Characterizing fracture networks by integrating hydrogeophysical data based on the ESMDA-DS method

JIAO Tingting; DENG Yaping; QIAN Jiazhong; LUO Qiankun

doi:10.16030/j.cnki.issn.1000-3665.202310004

2024 Vol. 51, No. 4

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Article navigation > Hydrogeology & Engineering Geology > 2024 > 51(4): 88-100

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JIAO Tingting, DENG Yaping, QIAN Jiazhong, LUO Qiankun. Characterizing fracture networks by integrating hydrogeophysical data based on the ESMDA-DS method[J]. Hydrogeology & Engineering Geology, 2024, 51(4): 88-100. doi: 10.16030/j.cnki.issn.1000-3665.202310004

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JIAO Tingting, DENG Yaping, QIAN Jiazhong, LUO Qiankun. Characterizing fracture networks by integrating hydrogeophysical data based on the ESMDA-DS method[J]. Hydrogeology & Engineering Geology, 2024, 51(4): 88-100. doi: 10.16030/j.cnki.issn.1000-3665.202310004

Characterizing fracture networks by integrating hydrogeophysical data based on the ESMDA-DS method

School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui　230009, China

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Corresponding author: DENG Yaping, dengyaping@hfut.edu.cn

Received Date: 02 October 2023

Revised Date: 18 December 2023

Publish Date: 15 July 2024

Abstract

Abstract

Characterizing fractured aquifers plays a crucial role in the issues related to groundwater contamination, and geothermal and hydrocarbon resource exploitation. Due to the heterogeneity of the fractured medium, the permeability of fractured medium generally exhibits significant non-Gaussian characteristics, leading to difficulties and challenges in the estimation of hydrogeological parameters. This study used the ESMDA-DS (ensemble smoother with multiple data assimilation-direct sampling) integrating hydrogeophysical data to explore the effectiveness of the data assimilation framework in portraying the parameter field of the fractured medium and to analyze the influences of assimilating three different types of observation data, the fracture density, and the number of observation wells on the parameter estimation. The results show that the method of ESMDA-DS integrating hydrogeophysical data can estimate the spatial distribution of hydrogeological parameters in the fractured medium effectively. Comparing the estimated results from three types of observation, it finds that fusing the hydraulic head and the self-potential observational data (hydrogeophysical data) has the best effect. The fracture density in the study area and the number of observation wells also affect the data assimilation results. A reasonable number of observation wells is suggested to obtain the optimal parameter estimation scheme in practical applications. This study can provide an effective method for characterizing the parameter field of the fractured medium and a reliable theoretical basis for the development and management of fractured water resources.
- fracture /
- hydrogeophysical method /
- data assimilation /
- multi-point geostatistics /
- parametric inversion

