少井条件下深水重力流储层地质建模方法

石佳鑫; 李少华; 代云娇; 王华; 丁丙乾; 卢昌盛; 窦梦皎

doi:10.19509/j.cnki.dzkq.tb202604050

少井条件下深水重力流储层地质建模方法

doi: 10.19509/j.cnki.dzkq.tb202604050

长江大学地球科学学院，武汉 430100
中海石油（中国）有限公司深圳分公司，广东深圳 518000

基金项目: 国家自然科学基金项目“少井条件下的储层不确定性建模与模型优选方法”（42172172）；国家科技重大专项“已开发低含水区剩余气精细表征与动用技术”（2025ZD1404303）

详细信息

作者简介:
石佳鑫：E-mail：726895453@qq.com

通讯作者:
E-mail：lish@yangtzeu.edu.cn

中图分类号: TE19；P618.13
计量
- 文章访问数: 181
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2026-04-24
- 录用日期: 2026-06-08
- 修回日期: 2026-05-21
- 网络出版日期: 2026-06-15

Geological modeling method for deepwater gravity flow reservoirs under sparse well conditions

School of Geosciences, Yangtze University, Wuhan 430100, China
Shenzhen Branch of CNOOC (China) Ltd., Shenzhen Guangdong 518000, China

More Information

Author Bio:
E-mail：726895453@qq.com

Corresponding author: E-mail：lish@yangtzeu.edu.cn

摘要

摘要:
深水油气田普遍具有钻井少、井距大、地震资料分辨率有限的特征，深水重力流储层发育多级次构型单元，砂体空间展布与叠置关系复杂，常规建模方法难以兼顾地质模式合理性与储层非均质性精细表征，给少井条件下储层地质建模带来了挑战。以珠江口盆地白云凹陷A气田为例，提出了一套适用于少井条件的深水重力流储层地质建模方法。该方法在井震联合约束下采用“层次约束、逐级嵌套”的核心思路。在第1层次建模中，将解译的复合水道剖面通过空间映射机制转化至三维网格，采用确定性和随机性结合的方法构建复合水道模型。在第2层次建模中，针对复合水道模型，通过构建反映单一水道形态与叠置关系的三维训练图像，利用多点地质统计学算法精细刻画单一水道和泥岩；针对朵叶复合体，采用基于目标的建模方法建立泥岩模型。最后，按照层次优先级对各级沉积单元进行嵌套融合，建立三维沉积相模型，并以此为基础构建储层物性模型。结果表明，相较于确定性建模方法，本方法更精准地表征了深水重力流储层的非均质性与砂体空间展布，模型精度显著提升。研究成果为深水重力流储层的勘探开发和决策提供了可靠的理论基础和技术支撑。
- 地质建模 /
- 少井条件 /
- 深水重力流储层 /
- 白云凹陷 /
- 珠江口盆地
Abstract:
Objective
Deepwater oil and gas fields are typically characterized by sparse wells, large well spacing, and limited seismic data resolution. Deepwater gravity flow reservoirs develop multilevel architectural units, with complex spatial distribution and stacking relationships of sand bodies. Conventional modeling methods struggle to simultaneously ensure the rationality of geological patterns and the fine characterization of reservoir heterogeneity, posing challenges for geological reservoir modeling under sparse well conditions.
Methods
Taking the gas field A in the Baiyun Sag, Pearl River Mouth Basin as a case study, this study proposed a geological modeling method for deepwater gravity flow reservoirs suitable for sparse well conditions. Under joint well-seismic constraints, this method adopted the core concept of "hierarchical constraint and level-by-level nesting". In the first-level modeling, the interpreted channel complex profiles were transformed into a 3D grid through a spatial mapping mechanism, and a combined deterministic and stochastic approach was employed to construct the channel complex model. In the second-level modeling, for the channel complex model, 3D training images reflecting the morphology and stacking relationships of single channels were constructed. Additionally, a multiple-point geostatistics algorithm was utilized to finely characterize single channels and mudstones. For the lobe complex, an object-based modeling method was adopted to establish the mudstone model. Finally, sedimentary units at all levels were nested and integrated according to hierarchical priority to establish a 3D sedimentary facies model, based on which a reservoir petrophysical model was constructed.
Results
The results demonstrated that, compared with deterministic modeling methods, the proposed method more accurately characterized the heterogeneity and spatial distribution of sand bodies in deepwater gravity flow reservoirs, significantly improving model accuracy.
Conclusion
The research findings provide a reliable theoretical basis and technical support for the exploration, development, and decision-making of deepwater gravity flow reservoirs.
- geological modeling /
- sparse well condition /
- deepwater gravity flow reservoir /
- Baiyun Sag /
- Pearl River Mouth Basin

