石油勘探与开发 >

2021 , Vol. 48 >Issue 2: 347 - 359

DOI: https://doi.org/10.11698/PED.2021.02.11

油气田开发

基于自适应空间抽样由二维剖面重构三维地质模型的方法－以加拿大某区块McMurray组储集层为例

  • 王立鑫 ,
  • 尹艳树 ,
  • 王晖 ,
  • 张昌民 ,
  • 冯文杰 ,
  • 刘振坤 ,
  • 王盘根 ,
  • 程丽芳 ,
  • 刘炯
展开
  • 1.长江大学地球科学学院,武汉 430100;
    2.中海油研究总院有限责任公司,北京 100028;
    3.中国石化石油勘探开发研究院,北京 100083
王立鑫（1991-）,男,湖北襄阳人,长江大学在读博士研究生,从事储集层建模方面研究。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：201571323@yangtzeu.edu.cn

收稿日期: 2020-06-14

  修回日期: 2021-01-30

  网络出版日期: 2021-03-19

基金资助

国家科技重大专项（2017ZX05005-004-002,2016ZX05031-002-001）; 国家自然科学基金项目“多点地质统计学相控地震同时反演方法”（41872138）; 长江大学地质资源与地质工程一流学科开放基金项目（2019KFJJ0818029）

收起

A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada

  • WANG Lixin ,
  • YIN Yanshu ,
  • WANG Hui ,
  • ZHANG Changmin ,
  • FENG Wenjie ,
  • LIU Zhenkun ,
  • WANG Pangen ,
  • CHENG Lifang ,
  • LIU Jiong
Expand
  • 1. School of Geosciences, Yangtze University, Wuhan 430100, China;
    2. CNOOC Research Institute Co., Ltd., Beijing 100028, China;
    3. Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China

Received date: 2020-06-14

  Revised date: 2021-01-30

  Online published: 2021-03-19

Fold

摘要

基于连井剖面、沉积相平面等地质分析结果构建正交的二维训练图像,通过线性池化方法获得3个方向上的二维概率,随后采用对数线性池化将3个方向上的概率融合,最终确定未知点处的三维多点模式概率,实现由二维剖面重构三维模型的目的。针对二维训练图像中模式变化性较小,概率分布代表性降低从而导致抽样不确定性增加的问题,引入自适应空间抽样方法,采用迭代模拟策略,从前次模拟结果中可信度高的区域抽取部分点作为附加条件点参与下一次模拟,从而提高了模式概率抽样稳定性。侧积层概念模型对比表明,采用自适应空间抽样的重构算法提高了模式抽样的准确性和空间结构特征的合理性,能够准确反映侧积层形态和分布样式。在加拿大某区块McMurray组曲流河储集层的实际应用表明,新方法准确地再现了潮汐影响下曲流河储集层内部复杂的侧积层形态、空间分布样式和发育特征。抽稀井检验表明,模拟准确度在85%以上,新钻水平井侧积层解释与预测结果符合率达到80%。图13表3参41

关键词： 地质建模; 二维剖面; 三维模型; 概率融合; 侧积层; 多点地质统计; 自适应空间抽样

本文引用格式

王立鑫 , 尹艳树 , 王晖 , 张昌民 , 冯文杰 , 刘振坤 , 王盘根 , 程丽芳 , 刘炯 . 基于自适应空间抽样由二维剖面重构三维地质模型的方法－以加拿大某区块McMurray组储集层为例[J]. 石油勘探与开发, 2021 , 48(2) : 347 -359 . DOI: 10.11698/PED.2021.02.11

Abstract

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies; the 2D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3D multi-point pattern probabilities at the unknown points, to realize the reconstruction of a 3D model from 2D cross-section. To solve the problems of reducing pattern variability in the 2D training image and increasing sampling uncertainty, an adaptive spatial sampling method is introduced, and an iterative simulation strategy is adopted, in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability. The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics, and accurately reflect the morphology and distribution pattern of the lateral accretion layer. Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape, spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect. The test by sparse wells shows that the simulation accuracy is above 85%, and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.

Key words： geological modeling; two-dimensional cross-section; three-dimensional model; probability aggregation; lateral accretion layer; multiple-point geostatistics; self-adaptive spatial sampling

