通过大数据分析提高卓越运营和产品可靠性

A. Mulunjkar, A. P. Deshpande, Stephen Claude Steinke, B. Chartier, Kuwertz Luke Alexander
{"title":"通过大数据分析提高卓越运营和产品可靠性","authors":"A. Mulunjkar, A. P. Deshpande, Stephen Claude Steinke, B. Chartier, Kuwertz Luke Alexander","doi":"10.4043/29513-MS","DOIUrl":null,"url":null,"abstract":"\n Schlumberger, one of the world’s leading suppliers of oilfield technology, is a measurement and data-driven company that collects massive amounts of data in the course of its daily operations. These data, diverse in nature, are collected for use in various business and technical workflows. The data can be downhole, surface, post-analysis, and support functions from manufacturing, maintenance, asset management, and finance. Analysis of this Big Data has the potential to drive a step change in operational performance across multiple dimensions. However, accomplishing this step change is not easy to accomplish because often, the data are not well structured and are scattered across individual business systems that do not communicate well with each other. Most of the analysis of these scattered data occurs on a point basis, requiring the significant involvement of various experts and complex time-consuming manipulations. The results are short lived in that they cannot be tracked in real time and the effort expended is not applicable to other data sets or problems. Increasing data volumes, data diversity, and demand from engineers to record multiple new data attributes during the product or technology life cycle further limits the benefits of such a spot analytics process, with potentially severe impacts on the business due to inadequate decision support or missed opportunities.\n This paper presents a developmental model and change processes, challenges faced and resolution approaches leading to digital transformation, and finally, the resulting value creation through building data visualizations and comprehensible decision-making tools.\n Once the initial high-value data sets and visualizations are identified, automation opportunities can be exploited. These data sets become the foundation for predictive analysis and machine learning through artificial intelligence (AI) and Internet of things (IoT) to further influence product performance and development in support of customer needs.","PeriodicalId":10948,"journal":{"name":"Day 2 Tue, May 07, 2019","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Operational Excellence and Product Reliability Enhancement Through Big Data Analytics\",\"authors\":\"A. Mulunjkar, A. P. Deshpande, Stephen Claude Steinke, B. Chartier, Kuwertz Luke Alexander\",\"doi\":\"10.4043/29513-MS\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Schlumberger, one of the world’s leading suppliers of oilfield technology, is a measurement and data-driven company that collects massive amounts of data in the course of its daily operations. These data, diverse in nature, are collected for use in various business and technical workflows. The data can be downhole, surface, post-analysis, and support functions from manufacturing, maintenance, asset management, and finance. Analysis of this Big Data has the potential to drive a step change in operational performance across multiple dimensions. However, accomplishing this step change is not easy to accomplish because often, the data are not well structured and are scattered across individual business systems that do not communicate well with each other. Most of the analysis of these scattered data occurs on a point basis, requiring the significant involvement of various experts and complex time-consuming manipulations. The results are short lived in that they cannot be tracked in real time and the effort expended is not applicable to other data sets or problems. Increasing data volumes, data diversity, and demand from engineers to record multiple new data attributes during the product or technology life cycle further limits the benefits of such a spot analytics process, with potentially severe impacts on the business due to inadequate decision support or missed opportunities.\\n This paper presents a developmental model and change processes, challenges faced and resolution approaches leading to digital transformation, and finally, the resulting value creation through building data visualizations and comprehensible decision-making tools.\\n Once the initial high-value data sets and visualizations are identified, automation opportunities can be exploited. These data sets become the foundation for predictive analysis and machine learning through artificial intelligence (AI) and Internet of things (IoT) to further influence product performance and development in support of customer needs.\",\"PeriodicalId\":10948,\"journal\":{\"name\":\"Day 2 Tue, May 07, 2019\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Tue, May 07, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4043/29513-MS\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, May 07, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4043/29513-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

斯伦贝谢是世界领先的油田技术供应商之一,是一家以测量和数据为导向的公司,在日常运营过程中收集大量数据。这些数据性质各异,被收集起来用于各种业务和技术工作流。数据可以来自井下、地面、后期分析以及制造、维护、资产管理和财务方面的支持功能。对这些大数据的分析有可能在多个维度上推动运营绩效的逐步变化。然而,完成这一步骤更改并不容易,因为数据通常没有很好地结构化,并且分散在各个业务系统中,这些业务系统之间不能很好地相互通信。对这些分散数据的大部分分析都是在点的基础上进行的,需要各种专家的大量参与和复杂耗时的操作。结果是短暂的,因为它们不能实时跟踪,并且所花费的精力不适用于其他数据集或问题。不断增加的数据量、数据多样性,以及工程师在产品或技术生命周期中记录多个新数据属性的需求,进一步限制了这种现场分析过程的好处,由于决策支持不足或错失机会,可能会对业务产生严重影响。本文介绍了数字化转型的发展模式和变革过程、面临的挑战和解决方法,以及通过构建数据可视化和可理解的决策工具所产生的价值创造。一旦确定了初始的高价值数据集和可视化,就可以利用自动化机会。这些数据集成为通过人工智能(AI)和物联网(IoT)进行预测分析和机器学习的基础,从而进一步影响产品性能和开发,以支持客户需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Operational Excellence and Product Reliability Enhancement Through Big Data Analytics
Schlumberger, one of the world’s leading suppliers of oilfield technology, is a measurement and data-driven company that collects massive amounts of data in the course of its daily operations. These data, diverse in nature, are collected for use in various business and technical workflows. The data can be downhole, surface, post-analysis, and support functions from manufacturing, maintenance, asset management, and finance. Analysis of this Big Data has the potential to drive a step change in operational performance across multiple dimensions. However, accomplishing this step change is not easy to accomplish because often, the data are not well structured and are scattered across individual business systems that do not communicate well with each other. Most of the analysis of these scattered data occurs on a point basis, requiring the significant involvement of various experts and complex time-consuming manipulations. The results are short lived in that they cannot be tracked in real time and the effort expended is not applicable to other data sets or problems. Increasing data volumes, data diversity, and demand from engineers to record multiple new data attributes during the product or technology life cycle further limits the benefits of such a spot analytics process, with potentially severe impacts on the business due to inadequate decision support or missed opportunities. This paper presents a developmental model and change processes, challenges faced and resolution approaches leading to digital transformation, and finally, the resulting value creation through building data visualizations and comprehensible decision-making tools. Once the initial high-value data sets and visualizations are identified, automation opportunities can be exploited. These data sets become the foundation for predictive analysis and machine learning through artificial intelligence (AI) and Internet of things (IoT) to further influence product performance and development in support of customer needs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Implementing the New INCOSE Systems Engineering Competency Framework Using an Evidence Based Approach for Oil and Gas Companies A Machine Learning Application for Field Planning Optimization of Well Start-Up Using Integrated Well and Electrical Submersible Pump Modeling The Subsea Sand Management Challenge – What to Do with the Sand? Systems Engineering Principles to Enable Supplier-Led Solutions
×
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