HTML全文

图 1 白云凹陷区域构造图(a)、沉积模式图(b)和地层综合柱状图(c)

Figure 1. Regional tectonic map (a), sedimentary model (b), and comprehensive stratigraphic column (c) of Baiyun Sag

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图 2 水道中线和剖面形态示意图

Figure 2. Schematic diagram of channel centerline and cross-sectional morphology

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图 3 复合水道三维建模方法示意图

Figure 3. Schematic diagram of 3D modeling method for channel complex

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图 4 A气田地质建模流程

Figure 4. Geological modeling workflow for gas field A

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图 5 A气田沉积相平面图

Figure 5. Sedimentary facies map of gas field A

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图 6 地震剖面处理与信息解译示意图

Figure 6. Schematic diagram of seismic profile processing and information interpretation

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图 7 映射在三维空间中的复合水道剖面

Figure 7. Channel complex profiles mapped in 3D space

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图 8 复合水道模型剖面对比图

Figure 8. Comparison of channel complex model profiles

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图 9 第1层次沉积相模型

Figure 9. First-level sedimentary facies model

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图 10 单一水道训练图像

Figure 10. Training image of single channel

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图 11 第2层次沉积相模型

Figure 11. Second-level sedimentary facies model

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图 12 储层物性模型

Figure 12. Reservoir petrophysical models

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表 1 复合水道剖面数据集（部分）

Table 1. Dataset of channel complex profiles

水道编号	中线节点编号	中线节点 X坐标	中线节点 Y坐标	中线节点 Z坐标	水道宽度 $ W(n) $/m	剖面水道深度 $ T(n) $/m
6	1	0	0	0	496	22
	2	491	−2648	70	537	21
	3	−106	−5033	108	413	27
	4	112	−7283	175	500	25
	5	−59	−8032	186	372	21
	6	−265	−8970	185	280	22
注：n为复合水道中线节点的编号

下载: 导出CSV

参考文献(48)

