T. Olneva, D. Kuzmin, S. Rasskazova, A. Timirgalin
{"title":"Big Data Approach for Geological Study of the Big Region West Siberia","authors":"T. Olneva, D. Kuzmin, S. Rasskazova, A. Timirgalin","doi":"10.2118/191726-MS","DOIUrl":null,"url":null,"abstract":"\n Big Data technologies are now being actively integrated into the oil and gas sector owing to the need to improve operational efficiency and to optimize a variety of processes. Successful projects in data processing automation have already been implemented, for example, new breakthroughs are expected in digital field modelling projects /1/.\n Geological and geophysical information accumulated over decades of studies in oil and gas bearing basins and fields development is a huge amount of data; Big Data approaches can be effectively applied to them, such as data mining, predictive analytics, training of a system on the reference objects. 3D seismic data is a classic example of Big Data. Their interpretation conventionally involves approaches based on Neural Networks, various classification and clustering algorithms /2/.\n According to the experts, the West Siberian Petroleum Basin being a holistic system, has unique properties such as existence of giant and unique hydrocarbon accumulations /3/. The potential of the basin has not yet been determined. The authors focused their attention on the Achimov play. Applying the Big Data approach to a regional database may allow establishing new patterns in fields distribution and will contribute to the development of new unique exploration criteria.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Mon, September 24, 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/191726-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

由于需要提高作业效率和优化各种流程,大数据技术正在积极融入石油和天然气行业。数据处理自动化方面的成功项目已经实施,例如,数字现场建模项目有望取得新的突破。经过几十年的含油气盆地和油气田开发研究积累的地质地球物理信息是海量的数据;大数据方法可以有效地应用于它们,例如数据挖掘、预测分析、基于参考对象的系统训练。三维地震数据是大数据的一个经典例子。它们的解释通常涉及基于神经网络、各种分类和聚类算法的方法。据专家介绍,西西伯利亚盆地是一个整体系统,具有独特的性质,如存在巨大而独特的油气聚集。该盆地的潜力尚未确定。作者们把注意力集中在阿奇莫夫的戏剧上。将大数据方法应用于区域数据库可以建立新的油田分布模式,并有助于开发新的独特勘探标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data Approach for Geological Study of the Big Region West Siberia
Big Data technologies are now being actively integrated into the oil and gas sector owing to the need to improve operational efficiency and to optimize a variety of processes. Successful projects in data processing automation have already been implemented, for example, new breakthroughs are expected in digital field modelling projects /1/. Geological and geophysical information accumulated over decades of studies in oil and gas bearing basins and fields development is a huge amount of data; Big Data approaches can be effectively applied to them, such as data mining, predictive analytics, training of a system on the reference objects. 3D seismic data is a classic example of Big Data. Their interpretation conventionally involves approaches based on Neural Networks, various classification and clustering algorithms /2/. According to the experts, the West Siberian Petroleum Basin being a holistic system, has unique properties such as existence of giant and unique hydrocarbon accumulations /3/. The potential of the basin has not yet been determined. The authors focused their attention on the Achimov play. Applying the Big Data approach to a regional database may allow establishing new patterns in fields distribution and will contribute to the development of new unique exploration criteria.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信