石油勘探与开发 >

2020 , Vol. 47 >Issue 6: 1198 - 1204

DOI: https://doi.org/10.11698/PED.2020.06.12

油气田开发

泥质夹层的三维预测与地质模型的等效粗化表征--以加拿大麦凯河油砂储集层为例

  • 尹艳树 ,
  • 陈和平 ,
  • 黄继新 ,
  • 冯文杰 ,
  • 刘焱鑫 ,
  • 高禹锋
展开
  • 1.长江大学地球科学学院,武汉 430100;
    2.中国石油勘探开发研究院,北京 100083
尹艳树（1978-）,男,湖北仙桃人,博士,长江大学教授,主要从事油藏描述与三维地质建模方面的教学与科研工作。地址：湖北省武汉市蔡甸区大学路111号,长江大学地球科学学院,邮政编码：430100。E-mail: yys6587@126.com

收稿日期: 2019-09-25

  修回日期: 2020-10-26

  网络出版日期: 2020-11-27

基金资助

国家科技重大专项（2016ZX05031-002-001）; 国家自然科学基金项目“三角洲前缘储层多点地质统计建模方法研究”（41572081）; 湖北省创新群体项目（2016CFA024）

收起

Muddy interlayer forecasting and an equivalent upscaling method based on tortuous paths: A case study of Mackay River oil sand reservoirs in Canada

  • YIN Yanshu ,
  • CHEN Heping ,
  • HUANG Jixin ,
  • FENG Wenjie ,
  • LIU Yanxin ,
  • GAO Yufeng
Expand
  • 1. School of Geosciences, Yangtze University, Wuhan 430100, China;
    2. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China

Received date: 2019-09-25

  Revised date: 2020-10-26

  Online published: 2020-11-27

Fold

摘要

根据加拿大麦凯河油砂储集层岩心观察统计泥质夹层终止频率,采用排队理论模型预测泥质夹层延伸范围,进而建立了岩心泥质夹层厚度与延伸长度的定量关系;提出了基于泥质夹层影响下流体迂曲路径计算的地质模型等效粗化方法。采用地质连通体追踪算法,实现每个粗化网格体内单一夹层的追踪识别,确定粗化网格内夹层平均长度、宽度以及占比;计算粗化网格内在泥质夹层影响下的流体流动平均路径长度等参数;以达西定律为基础,推导出粗化网格内平均渗透率计算公式,实现等效粗化网格渗透率的计算。麦凯河油砂实际地质模型的粗化结果及开发指标对比证实泥质夹层迂曲路径计算等效粗化方法可以很好体现泥质夹层的遮挡属性,更能代表实际地质条件对开发效果的影响。图10表1参28

关键词： 油砂; 泥质夹层; 分布预测; 地质模型; 迂曲路径; 等效粗化

本文引用格式

尹艳树 , 陈和平 , 黄继新 , 冯文杰 , 刘焱鑫 , 高禹锋 . 泥质夹层的三维预测与地质模型的等效粗化表征--以加拿大麦凯河油砂储集层为例[J]. 石油勘探与开发, 2020 , 47(6) : 1198 -1204 . DOI: 10.11698/PED.2020.06.12

Abstract

Based on the abundant core data of oil sands in the Mackay river in Canada, the termination frequency of muddy interlayers was counted to predict the extension range of interlayers using a queuing theory model, and then the quantitative relationship between the thickness and extension length of muddy interlayer was established. An equivalent upscaling method of geologic model based on tortuous paths under the effects of muddy interlayer has been proposed. Single muddy interlayers in each coarse grid are tracked and identified, and the average length, width and proportion of muddy interlayer in each coarse grid are determined by using the geological connectivity tracing algorithm. The average fluid flow length of tortuous path under the influence of muddy interlayer is calculated. Based on the Darcy formula, the formula calculating average permeability in the coarsened grid is deduced to work out the permeability of equivalent coarsened grid. The comparison of coarsening results of the oil sand reservoir of Mackay River with actual development indexes shows that the equivalent upscaling method of muddy interlayer by tortuous path calculation can reflect the blocking effect of muddy interlayer very well, and better reflect the effects of geological condition on production.

