基于核磁共振测井的砂砾岩储层分类与产能预测方法

doi:10.7623/syxb202205006

石油学报 ›› 2022, Vol. 43 ›› Issue (5): 648-657.DOI: 10.7623/syxb202205006

基于核磁共振测井的砂砾岩储层分类与产能预测方法

金国文¹, 王堂宇¹, 刘忠华², 谢然红¹, 邵亮¹, 李博宇¹

1. 中国石油大学(北京)油气资源与探测国家重点实验室北京 102249;
2. 中国石油勘探开发研究院北京 100083

收稿日期:2020-07-16 修回日期:2022-01-22 发布日期:2022-05-28
通讯作者: 谢然红,女,1966年8月生,2008年获中国石油大学(北京)博士学位,现为中国石油大学(北京)地球物理学院教授,主要从事岩石物理、核磁共振测井方法及应用、测井储层评价等方面的研究工作。Email:xieranhong@cup.edu.cn
作者简介:金国文,男,1991年1月生,2021年获中国石油大学(北京)博士学位,现为中国石油大学(北京)博士后,主要从事岩石物理与地球物理测井方面研究。Email:jinguowen_cup@163.com
基金资助:
国家自然科学基金项目（No.42174131）资助。

Classification and productivity prediction of glutenite reservoirs based on NMR logging

Jin Guowen¹, Wang Tangyu¹, Liu Zhonghua², Xie Ranhong¹, Shao Liang¹, Li Boyu¹

1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China;
2. PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China

Received:2020-07-16 Revised:2022-01-22 Published:2022-05-28

摘要/Abstract

摘要： 砂砾岩储层具有岩石颗粒粒径范围大、低孔低渗、孔隙结构复杂和非均质性强等特点，其品质分类与产能预测难度较大。基于核磁共振测井，提出了砂砾岩储层的分类方案与产能预测方法。利用岩石核磁共振实验测量的横向弛豫时间（T₂）分布，提取三孔隙组分百分比（小孔隙组分百分比S₁、中孔隙组分百分比S₂、大孔隙组分百分比S₃）、T₂分布几何平均值（T_{2_LM}）等T₂分布特征参数，构建了综合反映储层孔隙结构和岩石物理性质的储层品质指数（I_RQ）。T₂分布特征参数和储层品质指数与孔喉半径、排驱压力等压汞实验参数具有良好的相关性，可定量表征岩石孔隙结构。利用S₃/S₁和I_RQ建立了砂砾岩储层分类图版，基于S₃/S₁、T_{2_LM}、I_RQ和储层有效厚度（H）构建了砂砾岩储层的产能预测综合指数（F）和产能预测模型。基于核磁共振测井的砂砾岩储层分类方法和产能预测模型在准噶尔盆地玛湖凹陷西斜坡百口泉组砂砾岩储层研究中取得了良好的应用效果，验证了该方法的有效性和实用性。研究方法与认识对开展砂砾岩储层的分类与产能研究具有一定参考意义。

关键词: 砂砾岩储层, 核磁共振测井, 孔隙结构, 储层分类, 产能预测

Abstract: Glutenite reservoir is characterized by large particle size range, low porosity and permeability, complex pore structure and strong heterogeneity, so it is difficult to predict its quality classification and productivity. Based on NMR logging, this paper proposes the classification scheme and productivity prediction method of glutenite reservoirs. Using the transverse relaxation time (T₂) distribution measured by NMR experiment of rocks, the paper extracts the characteristic parameters of T₂ distribution such as percentages of three-pore components (percentage of small pore components S₁, percentage of medium pore components S₂ and percentage of large pore components S₃) and geometric mean of T₂ distribution (T_{2_LM}), and builds the reservoir quality index (I_RQ) comprehensively reflecting the pore structure and physical properties of rock. Characteristic parameters of T₂ distribution and reservoir quality index have a good correlation with pore throat radius, displacement pressure and other mercury penetration experiment parameters, which can quantitatively characterize the rock pore structure. The classification chart of glutenite reservoirs has been established using S₃/S₁ and I_RQ. The productivity prediction comprehensive index (F) and the productivity prediction model of glutenite reservoir have been built based on S₃/S₁, T_{2_LM}, I_RQ and effective thickness (H) of the reservoir. The classification method and productivity prediction model of glutenite reservoirs based on NMR logging have achieved a good application effect in the glutenite reservoir of Baikouquan Formation in the west slope of Mahu sag, Junggar Basin, and the effectiveness and practicability of the method have also been verified, wichi is of certain reference significance for the research of glutenite reservoir classification and productivity.