参考文献

[1] STREBELLE S.Conditional simulation of complex geological structures using multiple-point statistics[J]. Mathematical Geology, 2002, 34(1): 1-21.
[2] 尹艳树, 吴胜和. 储层随机建模研究进展[J]. 天然气地球科学, 2006, 17(2): 210-216.
YIN Yanshu, WU Shenghe.The progress of reservoir stochastic modeling[J]. Natural Gas Geoscience, 2006, 17(2): 210-216.
[3] 贾爱林. 中国储层地质模型20年[J]. 石油学报, 2011, 32(1): 181-188.
JIA Ailin.Research achievements on reservoir geological modeling of China in the past two decades[J]. Acta Petrolei Sinica, 2011, 32(1): 181-188.
[4] 尹艳树, 张昌民, 李玖勇, 等. 多点地质统计学研究进展与展望[J]. 古地理学报, 2011, 13(2): 245-252.
YIN Yanshu, ZHANG Changmin, LI Jiuyong, et al.Progress and prospect of multiple-point geostatistics[J]. Journal of Palaeogeography, 2011, 13(2): 245-252.
[5] 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.
[6] 贾爱林, 郭建林, 何东博. 精细油藏描述技术与发展方向[J]. 石油勘探与开发, 2007, 34(6): 691-695.
JIA Ailin, GUO Jianlin, HE Dongbo.Perspective of development in detailed reservoir description[J]. Petroleum Exploration and Development, 2007, 34(6): 691-695.
[7] 尹艳树, 张昌民, 李少华, 等. 一种基于沉积模式的多点地质统计学建模方法[J]. 地质论评, 2014, 60(1): 216-221.
YIN Yanshu, ZHANG Changmin, LI Shaohua, et al.A pattern-based multiple point geostatistics method[J]. Geological Review, 2014, 60(1): 216-221.
[8] 于兴河, 陈建阳, 张志杰, 等. 油气储层相控随机建模技术的约束方法[J]. 地学前缘, 2005, 12(3): 237-244.
YU Xinghe, CHEN Jianyang, ZHANG Zhijie, et al.Stochastic modeling for characteristics of petroleum reservoir constrained by facies[J]. Earth Science Frontiers, 2005, 12(3): 237-244
[9] 于兴河. 油气储层表征与随机建模的发展历程及展望[J]. 地学前缘, 2008, 15(1): 1-15.
YU Xinghe.A review of development course and prospect of petroleum reservoir characterization and stochastic modeling[J]. Earth Science Frontiers, 2008, 15(1): 1-15.
[10] ARPAT G B.Sequential simulation with patterns[D]. California: Stanford University, 2005.
[11] ZHANG T, SWITZER P, JOURNEL A.Filter-based classification of training image patterns for spatial simulation[J]. Mathematical Geology, 2006, 38(1): 63-80.
[12] HONARKHAH M, CAERS J.Stochastic simulation of patterns using distance-based pattern modeling[J]. Mathematical Geosciences, 2010, 42(5): 487-517.
[13] MARIETHOZ G, RENARD P.Reconstruction of incomplete data sets or images using direct sampling[J]. Mathematical Geosciences, 2010, 42(3): 245-268.
[14] FENG W, WU S, YIN Y, et al.A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics[J]. Computers & Geosciences, 2017, 104: 35-53.
[15] 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.
[16] FENG W, YIN Y, ZHANG C, et al.A training image optimal selecting method based on composite correlation coefficient ranking for multiple-point geostatistics[J]. Journal of Petroleum Science and Engineering, 2019, 179: 292-311.
[17] 胡勇, 于兴河, 李胜利, 等. 应用地震正反演技术提高地质建模精度[J]. 石油勘探与开发, 2014, 41(2): 190-197.
HU Yong, YU Xinghe, LI Shengli, et al.Improving the accuracy of geological model by using seismic forward and inversion techniques[J]. Petroleum Exploration and Development, 2014, 41(2): 190-197.
[18] 张文彪, 段太忠, 郑磊, 等. 基于浅层地震的三维训练图像获取及应用[J]. 石油与天然气地质, 2015, 36(6): 1030-1037.
ZHANG Wenbiao, DUAN Taizhong, ZHENG Lei, et al.Generation and application of three-dimensional MPS training images based on shallow seismic data[J]. Oil & Gas Geology, 2015, 36(6): 1030-1037.
[19] 陈更新, 赵凡, 王建功, 等. 分区域多点统计随机地质建模方法: 以柴达木盆地辫状河三角洲沉积储集层为例[J]. 石油勘探与开发, 2015, 42(5): 638-645.
CHEN Gengxin, ZHAO Fan, WANG Jiangong, et al.Regionalized multiple-point stochastic geological modeling: A case from braided delta sedimentary reservoirs in Qaidam Basin, NW China[J]. Petroleum Exploration and Development, 2015, 42(5): 638-645.
[20] 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.
[21] 胡迅, 尹艳树, 冯文杰, 等. 深水浊积水道训练图像建立与多点地质统计建模应用[J]. 石油与天然气地质, 2019, 40(5): 1126-1134.
HU Xun, YIN Yanshu, FENG Wenjie, et al.Establishment of training images of turbidity channels in deep waters and application of multi-point geostatistical modeling[J]. Oil & Gas Geology, 2019, 40(5): 1126-1134.
[22] OKABE H, BLUNT M J.Prediction of permeability for porous media reconstructed using multiple-point statistics[J]. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 70(6 Pt 2): 66135.