Key words： oil sands; muddy interlayer; distribution prediction; geological model; tortuous path; equivalent upscaling

参考文献

[1] HALDORSEN H H, LAKE L W. A new approach to shale management in field-scale models[J]. Society of Petroleum Engineers Journal, 1984, 24(4): 447-457.
[2] BEGG S H, KING P R. Modelling the effects of shales on reservoir performance: Calculation of effective vertical permeability[R]. Texas: SPE Reservoir Simulation Symposium, 1985.
[3] BEGG S H, CARTER R R, DRANFIELD P. Assigning effective values to simulator grid block parameters for heterogeneous reservoirs[J]. SPE Reservoir Engineering, 1989, 4(4): 455-463.
[4] GREEN C P, ENNIS-KING J. Vertical permeability distribution of reservoirs with impermeable barriers[J]. Transport in Porous Media, 2010, 83(3): 525-539.
[5] 李君, 李少华, 昌伦杰, 等. 考虑夹层影响的渗透率粗化新方法[J]. 大庆石油地质与开发, 2017, 36(2): 73-77.
LI Jun, LI Shaohua, CHANG Lunjie, et al. A new up-scaling method for the permeability considering the influences of the interlayers[J]. Petroleum Geology and Oilfield Development in Daqing, 2017, 36(2): 73-77.
[6] TANG M, ZHANG K, HUANG J, et al. Facies and the architecture of estuarine tidal bar in the lower Cretaceous Mcmurray Formation, Central Athabasca Oil Sands, Alberta, Canada[J]. Energies, 2019, 12(9): 1769.
[7] JABLONSKI, BRYCE V J, DALRYMPLE R W. Recognition of strong seasonality and climatic cyclicity in an ancient, fluvially dominated, tidally influenced point bar: Middle McMurray Formation, Lower Steepbank River, north-eastern Alberta, Canada[J]. Sedimentology, 2016, 63(3): 552-585.
[8] MARTINIUS A W, FUSTIC M, GARNER D L, et al. Reservoir characterization and multiscale heterogeneity modeling of inclined heterolithic strata for bitumen-production forecasting, McMurray Formation, Corner, Alberta, Canada[J]. Marine and Petroleum Geology, 2017, 82: 336-361.
[9] MUSIAL G, REYNAUD J Y, GINGRAS M K, et al. Subsurface and outcrop characterization of large tidally influenced point bars of the Cretaceous McMurray Formation (Alberta, Canada)[J]. Sedimentary Geology, 2012, 279: 156-172.
[10] PRATS M. The influence of oriented arrays of thin impermeable shale lenses or of highly conductive natural fractures on apparent permeability anisotropy[J]. Journal of Petroleum Technology, 1972, 24(10): 1219-1221.
[11] DESBARATS A J. Numerical estimation of effective permeability in sand-shale formations[J]. Water Resources Research, 1987, 23(2): 273-286.
[12] DEUTSCH C V. Calculating effective absolute permeability in sandstone/ shale sequences[J]. SPE Formation Evaluation, 1989, 4(3): 343-348.
[13] MACDONALD A C, HALLAND E K. Sedimentology and shale modeling of a sandstone-rich fluvial reservoir: Upper Statfjord Formation, Statfjord field, northern North Sea[J]. AAPG Bulletin, 1993, 77(6): 1016-1040.
[14] WHITE C D, BARTON M D. Comparison of the recovery behavior of contrasting reservoir analogs in the Ferron Sandstone using outcrop studies and numerical simulation[M]. Texas: The University of Texas at Austin, 1998: 249-295.
[15] ZEITO G A. Interbedding of shale breaks and reservoir heterogeneities[J]. Journal of Petroleum Technology, 1965, 17(10): 1223-1228.
