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Quantitative prediction technology for the hydrocarbon production capacity of fractures and vugs in fault-controlled carbonate reservoirs in the Shunbei area |
LIU Jun1,2( ), HUANG Chao2, YANG Lin2, ZHANG Yong-Sheng2, ZHA Ming1 |
1. School of Geosciences,China University of Petroleum,Qingdao 266580,China 2. Exploration and Development Research Institute,Sinopec Northwest China Petroleum Bureau,Urumqi 830011,China |
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Abstract The strike-slip fault system and the fractures and vugs in fault-controlled reservoirs have become new Ordovician exploration targets in the Shunbei area.This study explored a multi-attribute quantitative prediction technique for the single-well production of fault-controlled reservoirs,aiming to provide a scientific basis and technical support for well deployment and well trajectory optimization in the fault zones of the Shunbei area.Faults serve as pathways for hydrocarbon migration in fault-controlled reservoirs.Their connectivity to provenance areas can be characterized by their longitudinal extension and the width of the fault zone,and their convergence can be characterized by their lateral extension and the planar intersection structure between major and secondary faults.Both characteristics of faults were used to indicate the fault characteristics in the oil source conditions for oil accumulation.The space for hydrocarbon accumulation in fault-controlled reservoirs is dominated by dissolution fractures and vugs,such as caves.The fracture-vug characteristics,which represent the space for hydrocarbon accumulation and its connectivity,were characterized by the scale of fault-controlled reservoirs,the volume of the fractures and vugs,and the density of fractures in faults.The single-well production of fault-controlled reservoirs is related to both the fault characteristics and the fracture-vug characteristics.Therefore,the quantification of fault characteristics and the fracture-vug characteristics is the basis for the quantitative prediction of single-well production.The fault characteristic values were determined through the fine-scale interpretation of faults based on seismic data and their multiple attributes.Moreover,the range of fault-controlled reservoirs was delineated based on structure tensors;the distribution and volume of caves,fractures,and vugs were characterized using the amplitude of anomalous bodies;and the density of fractures in faults can be characterized using the maximum likelihood probability.Then,the three attributes were fused to determine the fracture-vug characteristic values.Finally,the quantified fault characteristic values and fractured-vug characteristic values were fused into the characteristic value of fault-controlled reservoirs.Through the statistical analysis of the characteristic value and annual liquid production of drilled wells,the statistical relationship between the annual fluid production and the characteristic value of fault-controlled reservoirs was determined,thus achieving the quantitative prediction of the single-well production of fault-controlled reservoirs.
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Received: 22 February 2022
Published: 05 July 2023
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Seismic response characteristics of different fault types a—compressional fault;b—pull-apart fault;c—translation fault
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Structural characteristics plane of intersecting fracture
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The migration and its three attributes section a—migration;b—abnormal body amplitude attribute;c—structural gradient tensor attribute;d—maximum likelihood probability attribute
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T 7 6- abnormal body amplitude attribute;b— - structural gradient tensor attribute;c— - maximum likelihood probability attribute ">