[1]	邹才能, 翟光明, 张光亚, 等. 全球常规−非常规油气形成分布、资源潜力及趋势预测[J]. 石油勘探与开发, 2015, 42(1): 13-25. doi: 10.11698/PED.2015.01.02 ZOU C N, ZHAI G M, ZHANG G Y, et al. Formation, distribution, potential and prediction of global conventional and unconventional hydrocarbon resources[J]. Petroleum Exploration and Development, 2015, 42(1): 13-25. (in Chinese with English abstract) doi: 10.11698/PED.2015.01.02
[2]	张功成, 屈红军, 张凤廉, 等. 全球深水油气重大新发现及启示[J]. 石油学报, 2019, 40(1): 1-34. ZHANG G C, QU H J, ZHANG F L, et al. Major new discoveries of oil and gas in global deepwaters and enlightenment[J]. Acta Petrolei Sinica, 2019, 40(1): 1-34. (in Chinese with English abstract)
[3]	CHEN W. Status and challenges of Chinese deepwater oil and gas development[J]. Petroleum Science, 2011, 8(4): 477-484. doi: 10.1007/s12182-011-0171-8
[4]	米立军, 周守为, 谢玉洪, 等. 南海北部深水区油气勘探进展与未来展望[J]. 中国工程科学, 2022, 24(3): 58-65. doi: 10.15302/J-SSCAE-2022.03.007 MI L J, ZHOU S W, XIE Y H, et al. Deep-water oil and gas exploration in northern South China Sea: Progress and outlook[J]. Strategic Study of CAE, 2022, 24(3): 58-65. (in Chinese with English abstract) doi: 10.15302/J-SSCAE-2022.03.007
[5]	蒋德鑫, 张厚和, 李春荣, 等. 全球深水−超深水油气勘探历程与发展趋势[J]. 海洋地质前沿, 2022, 38(10): 1-12. doi: 10.16028/j.1009-2722.2022.047 JIANG D X, ZHANG H H, LI C R, et al. Global deep- and ultra-deep-water oil and gas exploration: Review and outlook[J]. Marine Geology Frontiers, 2022, 38(10): 1-12. (in Chinese with English abstract) doi: 10.16028/j.1009-2722.2022.047
[6]	张宁宁, 王青, 王建君, 等. 近20年世界油气新发现特征与勘探趋势展望[J]. 中国石油勘探, 2018, 23(1): 44-53. doi: 10.3969/j.issn.1672-7703.2018.01.005 ZHANG N N, WANG Q, WANG J J, et al. Characteristics of oil and gas discoveries in recent 20 years and future exploration in the world[J]. China Petroleum Exploration, 2018, 23(1): 44-53. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-7703.2018.01.005
[7]	温志新, 王建君, 王兆明, 等. 世界深水油气勘探形势分析与思考[J]. 石油勘探与开发, 2023, 50(5): 924-936. doi: 10.11698/PED.20230415 WEN Z X, WANG J J, WANG Z M, et al. Analysis of the world deepwater oil and gas exploration situation[J]. Petroleum Exploration and Development, 2023, 50(5): 924-936. (in Chinese with English abstract) doi: 10.11698/PED.20230415
[8]	窦立荣, 温志新, 王建君, 等. 2021年世界油气勘探形势分析与思考[J]. 石油勘探与开发, 2022, 49(5): 1033-1044. doi: 10.11698/PED.20220160 DOU L R, WEN Z X, WANG J J, et al. Analysis of the world oil and gas exploration situation in 2021[J]. Petroleum Exploration and Development, 2022, 49(5): 1033-1044. (in Chinese with English abstract) doi: 10.11698/PED.20220160
[9]	裘怿楠. 储层地质模型[J]. 石油学报, 1991, 12(4): 55-62. doi: 10.3321/j.issn:0253-2697.2000.04.019 QIU Y N. Geological models of petroleum reservoir[J]. Acta Petrolei Sinica, 1991, 12(4): 55-62. (in Chinese with English abstract) doi: 10.3321/j.issn:0253-2697.2000.04.019
[10]	PYRCZ M J, DEUTSCH C V. Geostatistical reservoir modeling[M]. Oxford: Oxford University Press, 2014.
[11]	吴胜和, 李宇鹏. 储层地质建模的现状与展望[J]. 海相油气地质, 2007, 12(3): 53-60. doi: 10.3969/j.issn.1672-9854.2007.03.009 WU S H, LI Y P. Reservoir modeling: Current situation and development prospect[J]. Marine Origin Petroleum Geology, 2007, 12(3): 53-60. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-9854.2007.03.009
[12]	贾爱林, 郭智, 郭建林, 等. 中国储层地质模型30年[J]. 石油学报, 2021, 42(11): 1506-1515. doi: 10.7623/syxb202111010 JIA A L, GUO Z, GUO J L, et al. Research achievements on reservoir geological modeling of China in the past three decades[J]. Acta Petrolei Sinica, 2021, 42(11): 1506-1515. (in Chinese with English abstract) doi: 10.7623/syxb202111010