[16] WEBER K J. Influence of common sedimentary structures on fluid flow in reservoir models[J]. Journal of Petroleum Technology, 1982, 34(3): 665-672.
[17] ROBINSON J W, MCCABEA P J. Sandstone-body and shale-body dimensions in a braided fluvial system: Salt Wash Sandstone Member (Morrison Formation), Garfield County, Utah[J]. AAPG Bulletin, 1997, 81(8): 1267-1291.
[18] FLINT S S, BRYANT I D. The geological modelling of hydrocarbon reservoirs and outcrop analogues[M]. London: Blackwell Scientific Publications, Inc., 1993: 205-212.
[19] WHITE C D, WILLIS B J. A method to estimate length distributions from outcrop data[J]. Mathematical Geology, 2000, 32(4): 389-419.
[20] BURTON D, WOOD L J. Geologically-based permeability anisotropy estimates for tidally-influenced reservoirs using quantitative shale data[J]. Petroleum Geoscience, 2013, 19(1): 3-20.
[21] 谭学群, 刘云燕, 周晓舟, 等. 复杂断块油藏三维地质模型多参数定量评价[J]. 石油勘探与开发, 2019, 46(1): 185-194.
TAN Xuequn, LIU Yunyan, ZHOU Xiaozhou, et al. Multi-parameter quantitative assessment of 3D geological models for complex fault-block oil reservoirs[J]. Petroleum Exploration and Development, 2019, 46(1): 185-194.
[22] 王立鑫, 尹艳树, 冯文杰, 等. 多点地质统计学中训练图像优选方法及其在地质建模中的应用[J]. 石油勘探与开发, 2019, 46(4): 703-709.
WANG Lixin, YIN Yanshu, FENG Wenjie, et al. A training image optimization method in multiple-point geostatistics and its application in geological modeling[J]. Petroleum Exploration and Development, 2019, 46(4): 703-709.
[23] 石书缘, 尹艳树, 和景阳, 等. 基于随机游走过程的多点地质统计学建模方法[J]. 地质科技情报, 2011, 30(5): 127-131.
SHI Shuyuan,YIN Yanshu,HE Jingyang,et al. A random walk-based multiple-point statistics modeling methods[J]. Geological Science and Technology Information, 2011, 30(5): 127-131.
[24] 黄继新, 穆龙新, 陈和平, 等. 基于沉积界面的储集层建模方法初探[J]. 石油勘探与开发, 2013, 40(5): 591-594.
HUANG Jixin, MU Longxin, CHEN Heping, et al. Preliminary study on a depositional interface-based reservoir modeling method[J]. Petroleum Exploration and Development, 2013, 40(5): 591-594.
[25] 黄文松, 王家华, 陈和平, 等. 基于水平井资料进行地质建模的大数据误区分析与应对策略[J]. 石油勘探与开发, 2017, 44(6): 939-947.
HUANG Wensong, WANG Jiahua, CHEN Heping, et al. Big data paradox and modeling strategies in geological modeling based on horizontal wells data[J]. Petroleum Exploration and Development, 2017, 44(6): 939-947.
[26] 李阳, 吴胜和, 侯加根, 等. 油气藏开发地质研究进展与展望[J]. 石油勘探与开发, 2017, 44(4): 569-579.
LI Yang, WU Shenghe, HOU Jiagen, et al. Progress and prospects of reservoir development geology[J]. Petroleum Exploration and Development, 2017, 44(4): 569-579.
[27] 张文彪, 段太忠, 刘志强, 等. 深水浊积水道多点地质统计模拟: 以安哥拉Plutonio油田为例[J]. 石油勘探与开发, 2016, 43(3): 403-410.
ZHANG Wenbiao, DUAN Taizhong, LIU Zhiqiang, et al. Application of multi-point geostatistics in deep-water turbidity channel simulation: A case study of plutonio oilfield in angola[J]. Petroleum Exploration and Development, 2016, 43(3): 403-410.
[28] DEUTSCH C V. FORTRAN programs for calculating connectivity of 3-D numerical models and for ranking multiple realizations[J]. Computers & Geosciences, 1998, 24(1): 69-76.
Options
文章导航

/

〈 〉

版权所有：《石油勘探与开发》编辑部；《石油勘探与开发（英文）》编辑部
地址：北京市海淀区学院路20号，中国石油勘探开发研究院数据中心605室　

京ICP备15044687号    技术支持：北京玛格泰克科技发展有限公司