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Plans of attributes of abnormal body amplitude,structural gradient tensor and maximum likelihood probability at different horizons a— - abnormal body amplitude attribute;b— - structural gradient tensor attribute;c— - maximum likelihood probability attribute
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Sections of migration(a),abnormal body amplitude attribute(b),structural gradient tensor attribute(c),maximum likelihood probability attribute(d) across a high productivity well
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High-productivity wells at the intersection of main and branch faults
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断裂特征 | 断裂通源性 | 断裂汇聚性 | 延伸至 以下(I) | 延伸在 以上(II) | 主干+分支 交汇部位(I) | 仅有主 干断裂(II) | 量化值 | 0.5 | 0 | 0.5 | 0 |
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Quantitative classification of nature to communicate oil sources and nature to convergence more oil sources
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Forward modeling of the correlation between the amplitude of beads and the height and width of caves a—the cave forward modeling migration profile with a width of 60 m and a height of 0~40 m;b—the cave forward modeling migration profile with height of 6 m and width of 10~300 m;c—the amplitude variation curve of the beads with a width of 60 m and a height of 0~40 m;d—the amplitude variation curve of the beads with a height of 6 m and a width of 10~300 m
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井名 | 单井年产液量/ t | 断裂特征值 | 缝洞特征值 | 断控缝洞体特征值 | SWOC | A1 | A2 | A | B | | S1 | 77000 | 0.5 | 0.5 | 1 | 3484.184 | 6968.368 | S2 | 54000 | 0.5 | 0.5 | 1 | 3546.903 | 7093.806 | S3 | 42000 | | | 0 | 7605.866 | 7605.866 | S4 | 27000 | 0.5 | 0 | 0.5 | 4052.342 | 6078.515 | S5 | 25000 | | | 0 | 1985.622 | 1985.622 | S6 | 24000 | | | 0 | 2696.085 | 2696.085 | S7 | 24000 | | | 0 | 1354.254 | 1354.254 | S8 | 22000 | | | 0 | 2408.270 | 2408.27 | S9 | 21000 | | | 0 | 2810.141 | 2810.141 | S10 | 21000 | | | 0 | 2207.304 | 2207.304 | S11 | 19000 | | | 0 | 521.6 | 521.6 | S12 | 18000 | | | 0 | 3565.860 | 3565.860 | S13 | 12000 | | | 0 | 3936.628 | 3936.628 | S14 | 7000 | | | 0 | 1609.080 | 1609.080 | S15 | 5000 | | | 0 | 1503.801 | 1503.801 | S16 | 1000 | | | 0 | 1406.6 | 1406.6 |
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Annual fluid production SWOC and fault-controlled reservoirs features value EFK of 16 sample wells were calculated
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Variation trend curve of single well annual fluid yield and fault-controlled reservoirs features value
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The fitting relation curve between the annual liquid production of a single well and the fault-controlled reservoirs features value
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Plan of predicted annual liquid production in Shunbei 1 area
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序号 | 井名 | 年累计产 液量/万t | 按产液 量分级 | 预测产 量分级 | 预测 符合情况 | 1 | S1 | 7.7 | 高产井 | 高产 | √ | | 2 | S2 | 5.4 | 高产 | √ | | 3 | S3 | 4.2 | 高产 | √ | | 4 | S4 | 3.6 | 低产 | | × | 5 | S5 | 2.7 | 中产井 | 低产 | | × | 6 | S6 | 2.5 | 中产 | √ | | 7 | S7 | 2.4 | 高产 | | × | 8 | S8 | 2.4 | 中产 | √ | | 9 | S9 | 2.2 | 低产 | | × | 10 | S10 | 2.1 | 高产 | | × | 11 | S11 | 2.1 | 中产 | √ | | 12 | S12 | 1.9 | 中产 | √ | | 13 | S13 | 1.3 | 中产 | √ | | 14 | S14 | 1.2 | 低产 | | × | 15 | S15 | 1.1 | 中产 | √ | | 16 | S16 | 1 | 中产 | √ | | 17 | S17 | 0.5 | 低产 | 低产 | √ | | 18 | S18 | 0.1 | 低产 | √ | | 19 | S19 | 0.1 | 低产 | √ | | 20 | S20 | 0 | 中产 | | × | 总计 预测符合率 | 13 | 7 | 65% | 35% |
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Coincident rate of predicted annual liquid production in Shunbei 1 area
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[1] |
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.
|
[1] |
Lu X B, Hu W G, Wang Y, et al. Characteristics and development practice of fault-karst carbonate reservoirs in Tahe area,Tarim Basin[J]. Oil & Gas Geology, 2015, 36(3):347-355.
|
[2] |
焦方正. 塔里木盆地顺北特深碳酸盐岩断溶体油气藏发现意义与前景[J]. 石油与天然气地质, 2018, 39(2):208-216.
|
[2] |
Jiao F Z. Significance and prospect of ultra-deep carbonate fault-karst reservoirs in Shunbei area,Tarim Basin[J]. Oil & Gas Geology, 2018, 39(2):208-216.
