在场德雷斯顿第三届数字油田高端论坛暨第二届国际学术会议

二〇一三年三月18日-19日,参与了马尔默第三届数字油田高端论坛暨第二届国际学术会议,这次会议核心是大数额和数字油田2.0,插足此次会议的目标是想看看大数量这些定义怎样在油田领域中诞生,但很可惜与大数目有关的告诉或者与石油没有涉嫌,或者只是标题中有个大数据,但情节依旧关于数字油田建设举办。只有程国建的一篇介绍SPE数字能源大会的素材中讲到了一篇外国报告,可能介绍了大数据在油田可能的应用领域,回来再细致读读这篇材料。不管什么样,把多少个感兴趣的素材的关键思路整理了眨眼间间。

 

王璞(Google):大数目处理在google—-全球数据解析的章程技术

NoSQL,介绍了Google大数目处理架构中的多少个重大概念:MapReduce,Shuffle,BigTable/GFS,XFE(extensible
front end),protocol buffers & stubby(通讯协议,可将protocol
buffer转换为程序代码)。

NoSQL 1

 

高灯亮(美利坚同盟国西佛尼亚大学):地震属性在油藏描述中的三维可视化技术

告知相比较明晰,首要就讲了三有的情节:Reservoir structures构造,Reservoir
facies相,Reservoir
properties属性,对于她的性能分析的算法不打听,但他把不同的相facies用不同的水彩和透明度来三维表示,一些三维可视化的机能仍然值得大家上学的。

石玉江(长庆油田):大数额与油藏数字化—-油气藏数字化协同研商和裁定补助平台的建设与展望

是长庆油田一个6年项目标进展报告,不评价,提到的多少个技巧:业务流程标准化,SOA框架,服务总线,数据链技术,数据整合,专业软件接口,地质图件导航,远程传输,协同网络化探讨环境。

 

张志檩:从两化融合看数字石油石化的技艺内涵

告诉中有无数海外的资料,可惜时间有限每张片子都过得迅速,报告也不让拷贝。

 

张海:大数目处理的总结办法—-油田大数目分析方法

介绍了大数量的部分定义,举了2个例证,一个是应酬网络的,一个是与猴子有关的,很不满都与油田没有涉及。

 

张文坡:资水油田的油田大数目建设与展望

讲的情节重点是七个“大”,大数额基本(数据建设)、大总结(云桌面)、大存储(盘阵)、大网络、大连串(数据库集群)、大应用(油气水井生产数据管理、生产调度指挥、物联网、ERP、集团门户)。

程国建:数字油田国际动态—-2013 SPE数字能源大会专题介绍

20分钟概览了SPE大会的根本内容,我根本感兴趣的:

SPE-163718,Digital Oil Field Experience: An Overview and a Case Study

斯伦贝谢的一个数字油田案例实施的经验和教训

SPE-163709,Design of an Automated Drilling Prediction System –
Strengthening While-Drilling Decision Making
自动钻井预测系统的设计—-强化随钻决策

SPE-163683,The Roadmap for Industry Adoption of the PRODML Standard

PRODML标准的工业布局路线图

NoSQL 2

SPE-163717,大数额大买卖 Examples of Work with Big Data

Work on the application of Big Data and analytics in the oil and gas
industry is in the experimental stage. Much of the work centers on
data-intensive computing and how I/O data loading can be managed most
efficiently. Such as:

• Use of Hadoop to process seismic data. Chevron is using Hadoop as one
of the 25 steps in the workflow for the identification of reservoirs.
Processed data is fed into high-performance computing models. The
project uses the IBM BigInsights technology, which includes the Hadoop
component stack.

• Use of Hadoop in the cloud. Royal Dutch Shell is piloting Hadoop in a
private Amazon cloud.

• Production data for performance forecasting. One oil and gas company
is experimenting with the time series analysis of production data. Aging
wells where the forecast does not meet a predetermined production
threshold are flagged for immediate remediation.

• Investigation of two MapReduce approaches applied to drilling data. In
this experiment, Chukwa, an open source data collection system built on
top of Hadoop, was found to be a preferable approach to a Hadoop
distributed file system when working with large files.

• Storing and processing seismic data in a Hadoop cluster. Cloudera, a
company that provides a data platform built on top of Apache Hadoop, has
initiated a project called Seismic Hadoop project.

• Seismic and drilling data in the cloud. PointCross Inc. has introduced
two cloud-based offerings for the oil and gas industry — a seismic
server and data repository that uses NoSQL and Hadoop technologies to
store and manage SEG Y files and a drilling data server and data
repository that can accept WITSML, LAS, and WITS formats.

大数量在油气领域的也许应用点Possible uses of Big Data and analytics in
the oil and gas industry

• Exploration — By applying advanced analytics, such as 形式识别pattern
recognition, to a more comprehensive set of data collected during
seismic acquisition, geologists may be able to identify potentially
productive seismic trace signatures that have been overlooked.

• Development — Big Data and analytics could aid oil and gas companies
in acreage assessment and prospect generation. Analytics applied to
geospatial data, news feeds, oil and gas reports, or other syndicated
feeds could provide competitive intelligence on where to submit bids for
leases.

• Drilling — Beyond monitoring and alerting based on limited data, Big
Data and analytics could be applied to real-time “big” drilling data to
identify anomalies based on multiple conditions or predict the
likelihood of drilling success.

• Production Operations — Enhancing oil recovery from existing wells is
an objective of many oil and gas companies. Analytics applied to a
variety of Big Data at once — seismic, drilling, and production data —
could help reservoir engineers map changes in the reservoir over time
and provide decision support to production engineers for making changes
in lifting methods. This type of approach could also be used to guide
fracking in shale gas plays.

• Maintenance — Predictive maintenance is not a new concept for the oil
and gas industry, although if you ask a maintenance executive, it does
not get the attention and budget it deserves. In upstream, if pressure,
volume, and temperature can be collected and analyzed together and
compared with the past history of equipment failure on a compressor, for
example, then alerts can be automated. The same type of situation is
found in midstream pipelines. This would be useful in cases where time
to condition detection to failure is short and where assets are
considered critical to the operation or failure would have a significant
impact on health, safety, and environment.

 

刘志忠(大港油田):深化数字油田建设,推进数字油田发展

稍许相比实际的认识,不一味地追求技术的先进性,把一部分技巧概念要真的落地。数据标准与不断投入才能保证数字油田的无休止前进。

NoSQL 3

甘腊梅:延长油田数字油田建设与数字化协同工作平台

内部有张图竟然用了我们在二零零六年四月照的一张勘探项目部会议室的相片,里面是于总等人召开生产研究会时的背影。

 

再后边的报告根本是一些大会赞助商的产品与广告。

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