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
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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。
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.
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