|
[3] |
吴涛, 戴少康, 曹飞, 等. 走滑断裂系对碳酸盐岩“断溶体”油藏形成的控制作用——以塔河油田托普台北为例[J]. 地球科学前沿, 2017, 7(5):681-694.
|
[3] |
Wu T, Dai S K, Cao F, et al. Strike slip fault system and it's controlling on the formation of "reservoir of fault controlling dissoulution":A case study of Toufutai Area,Tahe Oil Fields[J]. Advances in Geosciences, 2017, 7(5):681-694.
|
[4] |
马乃拜, 金圣林, 杨瑞召, 等. 塔里木盆地顺北地区断溶体地震反射特征与识别[J]. 石油地球物理勘探, 2019, 54(2):398-403.
|
[4] |
Ma N B, Jin S L, Yang R Z, et al. Seismic reservoir characteristics and identification of fault karst reservoir in Shunbei area,Tarim Basin[J]. Oil Geophysical Prospecting, 2019, 54(2):398-403.
|
[5] |
王震, 文欢, 邓光校, 等. 塔河油田碳酸盐岩断溶体刻画技术研究与应用[J]. 石油物探, 2019, 58(1):149-154.
|
[5] |
Wang Z, Wen H, Deng G X, et al. Fault-karst characterization technology in the Tahe Oilfield,China[J]. Geophysical Prospecting for Petroleum, 2019, 58(1):149-154.
|
[6] |
李鹏飞, 崔德育, 田浩男. 塔里木盆地塔北地区X区块断溶体刻画方法与效果[J]. 石油地球物理勘探, 2017, 52(s1):189-194.
|
[6] |
Li P F, Cui D Y, Tian H N. Fault karst carbonate reservoir description with GeoEast interpretation sub-system in the Tabei area,Tarim Basin[J]. Oil Geophysical Prospecting, 2017, 52(s1):189-194.
|
[7] |
徐红霞, 沈春光, 李斌, 等. 多属性分析技术在碳酸盐岩断溶体预测中的应用[J]. 石油地球物理勘探, 2017, 52(s2):158-163.
|
[7] |
Xu H X, Shen C G, Li B, et al. Fault-karst carbonate reservoir prediction with comprehensive multi-attribute analysis[J]. Oil Geophysical Prospecting, 2017, 52(s2):158-163.
|
[8] |
唐文榜, 韩革华, 刘来祥, 等. 溶洞充填物判识的频率差异分析技术[J]. 石油与天然气地质, 2002, 23(1):41-44.
|
[8] |
Tang W B, Han G H, Liu L X, et al. Analysis technique of difference for discrimination of cavity fillers[J]. Oil & Gas Geology, 2002: 23(1):41-44.
|
[9] |
姚姚, 唐文榜. 深层碳酸盐岩岩溶风化壳洞缝型油气藏可检测性的理论研究[J]. 石油地球物理勘探, 2003, 38(6):623-629.
|
[9] |
Yao Y, Tang W B. The theoretical researching of detecting for deeply carbonate grotto mantlerrock cavity reservoir[J]. Oil Geophysical Prospecting, 2003, 38(6):623-629.
|
[10] |
刘坤岩, 许杰. 塔河奥陶系隐蔽溶洞体地震精细识别[J]. 石油地球物理勘探, 2019, 54(5):1106-1114.
|
[10] |
Liu K Y, Xu J. Identification of Ordovician subtle cave reservoirs in Tahe on seismic data[J]. Oil Geophysical Prospecting, 2019, 54(5):1106-1114.
|
[11] |
王晓梅, 唐文榜, 等.李学著, 碳酸盐岩溶洞型储层反演方法[J]. 物探与化探, 2005, 29(1):44-46.
|
[11] |
Wang X M, Tang W B, Li X Z, et al. An inversion techniques for water-eroded case type carbonate reservoirs[J]. Geophysical and Geochemical Exploration, 2005, 29(1):44-46.