Key words: glutenite reservoir, NMR logging, pore structure, reservoir classification, production prediction

中图分类号:

TE34

金国文, 王堂宇, 刘忠华, 谢然红, 邵亮, 李博宇. 基于核磁共振测井的砂砾岩储层分类与产能预测方法[J]. 石油学报, 2022, 43(5): 648-657.

Jin Guowen, Wang Tangyu, Liu Zhonghua, Xie Ranhong, Shao Liang, Li Boyu. Classification and productivity prediction of glutenite reservoirs based on NMR logging[J]. Acta Petrolei Sinica, 2022, 43(5): 648-657.

参考文献

[1] 瞿建华, 张磊, 吴俊, 等.玛湖凹陷西斜坡百口泉组砂砾岩储集层特征及物性控制因素[J].新疆石油地质, 2017, 38(1):1-6.
QU Jianhua, ZHANG Lei, WU Jun, et al. Characteristics of sandy conglomerate reservoirs and controlling factors on physical properties of Baikouquan Formation in the western slope of Mahu sag, Junggar Basin[J].Xinjiang Petroleum Geology, 2017, 38(1):1-6.
[2] 庞小军, 牛成民, 杜晓峰, 等.渤海海域石臼坨凸起东北缘沙河街组一段+二段砂砾岩储层差异定量表征[J].石油学报, 2020, 41(9):1073-1088.
PANG Xiaojun, NIU Chengmin, DU Xiaofeng, et al.Quantitative characterization of differences in glutenite reservoir in the Member 1 and 2 of Shahejie Formation in the northeastern margin of Shijiutuo uplift, Bohai Sea[J].Acta Petrolei Sinica, 2020, 41(9):1073-1088.
[3] 林红梅, 刘鹏, 王彤达, 等.基于母岩类型差异的深层砂砾岩储集体成岩演化机制——以渤海湾盆地车镇凹陷沙河街组三段下亚段为例[J].石油学报, 2019, 40(10):1180-1191.
LIN Hongmei, LIU Peng, WANG Tongda, et al.Diagenetic evolution mechanism of deep glutenite reservoirs based on differences in parent rock types:a case study of lower submember of Member 3 of Shahejie Formation in Chezhen sag, Bohai Bay Basin[J].Acta Petrolei Sinica, 2019, 40(10):1180-1191.
[4] LIU Mi, XIE Ranhong, LI Changxi, et al.A new method for determining tight sandstone permeability based on the characteristic parameters of the NMR T₂ distribution[J].Applied Magnetic Resonance, 2017, 48(10):1009-1029.
[5] WU Bohan, XIE Ranhong, WANG Xiaoyu, et al.Characterization of pore structure of tight sandstone reservoirs based on fractal analysis of NMR echo data[J].Journal of Natural Gas Science & Engineering, 2020, 81:103483.
[6] MARSCHALL D, GARDNER J S, MARDON D, et al.Method for correlating NMR relaxometry and mercury injection data[C]//Proceedings of the International Symposium of Society of Core Analysts.1995:79-85.
[7] 何雨丹, 毛志强, 肖立志, 等.核磁共振T₂分布评价岩石孔径分布的改进方法[J].地球物理学报, 2005, 48(2):373-378.
HE Yudan, MAO Zhiqiang, XIAO Lizhi, et al.An improved method of using NMR T₂ distribution to evaluate pore size distribution[J].Chinese Journal of Geophysics, 2005, 48(2):373-378.
[8] 邵维志, 丁娱娇, 刘亚, 等.核磁共振测井在储层孔隙结构评价中的应用[J].测井技术, 2009, 33(1):52-56.
SHAO Weizhi, DING Yujiao, LIU Ya, et al.The application of NMR log data in evaluation of reservoir pore structure[J].Well Logging Technology, 2009, 33(1):52-56.
[9] LIU Mi, XIE Ranhong, XU Hongjun, et al.A new method for predicting capillary pressure curves based on NMR logging in tight sandstone reservoirs[J].Applied Magnetic Resonance, 2018, 49(10):1043-1058.
[10] LIU Z H, ZHOU C C, ZHANG L H, et al.An innovative method to evaluate formation pore structure using NMR logging data[R].SPWLA 2007-S, 2007.
[11] 陈国军, 高明, 李静, 等.核磁共振测井在致密油储层孔隙结构评价中的应用[J].天然气勘探与开发, 2014, 37(2):41-44.
CHEN Guojun, GAO Ming, LI Jing, et al.Application of NMR well logging to evaluating porous structure of tight-oil reservoir[J].Natural Gas Exploration and Development, 2014, 37(2):41-44.
