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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-management-in-oil-and-gas-industry/
课程评论：没有评论
课程名称：石油和天然气行业的数据管理 概述：本课程旨在帮助参与者深入了解地面和地下数据管理的整体数据流原理，详细讲解数据管理中的黄金规则、地质、地震、导航等各类数据的分类、勘探与生产（E&P）数据库设计，以及地质与地球物理(G&G)软件的应用。课程中的重要观点通过图表和示例进行了生动阐释。随着油气行业日益增长的数据规模，目前存储容量已达到千亿兆字节（petabytes）级别，因此实施数据治理的必要性变得更加突出。有效的一致的数据治理确保了运营的成功，以及长期的成本与时间节约。本课程揭示了数据在E&P公司中的价值，并回顾了常见改进领域的重要主题。所有E&P公司都在利用现有的数据管理创造价值，关键问题是其现有能力是否具备扩展的商业案例。课程侧重于与地下相关的数据分析生成的信息，从勘探数据（如地震勘测）到生产数据（如每小时流量读数），以及从原始测量（如原始日志读数）到解释结果（如动态油藏模型）。石油公司在数据上的巨额投资，主要目的在于降低“开发不确定性”。 课程的最后结论是，所有石油公司员工应仔细审视当前的数据管理任务与责任。在大多数公司中，存在扩展数据治理、访问权限、安全性或质量的机会，这将显著提升组织的整体盈利能力。整个课程采用非正式、互动的交流方式，主题通过实施实际案例研究和动手练习加以覆盖。在视频录制中，讲师会提出问题，鼓励学员在纸上写下自己的回答，并进行自我检查。 讲师介绍：Serdar Kaya先生是一位高级顾问，在地球科学、数据分析、油藏表征、地质建模以及多种高科技应用方面具有丰富经验。他在创新数据建模方法方面发表了多篇期刊和会议论文，并成功培训、指导和辅导了许多地球科学家、地质学家和工程师。他拥有石油工程的硕士和学士学位，卓越的成就和高水平的技术能力不仅反映了他的工程知识，还展现了其高度的个人承诺和动力。
In this course, the content is going to leverage participants understanding the total data flow principles of surface and subsurface data management by going through golden rules in detail, various data categories in geology, seismic, navigation, E & P database design, knowing G & G software. All the important points covered are also illustrated by figures and tables with examples given.In all disciplines of oil and gas industry, acquired data is growing exponentially day by day. Currently, storage capacities are in the size of petabytes which is equivalent to ~1000TB (One thousand Terabytes). Thus, the need to perform data governance is cumbersome but inevitable. A strong and consistent data governance ensures operational success, long-term cost and time savings. This course reveals the value that data generates within E & P companies. It then reviews the most important themes that the areas where improvements are commonly can be found. All E & P companies are generating value with their existing data management, the important question is whether there are compelling business cases to expand on their current capabilities.This course is focused on the information generated through data analytics related to the subsurface. This data ranges from exploration data, such as seismic surveys to production data, such as hourly flow readings, and from objective measurements, such as raw log readings to interpreted results such as dynamic reservoir models. The key reason that oil companies spends millions on data is in order to reduce the "development uncertainties".The final conclusion is that all oil company personnel should carefully review their current data management tasks and responsibilities. In most companies there are opportunities to expand the governance, access, security or quality of data which would significantly increase the total value an organizational profitablity.Throughout the course, you will find communication medium informal, interactive and the topics are covered by implementation of practical case studies/hands on exercises. Every time in video recordings I ask questions, try to write your own reply on a pieces of paper and check yourself. InstructorMr. Serdar Kaya is a senior consultant with an extensive experience in geoscience, data analytics, reservoir characterization, geological modeling and various high tech applications. He has published several journal and conference papers about innovative data modeling approaches for challenging issues. He has also successfully trained, mentored and coached many geoscientists, geologist and engineers. He holds both MSc and BSc degrees in Petroleum Engineering. His achievements and high level of technical competence are a reflection not only his engineering knowledge but also high level of personnel commitment and drive.
