[1]刘 振,李 黎,汪生好,等.开发阶段油田研究工作中大数据模型的建立与应用[J].华南地震,2021,41(04):138-144.[doi:10.13512/j.hndz.2021.04.19]
 LIU Zhen,LI Li,WANG Shenghao,et al.The Establishment and Application of Big Data Model in Oil Field Research Work in Development Stage[J].,2021,41(04):138-144.[doi:10.13512/j.hndz.2021.04.19]
点击复制

开发阶段油田研究工作中大数据模型的建立与应用()
分享到:

华南地震[ISSN:1006-6977/CN:61-1281/TN]

卷:
41
期数:
2021年04期
页码:
138-144
栏目:
土木工程防震减灾
出版日期:
2021-12-15

文章信息/Info

Title:
The Establishment and Application of Big Data Model in Oil Field Research Work in Development Stage
文章编号:
1001-8662(2021)04-0138-07
作者:
刘 振李 黎汪生好杨小江王要森徐 超
中海石油(中国)有限公司深圳分公司,深圳 518000
Author(s):
LIU ZhenLI LiWANG ShenghaoYANG XiaojiangWANG YaosenXU Chao
Shenzhen Branch of China National Offshore Oil Corporation , Shenzhen 518000, China
关键词:
信息挖掘数据模型钻井深度测量误差滚动评价
Keywords:
Data miningDigital modelDepth measurement error between wellsRolling evaluation
分类号:
TE4
DOI:
10.13512/j.hndz.2021.04.19
文献标志码:
A
摘要:
针对如何充分利用地质油藏数据挖掘价值信息并指导油田开发研究工作,进行了数据模型构建及应用探索,建立了两个数据模型:将平面控制点密集的构造图与纵向数据点密集的井点分层数据结合,建立三维钻井—构造高程差数据模型辅助构造变化分析,以描述任意空间位置的构造变化特征;将录井显示、测井解释数据与油气成藏特征进行联系,建立油气运移—成藏数据模型,以辅助油气运移成藏规律分析。将构造高程差数学模型应用于开发井随钻跟踪工作中,成功识别深度测量数据异常,有效避免了开发井侧钻;将油气运移—成藏数据模型应用于成熟油区周边滚动评价研究中,有效获得研究区浅层断层侧向封堵的认识,指导了研究区一个小型断块圈闭的评价和钻探并获得成功。
Abstract:
The paper focuses on how to make full use of geological oil reservoir data mining value information, also guide oilfield development and research work, and conducts data model construction and application exploration. Two data models are established:by combining the structure map with dense horizontal control points and the layered data of well points with dense vertical data points,a three-dimensional drilling-structure elevation difference data model is established to assist the structural changes analysis,and to describe the structural change characteristics of any spatial location. The logging display and logging interpretation data are linked with the characteristics of oil and gas accumulation, and the data model of oil and gas migration-accumulation is established to assist in the analysis of the law of oil and gas migration and accumulation. The mathematical model of structural elevation difference is applied to the tracking during drilling of development wells, and the abnormal depth measurement data are successfully identified, which effectively avoids sidetracking of development wells;the drilling-tectonic elevation difference data model is applied to the rolling evaluation study around mature oil regions,which effectively obtains the understanding of the lateral sealing of shallow faults in the study area,and successfully guides the evaluation and drilling of a small fault block trap in the study area.

参考文献/References:

[1] Borowski M. How conocophillips solved its big data problem [J]. JPT,2018,70(7):16-26.
[2] Lashari S E,Takbiri-Borujeni A,Fathi E,et al. Drilling performance monitoring and optimization: a data-driven approach[J]. Journal of Petroleum Exploration and Production Technology,2019,9(4):2747-2756.
[3]杨剑锋,杜金虎,杨勇,等.油气行业数字化转型研究与实践[J].石油学报,2021,42(2):248-258.
[4]匡立春,刘合,任义丽,等.人工智能在石油勘探开发领域的应用现状与发展趋势[J].石油勘探与开发,2021,48(1):1-11.
[5]张凯,赵兴刚,张黎明,等.智能油田开发中的大数据及智能优化理论和方法研究现状及展望[J].中国石油大学学报(自然科学版),2020,44(4):28-38.
[6]赵改善.石油物探数字化转型之路:走向实时数据采集与自动化处理智能化解释时代[J].石油物探,2021,60(2):175-189.
[7]李阳,薛兆杰.中国石化油藏地球物理技术进展与探讨[J].石油物探,2020,59(2):159-168.
[8]李剑峰.智慧石化建设:从信息化到智能化[J].石油科技论坛,2020,39(1):34-42.
[9]刘卓,张宇,张宏洋.国内外数字油田技术发展趋势及策略[J].石油科技论坛,2020,39(4):62-67.
[10]陈溯,安鹏,吴刚,等.海上智能油田建设研究[J].石油科技论坛,2020,39(5):16-23.
[11]李志红.基于大数据技术的智慧油田发展现状及思考[J].中国管理信息化,2020,23(10):97-98.
[12] Al-Anazi A, Gates I. A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs[J]. Engineering Geology,2010,114 (3):267-277.
[13] Chauhan S,Ruhaak W,Khan F,et al. Processing of rock-core microtomography images using seven different machine learning algorithms[J]. Computers&Geosciences,2016(86) 120-128.
[14]李大伟,石广仁.油气勘探开发常用数据挖掘算法优选[J].石油学报,2018,39(2):240-246.
[15]罗建民,王晓伟,张琪,等.地质大数据方法在区域找矿靶区定量优选中的应用[J].地学前缘,2019,26(4):76-83.
[16] Yili Ren,Renbin Gong,Zhou Feng,et al. Valuable Data Extraction for Resistivity Imaging Logging Interpretation[J]. Tsinghua Science and Technology,2020,25(02):281-293.
[17]于荣泽,丁麟,郭为,等.大数据在油气勘探开发中的应用——以川南页岩气田为例[J].矿产勘查,2020,11(9):2000-2007.
[18]王奭.基于大数据的高效数据挖掘算法及应用[J].信息与电脑,2020,1(19):48-49.
[19]邬阳阳,汤建国.大数据背景下粗糙集属性约简研究进展[J].计算机工程与应用,2019,55(6):31-38.
[20]徐鹏,高健祎,陈溯,等.勘探开发数据资产化管理实践与思考[J].石油科技论坛,2020,39(5):34-40.

备注/Memo

备注/Memo:
收稿日期:2021-04-13
作者简介:刘振(1987-),男,工程师,主要从事油藏地球物理、油田开发与开采研究。E-mail:dreamsoflz@163.com
更新日期/Last Update: 2021-12-15