate of penetration (ROP), were logged in the meantime. Given the acoustic signal as input, we built 1D convolutional neural network (1D-CNN) models for drilling parameter prediction. The prediction results revealed the high efficiency and accuracy of 1D-CNN regression models based on deep learning in drilling condition monitoring. Batch normalization played an essential role in the regression model training processes. Given that these parameters have different units and dimensions, we compared models with different output modes to evaluate the multiparameter prediction performance of the 1D-CNN. Taking RPM, flow rate, pressure, and WOB as independent variables and torque and ROP as dependent variables, we developed a conditional variational autoencoder to realize optimization on drilling parameters based on expected drilling performance.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
bkagyin应助安安采纳,获得10
刚刚
刚刚
可可杨完成签到,获得积分10
1秒前
花无双完成签到,获得积分0
1秒前
2秒前
月yue完成签到,获得积分10
2秒前
丘比特应助贪玩的书包采纳,获得10
3秒前
北国雪未消完成签到 ,获得积分10
4秒前
可可杨发布了新的文献求助10
4秒前
6秒前
黄俊完成签到,获得积分10
6秒前
6秒前
Wzh发布了新的文献求助10
7秒前
konosuba完成签到,获得积分10
7秒前
刻苦大门完成签到 ,获得积分10
9秒前
黄俊发布了新的文献求助10
10秒前
11秒前
谨慎招牌完成签到,获得积分10
12秒前
艾文发布了新的文献求助10
13秒前
haoooooooooooooo完成签到,获得积分10
14秒前
果果完成签到 ,获得积分10
14秒前
谨慎招牌发布了新的文献求助10
15秒前
Jun完成签到 ,获得积分10
15秒前
英姑应助soda采纳,获得10
17秒前
Wzh完成签到,获得积分10
18秒前
朴实的哈密瓜数据线完成签到,获得积分10
20秒前
22秒前
24秒前
温子晴完成签到 ,获得积分20
25秒前
25秒前
26秒前
短岛发布了新的文献求助10
26秒前
南宫炽滔完成签到 ,获得积分10
29秒前
机智的小羊尾完成签到 ,获得积分10
29秒前
id完成签到,获得积分10
29秒前
29秒前
29秒前
糖糖糖发布了新的文献求助10
29秒前
30秒前
963完成签到,获得积分10
32秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Le dégorgement réflexe des Acridiens 800
Defense against predation 800
XAFS for Everyone (2nd Edition) 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3133798
求助须知:如何正确求助?哪些是违规求助? 2784777
关于积分的说明 7768435
捐赠科研通 2440073
什么是DOI,文献DOI怎么找? 1297175
科研通“疑难数据库(出版商)”最低求助积分说明 624888
版权声明 600791