[23] OKABE H, BLUNT M J. Pore space reconstruction of vuggy carbonates using microtomography and multiple-point statistics[J]. Water Resources Research, 2007, 43(12): W12S02.
[24] 张挺, 李道伦, 卢德唐, 等. 基于多点地质统计法的多孔介质重构研究[J]. 中国科学: 物理学力学天文学, 2009, 39(9): 1348-1360.
ZHANG Ting, LI Daolun, LU Detang, et al.Research on the reconstruction method of porous media using multiple-point geostatistics[J]. SCIENTIA SINICA Physica, Mechanica & Astronomica, 2010, 53(1): 122-134.
[25] COMUNIAN A, RENARD P, STRAUBHAAR J.3D multiple-point statistics simulation using 2D training images[J]. Computers & Geosciences, 2012, 40: 49-65.
[26] 吴玉其, 林承焰, 任丽华, 等. 基于多点地质统计学的数字岩心建模[J]. 中国石油大学学报(自然科学版), 2018, 42(3): 12-21.
WU Yuqi, LIN Chengyan, REN Lihua, et al.Digital core modeling based on multiple-point statistics[J]. Journal of China University of Petroleum (Edition of Natural Science), 2018, 42(3): 12-21.
[27] 林承焰, 吴玉其, 任丽华, 等. 数字岩心建模方法研究现状及展望[J]. 地球物理学进展, 2018, 33(2): 679-689.
LIN Chengyan, WU Yuqi, REN Lihua, et al.Review of digital core modeling methods[J]. Progress in Geophysics, 2018, 33(2): 679-689.
[28] CHEN Q, MARIETHOZ G, LIU G, et al.Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross-sections[J]. Hydrology and Earth System Sciences, 2018, 22(12): 6547-6566.
[29] JEONG C, MUKERJI T, MARIETHOZ G.A fast approximation for seismic inverse modeling: Adaptive spatial resampling[J]. Mathematical Geosciences, 2017, 49(7): 845-869.
[30] ALLARD D, COMUNIAN A, RENARD P.Probability aggregation methods in geoscience[J]. Mathematical Geosciences, 2012, 44(5): 545-581.
[31] MARIETHOZ G, RENARD P, CAERS J.Bayesian inverse problem and optimization with iterative spatial resampling[J]. Water Resources Research, 2010, 46(11): W11530.
[32] 胡光义, 许磊, 王宗俊, 等. 加拿大阿萨巴斯卡Kinosis区域下白垩统McMurray组内河口湾复合点坝砂体构型解剖[J]. 古地理学报, 2018, 20(6): 1001-1012.
HU Guangyi, XU Lei, WANG Zongjun, et al.Architectural analysis of compound point-bar sandbody in inner estuary of the Lower Cretaceous McMurray Formation in Kinosis area, Athabasca, Canada[J]. Journal of Palaeogeography, 2018, 20(6): 1001-1012.
[33] 程丽芳, 王晖, 尹艳树, 等. 基于浅层地震资料的曲流河点坝精细解剖: 以加拿大某区块Mcmurray组储层为例[J]. 断块油气田, 2019, 26(4): 464-469.
CHENG Lifang, WANG Hui, YIN Yanshu, et al.Fine dissection of meandering river point bars based on shallow seismic data: A case study of Mcmurray reservoir in a block, Canada[J]. Fault-block Oil & Gas Field, 2019, 26(4): 464-469.
[34] MIALL A D.Architectural-element analysis: A new method of facies analysis applied to fluvial deposits[J]. Earth-Science Reviews, 1985, 22(4): 261-308.
[35] FUSTIC M, BENNETT B, HUANG H, et al.Differential entrapment of charged oil: New insights on McMurray Formation oil trapping mechanisms[J]. Marine and Petroleum Geology, 2012, 36(1): 50-69.
[36] 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.
[37] FUSTIC M, STROBL R, GHINASSI M, et al.Unsuccessful cut offs - origin and partial preservation of enigmatic channel fills encased within a large-scale point-bar deposit: The McMurray Formation type section, Alberta, Canada[M]//GHINASSI M, COLOMBERA L, MOUNTNEY N P, et al. Fluvial meanders and their sedimentary products in the rock record. New Jersey: Wiley Blackwell, 2018: 321-348.
[38] 尹艳树, 张昌民, 李少华, 等. 一种新的曲流河点坝侧积层建模方法[J]. 石油学报, 2011, 32(2): 315-319.
YIN Yanshu, ZHANG Changmin, LI Shaohua, et al.A new stochastic modeling method for 3-D forecasting lateral accretion beddings of point bars in meandering rivers[J]. Acta Petrolei Sinica, 2011, 32(2): 315-319.
[39] 张昌民. 储层研究中的层次分析法[J]. 石油与天然气地质, 1992, 13(3): 344-350.
ZHANG Changmin.Hierarchy analysis in reservoir researches[J]. Oil & Gas Geology, 1992, 13(3): 344-350.
[40] 尹艳树. 层次建模方法及其在河流相储层建筑结构建模中的应用[J]. 石油地质与工程, 2011, 25(6): 1-4.
YIN Yanshu.Hierarchical modeling method and its application in the construction of fluvial architectural elements model[J]. Petroleum Geology and Engineering, 2011, 25(6): 1-4.
[41] 尹艳树, 张昌民, 尹太举, 等. 萨尔图油田辫状河储层三维层次建模[J]. 西南石油大学学报(自然科学版), 2012, 34(1): 13-18.
YIN Yanshu, ZHANG Changmin, YIN Taiju, et al.3-D Hierarchical modeling of the braided channel reservoir of Saertu oilfield[J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2012, 34(1): 13-18.
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