Formation mechanism and distribution prediction of fine-fracture pores in the Lower Ordovician in Tahe oilfield

doi:10.13745/j.esf.sf.2022.10.13

Abstract

Abstract:

The development direction of the Ordovician carbonate reservoirs in the Tahe Oilfield has gradually shifted from the seismic-reflected “bead-type” fracture/pore reservoirs to the “non-bead”-type fine-fracture/pore reservoirs. Determining the fine-fracture/pore formation mechanism and development is the key to improving tight-reservoir utilization. In this study, fine-fracture/pore characterization were carried out on core and thin sections, and the fracture genetic types, fracture parameters and filling properties of fine fractures were determined. The fracture types in the study area were classified as “tectonic fracture” and “non-tectonic fracture” according to the fracture origin, and the fractures are highly angular or vertically produced. The tectonic fractures are less filled compared to weathered fractures; whilst cavity filling is relatively high, but residual porosity can result from coarser crystals in the cavity. The formation mechanism of the fine-fracture/pore reservoirs in the study area was determined based on the analyses of pore-control factors, such as paleokarst geomorphology, local residual mound structure and fracture characteristics, combined with tectonic stress-field simulation. Fracture and pore are more developed at higher elevation in the east than in the west of the main area as highland areas are conducive to karst development. Local residual mound also influence karst development as its gentle slope allows rock and water to interact more fully which favors reservoir formation. The major faults control the formation of large caves, whilst large caves and faults control the development of fine fractures which can reach as deep as 200 m below the T₇⁴ interface. Tectonic stress fields influence the distribution of tectonic fractures, and tectonic fractures provide the dissolution channel for the formation of pores and thus indirectly controls the distribution of fine-fracture/pore reservoirs. A prediction method for fine-fracture/pore is developed based on nonlinear neural network deep learning algorithm to determine the planar distribution of fine-fracture/pores, and the prediction results agree with the single-well pore distribution data as well as the initial single-well oil production data. This research lays the technical foundation for a full utilization of fine-fracture/pore reserves in the oilfield.

Key words: fine-fracture/pores, formation mechanism, fracture/pore prediction, Tahe oilfield, Lower Ordovician, Yingshan Formation, main zone

CLC Number:

ZHANG Juan, XIE Runcheng, YANG Min, GAO Zhiqian, WANG Ming, ZHANG Changjian, WANG Hong. Formation mechanism and distribution prediction of fine-fracture pores in the Lower Ordovician in Tahe oilfield[J]. Earth Science Frontiers, 2023, 30(4): 51-64.

Figures/Tables 11

Fig.1 Location and stratigraphic characteristics of the main area of Tahe oilfield

Fig.2 Characteristics of fine-fracture/pore in the main area

Fig.3 Distribution of karst terrains and single-well fine-fracture/pore growth rates in the early Hercynian

Fig.4 Distribution relationships between monadnocks of different sizes and fine-fracture/pore growth rates in the main area

Fig.5 Distribution map of fractures and fine-fracture/pore growth rates in the main area

Fig.6 Comparison of vertical distribution of fine-fracture/pores between wells in the fault zone

Fig.7 Variations of fine-fracture/pore growth rate with fault distance in different parts of the main area

Fig.8 Distribution of tectonic stress field in the main work block and its relationship with the development rate of fine-fracture/pores in a single well

Fig.9 Landforms for fine-fracture/pore developments in the main area

Fig.10 Flowchart of fine-fracture/pore prediction method

Fig.11 Fine-fracture/pore distribution and its relationship with single-well oil production in the main area