[13]	陈麒玉, 荀磊, 崔哲思, 等. 三维地质建模技术的最新进展和发展趋势[J]. 地质科技通报, 2025, 44(3): 373-387. doi: 10.19509/j.cnki.dzkq.tb20240284 CHEN Q Y, XUN L, CUI Z S, et al. Recent progress and development trends of three-dimensional geological modeling[J]. Bulletin of Geological Science and Technology, 2025, 44(3): 373-387. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20240284
[14]	赵晓明, 吴胜和, 刘丽. 尼日尔三角洲盆地Akpo油田新近系深水浊积水道储层构型表征[J]. 石油学报, 2012, 33(6): 1049-1058. ZHAO X M, WU S H, LIU L. Characterization of reservoir architectures for Neogene deepwater turbidity channels of Akpo oilfield, Niger Delta Basin[J]. Acta Petrolei Sinica, 2012, 33(6): 1049-1058. (in Chinese with English abstract)
[15]	冯圣伦. 安哥拉深海区M油藏深水水道储层构型表征与建模[D]. 成都: 西南石油大学, 2020. FENG S L. Configuration characterization and modeling of deep-water channel reservoir in M reservoir in deep-sea area of Angola[D]. Chengdu: Southwest Petroleum University, 2020. (in Chinese with English abstract)
[16]	卜范青, 张宇焜. 高弯度深水浊积水道建模方法分析[J]. 科技导报, 2013, 31(28): 70-73. doi: 10.3981/j.issn.1000-7857.2013.h2.012 BU F Q, ZHANG Y K. Modeling of high-sinuosity deep water turbidite channel[J]. Science & Technology Review, 2013, 31(27/28): 70-73. (in Chinese with English abstract) doi: 10.3981/j.issn.1000-7857.2013.h2.012
[17]	左国平, 范国章, 孙辉, 等. 深水油气勘探关键评价技术及其在东非海域的应用[J]. 海相油气地质, 2021, 26(3): 211-223. doi: 10.3969/j.issn.1672-9854.2021.03.003 ZUO G P, FAN G Z, SUN H, et al. Key evaluation techniques for deep-water petroleum exploration and its application in the offshore of East Africa[J]. Marine Origin Petroleum Geology, 2021, 26(3): 211-223. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-9854.2021.03.003
[18]	付超, 谢玉洪, 宋来明, 等. 基于沉积模拟的虚拟井建立与深水峡谷相控建模策略: 以琼东南盆地中央峡谷陵水段为例[J]. 海相油气地质, 2023, 28(4): 413-424. doi: 10.3969/j.issn.1672-9854.2023.04.008 FU C, XIE Y H, SONG L M, et al. Pseudo-well setting and strategy of facies-controlled reservoir modeling of deep water canyon based on sedimentary simulation: Taking the Lingshui section of the Central Canyon in Qiongdongnan Basin as an example[J]. Marine Origin Petroleum Geology, 2023, 28(4): 413-424. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-9854.2023.04.008
[19]	张文彪, 陈志海, 刘志强, 等. 深水水道形态定量分析及沉积模拟: 以西非Gengibre油田为例[J]. 石油学报, 2015, 36(1): 41-49. doi: 10.7623/syxb201501005 ZHANG W B, CHEN Z H, LIU Z Q, et al. Morphology quantitative analysis and simulation of deepwater channel: A case study of Gengibre oilfield in West Africa[J]. Acta Petrolei Sinica, 2015, 36(1): 41-49. (in Chinese with English abstract) doi: 10.7623/syxb201501005
[20]	王恺其, 肖凡. 多点地质统计学的理论、方法、应用及发展现状[J]. 地质科技情报, 2019, 38(6): 256-268. doi: 10.19509/j.cnki.dzkq.2019.0630 WANG K Q, XIAO F. Multiple-points geostatistics: A review of theories, methods and applications[J]. Geological Science and Technology Information, 2019, 38(6): 256-268. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2019.0630
[21]	STREBELLE S, PAYRAZYAN K, CAERS J. Modeling of a deepwater turbidite reservoir conditional to seismic data using principal component analysis and multiple-point geostatistics[J]. SPE Journal, 2003, 8(3): 227-235. doi: 10.2118/85962-PA
[22]	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. doi: 10.11698/PED.2016.03.10 ZHANG W B, DUAN T Z, LIU Z Q, 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. (in Chinese with English abstract) doi: 10.11698/PED.2016.03.10
[23]	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. doi: 10.11743/ogg20190518 HU X, YIN Y S, FENG W J, 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. (in Chinese with English abstract) doi: 10.11743/ogg20190518
