找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Shale Analytics; Data-Driven Analytic Shahab D. Mohaghegh Book 2017 Springer International Publishing AG 2017 Data Mining of Shale Well Inf

[复制链接]
查看: 24174|回复: 49
发表于 2025-3-21 16:54:17 | 显示全部楼层 |阅读模式
书目名称Shale Analytics
副标题Data-Driven Analytic
编辑Shahab D. Mohaghegh
视频videohttp://file.papertrans.cn/867/866311/866311.mp4
概述Describes the use of artificial neural networks and fuzzy sets in petroleum engineering.Explains data mining in petroleum engineering.Demonstrates the only data driven reservoir modeling and productio
图书封面Titlebook: Shale Analytics; Data-Driven Analytic Shahab D. Mohaghegh Book 2017 Springer International Publishing AG 2017 Data Mining of Shale Well Inf
描述.This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering .Shale Analytics., it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations..
出版日期Book 2017
关键词Data Mining of Shale Well Information; Data-Driven Analytics; Hydraulic Fracturing in Shale; Production
版次1
doihttps://doi.org/10.1007/978-3-319-48753-3
isbn_softcover978-3-319-84008-6
isbn_ebook978-3-319-48753-3
copyrightSpringer International Publishing AG 2017
The information of publication is updating

书目名称Shale Analytics影响因子(影响力)




书目名称Shale Analytics影响因子(影响力)学科排名




书目名称Shale Analytics网络公开度




书目名称Shale Analytics网络公开度学科排名




书目名称Shale Analytics被引频次




书目名称Shale Analytics被引频次学科排名




书目名称Shale Analytics年度引用




书目名称Shale Analytics年度引用学科排名




书目名称Shale Analytics读者反馈




书目名称Shale Analytics读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 22:59:44 | 显示全部楼层
Modeling Production from Shale,Mitchell and his team of geologists and engineers began working on the shale challenge in 1981, trying different combinations of processes and technologies before ultimately succeeding in 1997.
发表于 2025-3-22 01:31:14 | 显示全部楼层
,Shale Analytics,The realization that much value can be extracted from the data that is routinely collected (and much of it is left unused).
发表于 2025-3-22 08:22:24 | 显示全部楼层
发表于 2025-3-22 12:23:38 | 显示全部楼层
发表于 2025-3-22 16:33:48 | 显示全部楼层
Extending the Utility of Decline Curve Analysis,Decline Curve Analysis (DCA) is one of the most utilized and probably the most simple (the least sophisticated) technique in petroleum engineering. DCA is a curve fitting technique that is used to forecast well’s production and to Estimate its Ultimate Recovery (EUR).
发表于 2025-3-22 19:03:00 | 显示全部楼层
发表于 2025-3-23 01:10:52 | 显示全部楼层
Shale Full Field Reservoir Modeling,A quick look at the history of reservoir simulation and modeling indicates that developing full field models (where all the wells in the asset are modeled together as one comprehensive entity) is the common practice for almost all prolific assets.
发表于 2025-3-23 03:52:03 | 显示全部楼层
Restimulation (Re-frac) of Shale Wells,Reference publications about refracturing treatments (re-stimulation) before 1990s are sparse. The first published work on re-frac dates back to 1960 [96], followed by another publication in 1973 [97].
发表于 2025-3-23 06:34:49 | 显示全部楼层
Shahab D. MohagheghDescribes the use of artificial neural networks and fuzzy sets in petroleum engineering.Explains data mining in petroleum engineering.Demonstrates the only data driven reservoir modeling and productio
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 吾爱论文网 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
QQ|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-8-5 14:05
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表