References 44

[1]	徐旭辉, 陆建林, 王保华, 等. 中国海相盆地油气资源动态评价与有利勘探方向[J]. 地学前缘, 2022, 29(6): 73-83. DOI
[2]	李朝瑞. 塔河油田碳酸盐岩缝洞油藏高效井预测研究[D]. 北京: 中国石油大学(北京), 2020.
[3]	王震, 文欢, 胡文革. 塔河油田碳酸盐岩缝洞空间位置预测方法研究[J]. 工程地球物理学报, 2019, 16(4): 433-438.
[4]	李阳, 范智慧. 塔河奥陶系碳酸盐岩油藏缝洞系统发育模式与分布规律[J]. 石油学报, 2011, 32(1): 101-106. DOI
[5]	侯加根, 马晓强, 刘钰铭, 等. 缝洞型碳酸盐岩储层多类多尺度建模方法研究: 以塔河油田四区奥陶系油藏为例[J]. 地学前缘, 2012, 19(2): 59-66.
[6]	鲁新便, 胡文革, 汪彦, 等. 塔河地区碳酸盐岩断溶体油藏特征与开发实践[J]. 石油与天然气地质, 2015, 36(3): 347-355.
[7]	徐敏. 深层碳酸盐岩岩溶缝洞储层综合识别新技术: 以四川盆地乐山-龙女寺古隆起震旦系灯影组为例[D]. 成都: 成都理工大学, 2016.
[8]	杨敏, 李小波, 谭涛, 等. 古暗河油藏剩余油分布规律及挖潜对策研究: 以塔河油田TK440井区为例[J]. 油气藏评价与开发, 2020, 10(2): 43-48.
[9]	赵龙飞, 熊昶, 杨凤英, 等. 缝洞型碳酸盐岩储层预测方法研究[C]// 2016中国地球科学联合学术年会. 北京: 中国和平音像电子出版社, 2016: 79.
[10]	张文彪, 张亚雄, 段太忠, 等. 塔里木盆地塔河油田托甫台区奥陶系碳酸盐岩断溶体系层次建模方法[J]. 石油与天然气地质, 2022, 43(1): 207-218.
[11]	张娟, 杨敏, 谢润成, 等. 塔里木盆地塔河油田4区和6区奥陶系小尺度缝洞储集体概率识别方法[J]. 石油与天然气地质, 2022, 43(1): 219-228.
[12]	冯志强, 李萌, 郭元岭, 等. 中国典型大型走滑断裂及相关盆地成因研究[J]. 地学前缘, 2022, 29(6): 206-223. DOI
[13]	杨德彬, 杨敏, 李新华, 等. 塔河油田碳酸盐岩小缝洞型储层特征及成因演化[J]. 油气地质与采收率, 2021, 28(1): 41-46.
[14]	鲁新便, 杨敏, 汪彦, 等. 塔里木盆地北部 “层控” 与 “断控” 型油藏特征: 以塔河油田奥陶系油藏为例[J]. 石油实验地质, 2018, 40(4): 461-469.
[15]	闫相宾, 韩振华, 李永宏. 塔河油田奥陶系油藏的储层特征和成因机理探讨[J]. 地质论评, 2002, 48(6): 619-626.
[16]	李阳. 塔河油田奥陶系碳酸盐岩溶洞型储集体识别及定量表征[J]. 中国石油大学学报(自然科学版), 2012, 36(1): 1-7.
[17]	曹建文, 金意志, 夏日元, 等. 塔河油田4区奥陶系风化壳古岩溶作用标志及控制因素分析[J]. 中国岩溶, 2012, 31(2): 220-226.
[18]	田飞, 金强, 李阳, 等. 塔河油田奥陶系缝洞型储层小型缝洞及其充填物测井识别[J]. 石油与天然气地质, 2012, 33(6): 900-908.
[19]	毛毳, 钟建华, 李勇, 等. 塔河油田奥陶系碳酸盐岩基质孔缝型储集体特征[J]. 石油勘探与开发, 2014, 41(6): 681-689.
[20]	周文. 裂缝性油气储集层评价方法[M]. 成都: 四川科学技术出版社, 1998.
[21]	徐微, 蔡忠贤, 林忠民, 等. 塔河油田奥陶系碳酸盐岩油藏岩溶成因类型[J]. 海相油气地质, 2012, 17(1): 66-72.
[22]	张希明. 新疆塔河油田下奥陶统碳酸盐岩缝洞型油气藏特征[J]. 石油勘探与开发, 2001, 28(5): 17-22, 15.
[23]	漆立新, 云露. 塔河油田奥陶系碳酸盐岩岩溶发育特征与主控因素[J]. 石油与天然气地质, 2010, 31(1): 1-12.