[12] GE Xinmin, FAN Yiren, CAO Yingchang, et al.Reservoir pore structure classification technology of carbonate rock based on NMR T₂ spectrum decomposition[J].Applied Magnetic Resonance, 2014, 45(2):155-167.
[13] LIU Mi, XIE Ranhong, GUO Jiangfeng, et al.Characterization of pore structures of tight sandstone reservoirs by multifractal analysis of the NMR T₂ distribution[J].Energy & Fuels, 2018, 32(12):12218-12230.
[14] YAN Jianping, HE Xu, ZHANG Shaolong, et al.Sensitive parameters of NMR T₂ spectrum and their application to pore structure characterization and evaluation in logging profile:a case study from Chang 7 in the Yanchang Formation, Heshui area, Ordos Basin, NW China[J].Marine and Petroleum Geology, 2020, 111:230-239.
[15] 吴进波.MRGC方法在储层分类及渗透率精细评价中的应用[J].海洋石油, 2017, 37(1):49-53.
WU Jinbo.Application of reservoir classification and permeability elaborate evaluation with MRGC method[J].Offshore Oil, 2017, 37(1):49-53.
[16] 路研, 徐守余, 刘可禹, 等.陆相滑塌浊积扇储层流动单元定量表征[J].中国石油大学学报:自然科学版, 2019, 43(6):1-10.
LU Yan, XU Shouyu, LIU Keyu, et al.Quantitative characterization of reservoir flow units of basin floor-fan turbidite[J].Journal of China University of Petroleum:Edition of Natural Science, 2019, 43(6):1-10.
[17] 张程恩, 潘保芝, 刘倩茹.储层品质因子RQI结合聚类算法进行储层分类评价研究[J].国外测井技术, 2012(4):11-13.
ZHANG Cheng'en, PAN Baozhi, LIU Qianru.Reservoir classification evaluation research with combination of reservoir quality factor RQI and clustering algorithm[J].World Well Logging Technology, 2012(4):11-13.
[18] 王英伟, 张超谟, 严伟, 等.核磁共振测井在致密砂岩气层储层分类评价中的应用[J].石油天然气学报, 2012, 34(1):75-79.
WANG Yingwei, ZHANG Chaomo, YAN Wei, et al.Application of NMR logging in identification of tight sandstone gas reservoirs[J].Journal of Oil and Gas Technology, 2012, 34(1):75-79.
[19] 马立民, 林承焰, 范梦玮.基于微观孔隙结构分形特征的定量储层分类与评价[J].石油天然气学报, 2012, 34(5):15-19.
MA Limin, LIN Chengyan, FAN Mengwei.Quantitative classification and evaluation of reservoirs based on fractal features of micro-pore structures[J].Journal of Oil and Gas Technology, 2012, 34(5):15-19.
[20] 刘航宇, 田中元, 刘波, 等.中东地区巨厚强非均质碳酸盐岩储层分类与预测——以伊拉克W油田中白垩统Mishrif组为例[J].石油学报, 2019, 40(6):677-691.
LIU Hangyu, TIAN Zhongyuan, LIU Bo, et al.Classification and prediction of giant thick strongly heterogeneous carbonate reservoirs in the Middle East area:a case study of Mid-Cretaceous Mishrif Formation in the W oilfield of Iraq[J].Acta Petrolei Sinica, 2019, 40(6):677-691.
[21] 孙晓霞.准噶尔盆地永进油田特低孔隙度超低渗透率储层分类评价[J].测井技术, 2012, 36(5):479-484.
SUN Xiaoxia.On classification and evaluation of extra-low porosity and permeability reservoir in Yongjin oilfield, Dzungaria Basin[J].Well Logging Technology, 2012, 36(5):479-484.
[22] 干磊, 何东博, 郭建林, 等.机器学习方法在储层分类中的应用[J].数学的实践与认识, 2019, 49(13):138-144.
GAN Lei, HE Dongbo, GUO Jianlin, et al.Application of machine learning method in reservoir classification[J].Mathematics in Practice and Theory, 2019, 49(13):138-144.
[23] 刘伟, 张晋言, 张文姣, 等.基于电成像测井资料的砂砾岩储层有效性分类评价方法[J].石油钻探技术, 2016, 44(4):114-119.
LIU Wei, ZHANG Jinyan, ZHANG Wenjiao, et al.An evaluation method for glutenite reservoir effectiveness classification based on electrical imaging logging data[J].Petroleum Drilling Techniques, 2016, 44(4):114-119.
[24] 杨兵, 张占松, 张超谟.渤中19-6气田孔店组砂砾岩储层分类研究[J].当代化工, 2020, 49(12):2786-2790.
YANG Bing, ZHANG Zhansong, ZHANG Chaomo.Investigation of reservoir classification of glutenites in Bozhong 19-6 gas field[J].Contemporary Chemical Industry, 2020, 49(12):2786-2790.