[24]	MCHARGUE T, PYRCZ M J, SULLIVAN M D, et al. Architecture of turbidite channel systems on the continental slope: Patterns and predictions[J]. Marine and Petroleum Geology, 2011, 28(3): 728-743. doi: 10.1016/j.marpetgeo.2010.07.008
[25]	STRIGHT L, BERNHARDT A, BOUCHER A. DFTopoSim: Modeling topographically-controlled deposition of subseismic scale sandstone packages within a mass transport dominated deep-water channel belt[J]. Mathematical Geosciences, 2013, 45(3): 277-296. doi: 10.1007/s11004-013-9444-7
[26]	JO H, PAN W, SANTOS J E, et al. Machine learning assisted history matching for a deepwater lobe system[J]. Journal of Petroleum Science and Engineering, 2021, 207: 109086. doi: 10.1016/j.petrol.2021.109086
[27]	PAN W, JO H, SANTOS J E, et al. Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling[J]. AAPG Bulletin, 2022, 106(11): 2163-2186. doi: 10.1306/05162221022
[28]	LI G, MEI L F, PANG X, et al. Magmatism within the northern margin of the South China Sea during the post-rift stage: An overview, and new insights into the geodynamics[J]. Earth-Science Reviews, 2022, 225: 103917. doi: 10.1016/j.earscirev.2022.103917
[29]	刘军, 彭光荣, 张丽丽, 等. 南海北部白云凹陷继承性陆缘背景古潜山成因及类型[J]. 地球科学, 2025, 50(2): 405-418. doi: 10.3799/dqkx.2023.187 LIU J, PENG G R, ZHANG L L, et al. Genesis and types of buried hills in the tectonic setting of the inheriting continental margin in Baiyun Sag, northern South China Sea[J]. Earth Science, 2025, 50(2): 405-418. (in Chinese with English abstract) doi: 10.3799/dqkx.2023.187
[30]	纪凯, 邓超, 李博, 等. 珠江口盆地新生代裂陷形成和演化的时空差异性及其成因机制探讨[J]. 地质科技通报, 2025, 44(6): 121-133. doi: 10.19509/j.cnki.dzkq.tb20240054 JI K, DENG C, LI B, et al. Discussion on the spatiotemporal differences of Cenozoic rift formation and evolution and its genetic mechanism in the Pearl River Mouth Basin[J]. Bulletin of Geological Science and Technology, 2025, 44(6): 121-133. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20240054
[31]	PING H W, CHEN H H, ZHAI P Q, et al. Petroleum charge history in the Baiyun Depression and Panyu lower uplift in the Pearl River Mouth Basin, northern South China Sea: Constraints from integration of organic geochemical and fluid inclusion data[J]. AAPG Bulletin, 2019, 103(6): 1401-1442. doi: 10.1306/11151817369
[32]	赵玉娟, 杨香华, 朱红涛, 等. 白云凹陷古近系恩平组沉积背景差异及其烃类特征[J]. 地质科技情报, 2017, 36(3): 156-163. doi: 10.19509/j.cnki.dzkq.2017.0321 ZHAO Y J, YANG X H, ZHU H T, et al. Distinct sedimentary backgrounds and hydrocarbon characteristics of Paleogene Enping Formation, Baiyun Sag[J]. Geological Science and Technology Information, 2017, 36(3): 156-163. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2017.0321
[33]	高阳东, 汪旭东, 徐乐意, 等. 白云凹陷天然气勘探历程与大中型气田形成主控因素[J]. 中国海上油气, 2025, 37(3): 1-15. doi: 10.11935/j.issn.1673-1506.2025.03.001 GAO Y D, WANG X D, XU L Y, et al. Natural gas exploration history and main controlling factors for formation of large and medium gas fields in Baiyun Sag[J]. China Offshore Oil and Gas, 2025, 37(3): 1-15. (in Chinese with English abstract) doi: 10.11935/j.issn.1673-1506.2025.03.001
[34]	HE M, ZHONG G F, LIU X F, et al. Rapid post-rift tectonic subsidence events in the Pearl River Mouth Basin, northern South China Sea margin[J]. Journal of Asian Earth Sciences, 2017, 147: 271-283. doi: 10.1016/j.jseaes.2017.07.024
[35]	DEUTSCH C V, WANG L B. Hierarchical object-based stochastic modeling of fluvial reservoirs[J]. Mathematical Geology, 1996, 28(7): 857-880. doi: 10.1007/BF02066005
[36]	DEUTSCH C V, TRAN T T. FLUVSIM: A program for object-based stochastic modeling of fluvial depositional systems[J]. Computers & Geosciences, 2002, 28(4): 525-535. doi: 10.1016/S0098-3004(01)00075-9