|
[12] |
孙海宁, 王晓梅, 刘来祥. AVO技术在识别充填流体溶洞中的应用[J]. 物探与化探, 2008, 32(4):397-399.
|
[12] |
Sun H N, Wang X M, Liu L X. The application of AVO to the predication of water-eroded caves filled with liquids for carbonate reservoirs[J]. Geophysical and Geochemical Exploration, 2008, 32(4):397-399.
|
[13] |
李宗杰, 刘群, 李海英, 等. 塔河油田缝洞储集体油水识别的谐频特征分析技术应用研究[J]. 石油物探, 2014, 53(4):484-490.
|
[13] |
Li Z J, Liu Q, Li H Y, et al. Application of HFC technique for hydrocarbon identification in fracture-cave reservoirs of lower-Ordovician Carbonate in Tahe oilfield[J]. Geophysical Prospecting for Petroleum, 2014, 53(4):484-490.
|
[14] |
樊佳方, 李宗杰, 韩革华, 等. 基于属性差异薄储集层(体)流体预测技术[C]// 北京: SPG/SEG 南京2020国际地球物理会议论文集, 2020:723-726.
|
[14] |
Fan J F, Li Z J, Han G H, et al. Fluid prediction techniques for thin reservoirs based on seismic attribute differences[C]// Beijing: SPG/SEG Nanjing 2020 International Geophysical Conference, 2020:723-726.
|
[15] |
马艺璇, 李慧莉, 刘坤岩, 等. 基于分频相干体的蚂蚁追踪技术在塔河油田断裂刻画中的应用[J]. 石油物探, 2020, 59(2):258-266.
|
[15] |
Ma Y X, Li H L, Liu K Y, et al. Application of an ant-tracking technique based on spectral decomposition to fault characterization[J]. Geophysical Prospecting for Petroleum, 2020, 59(2):258-266.
|
[16] |
陈双全, 季敏. 地震数据结构张量相干计算方法[J]. 石油物探, 2012, 51(3):234-238.
|
[16] |
Chen S Q, Ji M. Structure tensor coherence computation method of seismic data[J]. Geophysical Prospecting for Petroleum, 2012, 51(3):234-238.
|
[17] |
梁志强. 不同尺度裂缝的叠后地震预测技术研究[J]. 石油物探, 2019, 58(5):766-772.
|
[17] |
Liang Z Q. Post stack seismic prediction techniques for fractures of different scales[J]. Geophysical Prospecting for Petroleum, 2019, 58(5):766-772.
|
[18] |
刘振峰, 曲寿利, 孙建国, 等. 地震裂缝预测技术研究进展[J]. 石油物探, 2012, 51(2):191-196.
|
[18] |
Liu Z F, Qu S L, Sun J G, et al. Progress of seismic fracture characterization technology[J]. Geophysical Prospecting for Petroleum, 2012, 51(2):191-196.
|
[19] |
朱成宏, 黄国骞, 秦瞳. 断裂系统精细分析技术[J]. 石油物探, 2002, 41(1):42-48.
|
[19] |
Zhu C H, Huang G Q, Qin T. Methods for detailed fracture system description[J]. Geophysical Prospecting for Petroleum, 2002, 41(1):42-48.
|
[20] |
黄苇, 周捷, 高利君, 等. 基于同步挤压改进短时傅里叶变换的分频蚂蚁追踪在断裂识别中的应用[J]. 物探与化探, 2021, 45(2):432-439.
|
[20] |
Huang W, Zhou J, Gao L J, et al. The application of frequency division ant tracking based on synchronous extrusion improvement of short time Fourier transform in crack detection[J]. Geophysical and Geochemical Exploration, 2021, 45(2):432-439.
|
[21] |
马玉歌, 苏朝光, 张健, 等. 低序级断层结构导向坎尼属性边缘检测识别方法[J]. 物探与化探, 2020, 44(3):698-703.
|
[21] |
Ma Y G, Su C G, Zhang J, et al. Low-order fault structure-oriented Canny property edge detection and recognition method[J]. Geophysical and Geochemical Exploration, 2020, 44(3):698-703.
|
|
|
|