[25] 边会媛, 韩博华, 王飞, 等.柴北缘牛东地区砂砾岩储层特征及分类评价[J].西安科技大学学报, 2020, 40(5):894-901.
BIAN Huiyuan, HAN Bohua, WANG Fei, et al.Characteristics and classification of glutenite reservoirs in Niudong area, north margin of Qaidam Basin[J].Journal of Xi'an University of Science and Technology, 2020, 40(5):894-901.
[26] 李郑辰, 钟淑敏, 杨永军.朝长地区扶余油层储层分类与产能预测[J].地球物理学进展, 2013, 28(5):2561-2568.
LI Zhengchen, ZHONG Shumin, YANG Yongjun.Reservoir classification and productivity forecast in Fuyu layer Chaochang district[J].Progress in Geophysics, 2013, 28(5):2561-2568.
[27] 庞秋维, 霍勇, 马强, 等.X油田试油层自然产能预测方法研究[J].长江大学学报:自科版, 2014, 11(8):85-86.
PANG Qiuwei, HUO Yong, MA Qiang, et al.Natural productivity predicting method of production testing formation in X oilfield[J].Journal of Yangtze University:Natural Science Edition, 2014, 11(8):85-86.
[28] 白莎, 蔡文渊, 熊葵, 等.基于老井测井资料和孔隙结构参数的产能预测[J].测井技术, 2020, 44(2):215-220.
BAI Sha, CAI Wenyuan, XIONG Kui, et al.Forecast of production capacity based on log data and pore structures from old wells[J].Well Logging Technology, 2020, 44(2):215-220.
[29] KLEINBERG R L, KENYON W E, MITRA P P.Mechanism of NMR relaxation of fluids in rock[J].Journal of Magnetic Resonance, Series A, 1994, 108(2):206-214.
[30] 司兆伟, 赵建斌, 白松涛, 等.基于岩石物理与核磁测井的储层分类方法研究——以冀东油田某区块为例[J].石油天然气学报, 2013, 35(12):73-78.
SI Zhaowei, ZHAO Jianbin, BAI Songtao, et al.Research of reservoir classification by NMR logging based on rock physics——by taking a block of Jidong oilfield for example[J].Journal of Oil and Gas Technology, 2013, 35(12):73-78.
[31] MORRISS C E, FREEDMAN R, STRALEY C, et al.Hydrocarbon saturation and viscosity estimation from NMR logging in the Belridge Diatomite[J].Log Analyst, 1997, 38(2):44-59.
[32] CHANG Dahai, VINEGAR H J, MORRISS C, et al.Effective porosity, producible fluid, and permeability in carbonates from NMR logging[J].Log Analyst, 1997, 38(2):60-72.
[33] 陈彦竹, 李戈理, 及成林, 等.利用储层品质指数提高测井产能预测精度——以鄂尔多斯盆地二叠系S₃²储层为例[J].国外测井技术, 2017, 38(5):17-24.
CHEN Yanzhu, LI Geli, JI Chenglin, et al.Improving logging prediction precision of the production by using reservoir quality index:the Permian SHAN₃² area in Ordos Basin[J].World Well Logging Technology, 2017, 38(5):17-24.
[34] 张涛, 张宪国, 林承焰, 等.基于常规测井的低渗透储层孔隙结构评价[J].成都理工大学学报:自然科学版, 2014, 41(4):413-421.
ZHANG Tao, ZHANG Xianguo, LIN Chengyan, et al.Evaluation of pore structure in low permeability reservoirs based on common well logs[J].Journal of Chengdu University of Technology:Science & Technology Edition, 2014, 41(4):413-421.
[35] 中华人民共和国国土资源部.石油天然气储量计算规范:DZ/T 0217-2005[S].北京:中国标准出版社, 2005.
Ministry of Land and Resources of the People's Republic of China.Regulation of petroleum reserves estimation:DZ/T 0217-2005[S].Beijing:Standards Press of China, 2005.
[36] 祝春生, 程林松, 阳忠华, 等.特低渗透砂岩油藏渗流特性研究[J].油气地质与采收率, 2008, 15(2):102-104.
ZHU Chunsheng, CHENG Linsong, YANG Zhonghua, et al.Flowing characteristics in extra-low permeability sandstone reservoirs[J].Petroleum Geology and Recovery Efficiency, 2008, 15(2):102-104.
[37] 谭成仟, 马娜蕊, 苏超.储层油气产能的预测模型和方法[J].地球科学与环境学报, 2004, 26(2):42-46.
TAN Chengqian, MA Narui, SU Chao.Model and method for oil and gas productivity prediction of reservoir[J].Journal of Earth Sciences and Environment, 2004, 26(2):42-46.