[37]	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 Y S, ZHANG C M, LI S H, 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. (in Chinese with English abstract)
[38]	李少华, 卢昌盛, 何维领, 等. 一种基于目标的非贯穿型河道建模方法[J]. 天然气地球科学, 2019, 30(3): 305-311. doi: 10.11764/j.issn.1672-1926.2018.12.010 LI S H, LU C S, HE W L, et al. An object-based modeling method for channels not across through interesting area[J]. Natural Gas Geoscience, 2019, 30(3): 305-311. (in Chinese with English abstract) doi: 10.11764/j.issn.1672-1926.2018.12.010
[39]	刘文岭. 地震约束储层地质建模技术[J]. 石油学报, 2008, 29(1): 64-68. doi: 10.3321/j.issn:0253-2697.2008.01.011 LIU W L. Geological modeling technique for reservoir constrained by seismic data[J]. Acta Petrolei Sinica, 2008, 29(1): 64-68. (in Chinese with English abstract) doi: 10.3321/j.issn:0253-2697.2008.01.011
[40]	STREBELLE S B, JOURNEL A G. Reservoir modeling using multiple-point statistics[C]//Anon. SPE Annual Technical Conference and Exhibition. Richardson: Society of Petroleum Engineers, 2001: SPE 71324-MS.
[41]	STREBELLE S. Conditional simulation of complex geological structures using multiple-point statistics[J]. Mathematical Geology, 2002, 34(1): 1-21. doi: 10.1023/A:1014009426274
[42]	王鸣川, 段太忠, 计秉玉. 多点统计地质建模技术研究进展与应用[J]. 古地理学报, 2017, 19(3): 557-566. doi: 10.7605/gdlxb.2017.03.043 WANG M C, DUAN T Z, JI B Y. Research progress and application of multipoint statistics geological modeling technology[J]. Journal of Palaeogeography, 2017, 19(3): 557-566. (in Chinese with English abstract) doi: 10.7605/gdlxb.2017.03.043
[43]	骆杨, 赵彦超. 多点地质统计学在河流相储层建模中的应用[J]. 地质科技情报, 2008, 27(3): 68-72. doi: 10.3969/j.issn.1000-7849.2008.03.010 LUO Y, ZHAO Y C. Application of multiple-point geostatistics in fluvial reservoir stochastic modeling[J]. Geological Science and Technology Information, 2008, 27(3): 68-72. (in Chinese with English abstract) doi: 10.3969/j.issn.1000-7849.2008.03.010
[44]	DAMSLETH E, TJOLSEN C B, OMRE H, et al. A two-stage stochastic model applied to a North Sea reservoir[J]. Journal of Petroleum Technology, 1992, 44(4): 402-486. doi: 10.2118/20605-pa
[45]	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. doi: 10.3321/j.issn:1005-2321.2005.03.025 YU X H, CHEN J Y, ZHANG Z J, et al. Stochastic modeling for characteristics of petroleum reservoir constrained by facies[J]. Earth Science Frontiers, 2005, 12(3): 237-244. (in Chinese with English abstract) doi: 10.3321/j.issn:1005-2321.2005.03.025
[46]	李少华, 张昌民, 张尚锋, 等. 沉积微相控制下的储层物性参数建模[J]. 江汉石油学院学报, 2003, 25(1): 24-26. doi: 10.3969/j.issn.1000-9752.2003.01.009 LI S H, ZHANG C M, ZHANG S F, et al. Modeling of reservoir petrophysical parameters under the control of sedimentary microfacies[J]. Journal of Jianghan Petroleum Institute, 2003, 25(1): 24-26. (in Chinese with English abstract) doi: 10.3969/j.issn.1000-9752.2003.01.009
[47]	胡勇, 于兴河, 陈恭洋, 等. 平均毛管压力函数分类及其在流体饱和度计算中的应用[J]. 石油勘探与开发, 2012, 39(6): 733-738. HU Y, YU X H, CHEN G Y, et al. Classification of the average capillary pressure function and its application in calculating fluid saturation[J]. Petroleum Exploration and Development, 2012, 39(6): 733-738. (in Chinese with English abstract)
[48]	邵才瑞, 张鹏飞, 张福明, 等. 用J函数提高致密砂岩气层饱和度测井评价精度[J]. 中国石油大学学报(自然科学版), 2016, 40(4): 57-65. doi: 10.3969/j.issn.1673-5005.2016.04.007 SHAO C R, ZHANG P F, ZHANG F M, et al. Improving well log evaluation accuracy of tight sandstone gas saturation using J function[J]. Journal of China University of Petroleum (Edition of Natural Science), 2016, 40(4): 57-65. (in Chinese with English abstract) doi: 10.3969/j.issn.1673-5005.2016.04.007