Modeling the downhole density of drilling muds using multigene genetic programming

IF 2.6 Q3 ENERGY & FUELS Upstream Oil and Gas Technology Pub Date : 2021-02-01 DOI:10.1016/j.upstre.2020.100030
Okorie Ekwe Agwu , Julius Udoh Akpabio , Adewale Dosunmu
{"title":"Modeling the downhole density of drilling muds using multigene genetic programming","authors":"Okorie Ekwe Agwu ,&nbsp;Julius Udoh Akpabio ,&nbsp;Adewale Dosunmu","doi":"10.1016/j.upstre.2020.100030","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a </span>mean absolute error<span> of 0.0246 and the square of correlation coefficient (R</span></span><sup>2</sup>) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R<sup>2</sup> of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.</p></div>","PeriodicalId":101264,"journal":{"name":"Upstream Oil and Gas Technology","volume":"6 ","pages":"Article 100030"},"PeriodicalIF":2.6000,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.upstre.2020.100030","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Upstream Oil and Gas Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266626042030030X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 4

Abstract

The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R2) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R2 of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于多基因遗传规划的钻井泥浆井下密度建模
本文的主要目的是通过对井下压力、温度和初始泥浆密度的实验测量,利用多基因遗传编程来预测井下密度。结果表明,WBM密度模型的均方误差为0.0012,平均绝对误差为0.0246,相关系数平方(R2)为0.9998;而OBM的MSE为0.000359,MAE为0.01436,R2为0.99995。在评估OBM模型的泛化能力时,模型的MSE为0.031,MAE为0.138,平均绝对百分比误差(MAPE)为0.95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.50
自引率
0.00%
发文量
0
期刊最新文献
Dynamics of pump jacks with theories and experiments Well perforating—More than reservoir connection A new method for predicting casing wear in highly deviated wells using mud logging data Experimental investigation of bypassed-oil recovery in tight reservoir rock using a two-step CO2 soaking strategy: Effects of fracture geometry A Review of Modern Approaches of Digitalization in Oil and Gas Industry
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1