基于核磁共振测井的砂砾岩储层分类与产能预测方法

Classification and productivity prediction of glutenite reservoirs based on NMR logging

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	惠沙沙, 庞雄奇, 谌卓恒, 王琛茜, 施砍园, 胡涛, 胡耀, 李敏, 梅术星, 黎茂稳. 四川盆地海相页岩孔隙类型对孔隙空间贡献定量表征[J]. 石油学报, 2024, 45(3): 531-547.
[2]	陈义林, 秦勇, 张博, 杨天宇, 刘静, 王兰花, 洪勇. 制样粒径对不同变质程度煤孔隙结构的差异化影响[J]. 石油学报, 2023, 44(8): 1313-1332.
[3]	崔传智, 孙莲婷, 单高军, 杨勇, 吴义志. 多层合采油井的多措施同步优化方法[J]. 石油学报, 2023, 44(8): 1356-1364,1381.
[4]	郭豪, 王艳忠, 操应长, 孟涛, 王淑萍, 李宇志, 石泉清, 郭迎春, 王加明. 多期石油充注对中—深层碎屑岩储层压溶作用的影响——以车镇凹陷北带沙河街组三段下亚段砂砾岩储层为例[J]. 石油学报, 2023, 44(3): 485-499,517.
[5]	丁骞, 甘利灯, 魏乐乐, 张宇生, 杨昊, 姜晓宇, 王峣钧. 双孔隙结构因子及其在储层渗透性地震预测中的应用[J]. 石油学报, 2023, 44(2): 339-347.
[6]	唐淑玲, 汤达祯, 杨焦生, 邓泽, 李松, 陈世达, 冯鹏, 黄晨, 李站伟. 鄂尔多斯盆地大宁—吉县区块深部煤储层孔隙结构特征及储气潜力[J]. 石油学报, 2023, 44(11): 1854-1866,1902.
[7]	方正, 蒲秀刚, 陈世悦, 鄢继华, 陈星燃, 崔绮梦. 陆相湖盆深水区页岩高频层序对储层发育的影响——以渤海湾盆地沧东凹陷孔店组二段页岩为例[J]. 石油学报, 2023, 44(10): 1663-1682.
[8]	郭晖, 纪宝强, 杨森, 王然, 张顺存, 李佳思, 张生银, 邹妞妞, 史基安. 准噶尔盆地环玛湖凹陷二叠系砂砾岩储层沸石类胶结物的形成及石油地质意义[J]. 石油学报, 2022, 43(3): 341-354.
[9]	黄兴, 窦亮彬, 左雄娣, 高辉, 李天太. 致密油藏裂缝动态渗吸排驱规律[J]. 石油学报, 2021, 42(7): 924-935.
[10]	郜益华, 张迎春, 杨宝泉, 李珂, 苑志旺, 康博韬, 张昕, 张旭, 陈国宁. 复杂断块油田跨断层水平井产能预测及分段长度优化方法——以西非A深水油田为例[J]. 石油学报, 2021, 42(7): 948-961.
[11]	姜平, 张辉, 王磊, 汪新光, 向耀权. 北部湾低渗透油田储层“甜点”评价方法[J]. 石油学报, 2021, 42(6): 751-764.
[12]	李童, 龙安林, 刘波, 丁晓军, 于慧敏, 鲁珊珊, 宋彦辰, 石开波. 低渗透砂岩油藏隔夹层注气突破压力及注气开发策略——以柴达木盆地尕斯库勒油田E₃¹油藏为例[J]. 石油学报, 2021, 42(10): 1364-1372.
[13]	黄兴, 倪军, 李响, 薛俊杰, 柏明星, 周彤. 致密油藏不同微观孔隙结构储层CO₂驱动用特征及影响因素[J]. 石油学报, 2020, 41(7): 853-864.
[14]	王安民, 曹代勇, 聂敬, 秦荣芳. 巨厚煤储层孔隙结构的垂向非均质性特征——以青海聚乎更矿区为例[J]. 石油学报, 2020, 41(6): 691-701.
[15]	单长安, 张廷山, 梁兴, 胡冉冉, 赵卫卫. 富镜质组和富惰质组高阶煤纳米孔隙结构特征[J]. 石油学报, 2020, 41(6): 723-736.