高含水油藏流线场表征与评价方法

doi:10.11885/j.issn.1674-5086.2020.10.22.02

西南石油大学学报(自然科学版) ›› 2023, Vol. 45 ›› Issue (1): 89-96.DOI: 10.11885/j.issn.1674-5086.2020.10.22.02

高含水油藏流线场表征与评价方法

柳朝阳¹, 郭奇^2,3, 李刚¹, 黄博¹, 王振宇¹

1. 延长油田股份有限公司勘探开发技术研究中心, 陕西延安 716000;
2. 中国石化胜利油田分公司勘探开发研究院, 山东东营 257015;
3. 中国石化胜利石油管理局有限公司博士后科研工作站, 山东东营 257000

收稿日期:2020-10-22 发布日期:2023-02-24
通讯作者: 郭奇,E-mail:qqqqguoqi@163.com
作者简介:柳朝阳,1986年生,男,汉族,陕西咸阳人,高级工程师,硕士,主要从事油田开发方面的研究。E-mail:358553108@qq.com
郭奇,1988年生,男,汉族,山东东营人,副研究员,博士,主要从事油气田开发方面的研究。E-mail:qqqqguoqi@163.com
李刚,1986年生,男,汉族,陕西米脂人,高级工程师,硕士,主要从事油田开发方面的研究。E-mail:348569954@qq.com
黄博,1984年生,男,汉族,陕西延安人,高级工程师,硕士,主要从事油田开发方面的研究。E-mail:308785292@qq.com
王振宇,1988年生,男,汉族,陕西延安人,工程师,硕士,主要从事油田开发方面的研究。E-mail:2580939572@qq.com
基金资助:
国家科技重大专项（2011ZX05011）；长江学者和创新团队发展计划（IRT1294）

Characterization and Evaluation Method of Streamline Field in High Water Cut Reservoir

LIU Chaoyang¹, GUO Qi^2,3, LI Gang¹, HUANG Bo¹, WANG Zhenyu¹

1. Exploration and Development Technology Research Center, Yanchang Oilfield Co. Ltd., Yan'an, Shaanxi 716000, China;
2. Exploration and Development Research Institute, Shengli Oilfield Company, SINOPEC, Dongying, Shandong 257015, China;
3. Post-doctoral Scientific Research Workstation of SINOPEC Shengli Petroleum Management Co. Ltd., Dongying, Shandong 257000, China

Received:2020-10-22 Published:2023-02-24

摘要/Abstract

摘要： 通过提取数值模拟得到的流线场内质点的空间坐标及属性数据，建立流线簇流量、流线簇潜力和流线簇含油率的油藏流线场表征方法，应用密度峰值算法对流线场进行聚类分级评价，并通过S_Dbw系数验证划分流线等级的聚类效果，最终形成高含水期油藏流线场定量表征与评价的技术方法。结果表明，利用流线簇流量、流线簇潜力及流线簇含油率等3参数对流线场进行表征较常规方法更能反映注采井间流量及潜力的分布关系和大小，通过表征参数的聚类分级定量确定不同区域的流线强度等级。将流线场表征与评价方法应用于某东部油田实际区块，整个流线场被划分为14类，各区域驱替强度差异较大，通过流线场重构，调整前后流线场等级由14类变为7类，流动非均质性减弱，油藏动用程度明显改善。

关键词: 密度峰值算法, 机器学习, 流线场, 均衡驱替, 流线簇

Abstract: By extracting the spatial coordinates and attribute data of particles in streamline field obtained through numerical simulation, we established a characterization method of streamline field with streamline cluster flow rate, streamline cluster potential and streamline cluster oil content. The streamline field is classified and evaluated by density peak algorithm, and the clustering effect of streamline classification is verified by S_Dbw coefficient. Finally the method of quantitative characterization and evaluation of streamline field in high water cut reservoir is formed. The results show that the three parameters can better reflect the distribution relationship and size of flow rate and potential between injection and production wells than conventional methods. Streamline strength grades in different regions can be quantitatively determined by clustering and grading of characterization parameters. The method is applied to an actual block in an eastern oilfield. The whole streamline field is divided into 14 types. The displacement intensity of each region is quite different. Through streamline field reconstruction, the streamline field grade are adjusted from 14 types to 7 types, the flow heterogeneity is weakened, and the reservoir productivity is improved obviously.

Key words: density peak algorithm, machine learning, streamline field, equilibrium displacement, streamline cluster

中图分类号:

TE345

柳朝阳, 郭奇, 李刚, 黄博, 王振宇. 高含水油藏流线场表征与评价方法[J]. 西南石油大学学报(自然科学版), 2023, 45(1): 89-96.

LIU Chaoyang, GUO Qi, LI Gang, HUANG Bo, WANG Zhenyu. Characterization and Evaluation Method of Streamline Field in High Water Cut Reservoir[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2023, 45(1): 89-96.