[24]	XIE R C, ZHOU W, ZHANG C, et al. Characteristics, influencing factors, and prediction of fractures in weathered crust Karst Reservoirs: a case study of the Ordovician Majiagou Formation in the Daniudi Gas Field, Ordos Basin, China[J]. Geological Journal, 2020, 55(12): 7790-7806. DOI URL
[25]	SAEIN A F, RIAHI Z T. Controls on fracture distribution in Cretaceous sedimentary rocks from the Isfahan region, Iran[J]. Geological Magazine, 2019, 156(6): 1092-1104. DOI URL
[26]	艾合买提江. 塔河油田碳酸盐岩缝洞系统成因及模式研究[D]. 青岛: 中国石油大学(华东), 2009.
[27]	蔡春芳, 李开开, 李斌, 等. 塔河地区奥陶系碳酸盐岩缝洞充填物的地球化学特征及其形成流体分析[J]. 岩石学报, 2009, 25(10): 2399-2404.
[28]	肖玉茹, 何峰煜, 孙义梅. 古洞穴型碳酸盐岩储层特征研究: 以塔河油田奥陶系古洞穴为例[J]. 石油与天然气地质, 2003, 24(1): 75-80, 86.
[29]	刘存革, 李国蓉, 吴勇. 新疆塔河油田下奥陶统碳酸盐岩储层成因类型与评价[J]. 沉积与特提斯地质, 2004, 24(1): 91-96.
[30]	祝贺, 刘家铎, 孟万斌, 等. 塔河油田托甫台地区中下奥陶统沉积相与储层特征分析[J]. 成都理工大学学报(自然科学版), 2008, 35(3): 232-237.
[31]	周文, 李秀华, 金文辉, 等. 塔河奥陶系油藏断裂对古岩溶的控制作用[J]. 岩石学报, 2011, 27(8): 2339-2348.
[32]	刘佳庚, 李静, 苏玉亮, 等. 塔河油田奥陶系储层构造应力场研究[J]. 地质力学学报, 2020, 26(1): 48-54.
[33]	周文, 张银德, 闫长辉, 等. 泌阳凹陷安棚油田核三段储层裂缝成因、期次及分布研究[J]. 地学前缘, 2009, 16(4): 157-165.
[34]	张冲, 周文, 谢润成, 等. 大牛地气田马五1-2 致密碳酸盐岩储层平缓构造带裂缝预测[J]. 东北石油大学学报, 2014, 38(3): 9- 17, 5-6.
[35]	边瑞康, 张金川, 唐玄, 等. 塔河油田深层能量场特征及其与油气运聚关系[J]. 石油勘探与开发, 2010, 37(4): 416-423.
[36]	陈志海, 戴勇, 郎兆新. 缝洞性碳酸盐岩油藏储渗模式及其开采特征[J]. 石油勘探与开发, 2005, 32(3): 101-105.
[37]	王淑萍. 塔河油田下奥陶统碳酸盐岩缝洞型储层结构特征及成因模式[D]. 青岛: 中国石油大学(华东), 2015: 32-40.
[38]	金强, 田飞. 塔河油田岩溶型碳酸盐岩缝洞结构研究[J]. 中国石油大学学报(自然科学版), 2013, 37(5): 15-21.
[39]	朱东亚, 金之钧, 张荣强, 等. 震旦系灯影组白云岩多级次岩溶储层叠合发育特征及机制[J]. 地学前缘, 2014, 21(6): 335-345. DOI
[40]	姜仁旗, 吴键, CASTAGNA J, 等. 地球物理技术最新进展: 高分辨率地震频率和相位属性分析技术研究与应用效果[J]. 地学前缘, 2023, 30(1): 199-212. DOI
[41]	王兆峰, 陈鑫, 王鹏, 等. 缝洞型碳酸盐岩储集体特征及预测: 以A油田Pz段为例[J]. 石油与天然气地质, 2012, 33(3): 459-466.
[42]	陈钢花, 胡琮, 曾亚丽, 等. 基于BP神经网络的碳酸盐岩储层缝洞充填物测井识别方法[J]. 石油物探, 2015, 54(1): 99-104. DOI
[43]	李守巨. 深度学习神经网络在力学模型参数估计中的应用研究进展[J]. 人工智能与机器人研究, 2020, 9(2): 100-109.
[44]	SHEN S, SADOUGHI M, CHEN X Y. A deep learning method for online capacity estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2019, 25: 100817. DOI URL