亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Prediction and visualization analysis of drilling energy consumption based on mechanism and data hybrid drive

机制(生物学) 可视化 能源消耗 钻探 消费(社会学) 计算机科学 数据可视化 能量(信号处理) 工程类 数据挖掘 机械工程 统计 数学 电气工程 社会科学 哲学 认识论 社会学
作者
Kangping Gao,Xinxin Xu,Shengjie Jiao
出处
期刊:Energy [Elsevier BV]
卷期号:261: 125227-125227 被引量:7
标识
DOI:10.1016/j.energy.2022.125227
摘要

To obtain an accurate and reliable energy consumption (EC) prediction model, and to quantify the relationship between drilling power, EC, and energy efficiency. An EC prediction model and multi-angle visualization analysis method driven by mechanism and data are proposed. Firstly, the power and energy models of each stage of the drilling rig are established through detailed power flow theory. Additionally, based on the deviation between the actual EC results and the theoretical mechanism model calculation results, a least squares support vector machine (LSSVM) data compensation model is established, and the LSSVM model parameters are optimized by the improved whale optimization algorithm; after that, multi-angle visualization analysis of energy parameters was performed by drilling power histogram, energy efficiency ring diagram, energy sequence diagram, and energy bubble diagram. Finally, the experiment of curb drilling shows that the prediction error of the hybrid drive model is 2.44%. Compared with the prediction results of the mechanism model and the data-driven model, the average prediction error is reduced by 0.76% and 2.25%, which verifies the high efficiency of the hybrid-driven model. Also, through the multi-angle visualization analysis of energy parameters, the drilling energy saving is 2127.4kJ, and the energy efficiency is improved by 26.71%. • A mechanism analysis and data-driven integrated drilling energy consumption prediction model is established. • The IWOA algorithm was used to optimize the LSSVM parameters. • A multi-angle visual analysis of the energy at each stage of drilling is carried out. • The relationship between drilling power, load energy consumption, and load energy efficiency is explored. • The influence of drilling rig working parameters on drilling energy parameters is studied.
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
温暖元容完成签到,获得积分10
3秒前
scholar丨崔发布了新的文献求助10
7秒前
13秒前
淡定的彩虹完成签到,获得积分10
18秒前
22秒前
sss发布了新的文献求助10
26秒前
33秒前
情怀应助科研通管家采纳,获得10
34秒前
34秒前
卡卡完成签到 ,获得积分10
36秒前
CNY完成签到 ,获得积分10
45秒前
赘婿应助_ban采纳,获得10
1分钟前
1分钟前
bksqc发布了新的文献求助10
1分钟前
bksqc完成签到,获得积分10
1分钟前
卑微学术人完成签到 ,获得积分10
1分钟前
1分钟前
无花果应助feifei采纳,获得10
2分钟前
griffon完成签到,获得积分10
2分钟前
汉堡包应助通通采纳,获得10
2分钟前
2分钟前
Huay完成签到 ,获得积分10
2分钟前
tingting9发布了新的文献求助10
2分钟前
英俊的铭应助科研通管家采纳,获得10
2分钟前
2分钟前
kd1412应助科研通管家采纳,获得10
2分钟前
善学以致用应助吴昕昕采纳,获得10
2分钟前
tingting9完成签到,获得积分10
2分钟前
吾日三省吾身完成签到,获得积分10
2分钟前
sola完成签到 ,获得积分10
2分钟前
DreamMaker完成签到 ,获得积分10
3分钟前
VVV完成签到,获得积分10
3分钟前
3分钟前
3分钟前
_ban发布了新的文献求助10
3分钟前
小黄鸭完成签到,获得积分10
3分钟前
滴滴哒发布了新的文献求助10
3分钟前
李李原上草完成签到 ,获得积分10
3分钟前
超级的千青完成签到 ,获得积分10
3分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
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 3990020
求助须知:如何正确求助?哪些是违规求助? 3532077
关于积分的说明 11256276
捐赠科研通 3270943
什么是DOI,文献DOI怎么找? 1805139
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809228