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参考文献

[1] THIELE M R, BATYCKY R P. Water injection optimization using a streamline-based workflow[C]. SPE 84080-MS, 2003. doi:10.2118/84080-MS
[2] CHENG Hao, OYERINDE D, DATTA-GUPTA A, et al. Compressible streamlines and three-phase history matching[C]. SPE 99465-PA, 2007. doi:10.2118/99465-PA
[3] 王继强,石成方,纪淑红,等. 特高含水期新型水驱特征曲线[J]. 石油勘探与开发,2017,44(6):955-960. doi:10.11698/PED.2017.06.13 WANG Jiqiang, SHI Chengfang, JI Shuhong, et al. New water drive characteristic curves at ultra-high water cut stage[J]. Petroleum Exploration and Development, 2017, 44(6):955-960. doi:10.11698/PED.2017.06.13
[4] GRABMEIER J, RUDOLPH A. Techniques of cluster algorithms in data mining[J]. Data Mining & Knowledge Discovery, 2002, 6(4):303-360.
[5] 苏金树,张博锋,徐昕. 基于机器学习的文本分类技术研究进展[J]. 软件学报,2006,17(9):1848-1859. SU Jinshu, ZHANG Bofeng, XU Xin. Advances in machines learning based text categorization[J]. Journal of Software, 2006, 17(9):1848-1859.
[6] 高琰,谷士文,唐琎,等. 机器学习中谱聚类方法的研究[J]. 计算机科学,2007,34(2):201-203. doi:10.3969/j.issn.1002-137X.2007.02.051 GAO Yan, GU Shiwen, TANG Jin, et al. Research on spectral clustering in machine learning[J]. Computer Science, 2007, 34(2):201-203. doi:10.3969/j.issn.1002-137X.2007.02.051
[7] 郭亚宁,冯莎莎. 机器学习理论研究[J]. 中国科技信息,2010(14):208-209,214. doi:10.3969/j.issn.1001-8972.2010.14.108 GUO Yaning, FENG Shasha. Research on machine learning theory[J]. China Science and Technology Information, 2010(14):208-209, 214. doi:10.3969/j.issn.1001-8972.2010.14.108
[8] 王元卓,靳小龙,程学旗. 网络大数据:现状与展望[J]. 计算机学报,2013,36(6):1125-1138. doi:10.3724/SP.J.1016.2013.01125 WANG Yuanzhuo, JIN Xiaolong, CHENG Xueqi. Network big data:Present and future[J]. Chinese Journal of Computers, 2013, 36(6):1125-1138. doi:10.3724/SP.J.1016.2013.01125
[9] AMINZADEH F, TAFTI T A, MAITY D. An integrated methodology for sub-surface fracture characterization using microseismic data:A case study at the NW Geysers[J]. Computers & Geosciences, 2013, 54:39-49. doi:10.1016/j.cageo.2012.10.015
[10] 林伯韬,郭建成. 人工智能在石油工业中的应用现状探讨[J]. 石油科学通报,2019,4(4):403-413. doi:10.3969/j.issn.2096-1693.2019.04.036 LIN Botao, GUO Jiancheng. Discussion on current application of artificial intelligence in petroleum industry[J]. Petroleum Science Bulletin, 2019, 4(4):403-413. doi:10.3969/j.issn.2096-1693.2019.04.036
[11] ALIYAROV R Y, RAMAZANOV R A. Prediction of multivariable properties of reservoir rocks by using fuzzy clustering[J]. Procedia Computer Science, 2016, 102:434-440.
[12] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496. doi:10.1126/science.124207
[13] CALIŃSKI T, HARABASZ J. A dendrite method for cluster analysis[J]. Communication in Statistics,1974,3(1):1-27.
[14] HUBERT L, ARABIE P. Comparing partitions[J]. Journal of Classification, 1985, 2(1):193-218.
[15] ROUSSEEUW P J. Silhouettes:A graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational & Applied Mathematics, 1987, 20:53-65.
[16] SHARMA S, JENA S K. Cluster based multipath routing protocol for wireless sensor networks[J]. Computer Science, 2015, 45(2):14-20.
[17] DUN J C. Well-separated clusters and optimal fuzzy partitions[J]. Journal of Cybernetics, 1974, 4(1):95-104. doi:10.1080/01969727408546059
[18] MARCUCCI M. Applied multivariate techniques[J]. Technometrics, 1996, 39(1):100-101. doi:10.1080/00401706.1997.10485444
[19] HALKIDI M, VAZIRGIANNIS M, BATISTAKIS Y. Quality scheme assessment in the clustering process[C]. Berlin:European Conference on Principles of Data Mining and Knowledge Discovery, 2000.
[20] MAULIK U, BANDYOPADHYAY S. Performance evaluation of some clustering algorithms and validity indices[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(12):1650-1654.

高含水油藏流线场表征与评价方法

Characterization and Evaluation Method of Streamline Field in High Water Cut Reservoir

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