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References

[1]	LIU Longcheng,MENG Shuo,LI Chunguang. A new analytical solution of contaminant transport along a single fracture connected with porous matrix and its time domain random walk algorithm[J]. Journal of Hydrology,2022,610(1):127828. Google Scholar
[2]	JI Jiayan,SONG Xianzhi,SONG Guofeng,et al. Study on fracture evolution model of the enhanced geothermal system under thermal-hydraulic-chemical-deformation coupling[J]. Energy,2023,269:126604. doi: 10.1016/j.energy.2022.126604 CrossRef Google Scholar
[3]	张烈辉,贾鸣,张芮菡,等. 裂缝性油藏离散裂缝网络模型与数值模拟[J]. 西南石油大学学报(自然科学版),2017,39(3):121 − 127. [ZHANG Liehui,JIA Ming,ZHANG Ruihan,et al. Discrete fracture network modeling and numerical simulation of fractured reservoirs[J]. Journal of Southwest Petroleum University (Science & Technology Edition),2017,39(3):121 − 127. (in Chinese with English abstract)] Google Scholar ZHANG Liehui, JIA Ming, ZHANG Ruihan, et al. Discrete fracture network modeling and numerical simulation of fractured reservoirs[J]. Journal of Southwest Petroleum University （Science & Technology Edition）, 2017, 39（3）: 121 − 127. （in Chinese with English abstract） Google Scholar
[4]	石鸿蕾,郝奇琛,邵景力,等. 基于多源数据的弱透水层水文地质参数反演研究——以呼和浩特盆地某淤泥层为例[J]. 水文地质工程地质,2021,48(2):1 − 7. [SHI Honglei,HAO Qichen,SHAO Jingli,et al. Research on hydrogeological parameter inversion of an aquitard based on multi-source data:A case study of a silt layer in the Hohhot Basin[J]. Hydrogeology & Engineering Geology,2021,48(2):1 − 7. (in Chinese with English abstract)] Google Scholar SHI Honglei, HAO Qichen, SHAO Jingli, et al. Research on hydrogeological parameter inversion of an aquitard based on multi-source data: A case study of a silt layer in the Hohhot Basin[J]. Hydrogeology & Engineering Geology, 2021, 48（2）: 1 − 7. （in Chinese with English abstract） Google Scholar
[5]	吴延浩,江思珉,吴自军. 地下水污染强度及渗透系数场的反演识别研究[J]. 水文地质工程地质,2023,50(4):193 − 203. [WU Yanhao,JIANG Simin,WU Zijun. Identification of groundwater pollution intensity and hydraulic conductivity field[J]. Hydrogeology & Engineering Geology,2023,50(4):193 − 203. (in Chinese with English abstract)] Google Scholar WU Yanhao, JIANG Simin, WU Zijun. Identification of groundwater pollution intensity and hydraulic conductivity field[J]. Hydrogeology & Engineering Geology, 2023, 50（4）: 193 − 203. （in Chinese with English abstract） Google Scholar
[6]	陈梦迪,姜振蛟,霍晨琛. 考虑矿层渗透系数非均质性和不确定性的砂岩型铀矿地浸采铀过程随机模拟与分析[J]. 水文地质工程地质,2023,50(2):63 − 72. [CHEN Mengdi,JIANG Zhenjiao,HUO Chenchen. Stochastic modeling of in situ sandstone-type uranium leaching in response to uncertain and heterogeneous hydraulic conductivity[J]. Hydrogeology & Engineering Geology,2023,50(2):63 − 72. (in Chinese with English abstract)] Google Scholar CHEN Mengdi, JIANG Zhenjiao, HUO Chenchen. Stochastic modeling of in situ sandstone-type uranium leaching in response to uncertain and heterogeneous hydraulic conductivity[J]. Hydrogeology & Engineering Geology, 2023, 50（2）: 63 − 72. （in Chinese with English abstract） Google Scholar
[7]	KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering,1960,82(1):35 − 45. doi: 10.1115/1.3662552 CrossRef Google Scholar
[8]	VAN LEEUWEN P J,EVENSEN G. Data assimilation and inverse methods in terms of a probabilistic formulation[J]. Monthly Weather Review,1996,124(12):2898 − 2913. doi: 10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2 CrossRef Google Scholar
[9]	ZHANG Jiangjiang,LIN Guang,LI Weixuan,et al. An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions[J]. Water Resources Research,2018,54(3):1716 − 1733. doi: 10.1002/2017WR020906 CrossRef Google Scholar
[10]	EVENSEN G. The Ensemble Kalman Filter:Theoretical formulation and practical implementation[J]. Ocean Dynamics,2003,53(4):343 − 367. doi: 10.1007/s10236-003-0036-9 CrossRef Google Scholar
[11]	康学远,施小清,邓亚平,等. 基于EnKF融合地球物理数据刻画含水层非均质性[J]. 水科学进展,2018,29(1):40 − 49. [KANG Xueyuan,SHI Xiaoqing,DENG Yaping,et al. Assimilation of hydrogeophysical data for the characterization of subsurface heterogeneity using Ensemble Kalman Filter (EnKF)[J]. Advances in Water Science,2018,29(1):40 − 49. (in Chinese with English abstract)] Google Scholar KANG Xueyuan, SHI Xiaoqing, DENG Yaping, et al. Assimilation of hydrogeophysical data for the characterization of subsurface heterogeneity using Ensemble Kalman Filter （EnKF）[J]. Advances in Water Science, 2018, 29（1）: 40 − 49. （in Chinese with English abstract） Google Scholar
[12]	兰天,康学远,施小清,等. 基于EnKF综合水头和浓度观测数据推估地下水流模型参数[J]. 水文地质工程地质,2017,44(5):6 − 13. [LAN Tian,KANG Xueyuan,SHI Xiaoqing,et al. Joint assimilation of heads and concentrations for estimating parameters of groundwater flow models using the Ensemble Kalman Filter[J]. Hydrogeology & Engineering Geology,2017,44(5):6 − 13. (in Chinese with English abstract)] Google Scholar LAN Tian, KANG Xueyuan, SHI Xiaoqing, et al. Joint assimilation of heads and concentrations for estimating parameters of groundwater flow models using the Ensemble Kalman Filter[J]. Hydrogeology & Engineering Geology, 2017, 44（5）: 6 − 13. （in Chinese with English abstract） Google Scholar
[13]	CHEN Yan,OLIVER D S. Ensemble randomized maximum likelihood method as an iterative ensemble smoother[J]. Mathematical Geosciences,2012,44(1):1 − 26. doi: 10.1007/s11004-011-9376-z CrossRef Google Scholar
[14]	夏传安,王浩,简文彬. 基于相关性局域化迭代集合平滑反演渗透系数场[J]. 水文地质工程地质,2024,51(1):12 − 21. [XIA Chuanan,WANG Hao,JIAN Wenbin. Estimation of conductivity fields by using a correlation-based localization scheme of iterative ensemble smoother[J]. Hydrogeology & Engineering Geology,2024,51(1):12 − 21. (in Chinese)] Google Scholar XIA Chuanan, WANG Hao, JIAN Wenbin. Estimation of conductivity fields by using a correlation-based localization scheme of iterative ensemble smoother[J]. Hydrogeology & Engineering Geology, 2024, 51（1）: 12 − 21. （in Chinese） Google Scholar
[15]	EMERICK A,REYNOLDS A. Ensemble smoother with multiple data assimilation[J]. Computers & Geosciences,2013,55(3):3 − 15. Google Scholar
[16]	周念清,张瑞城,江思珉,等. ES-MDA算法融合ERT数据联合反演地下水污染源与含水层参数[J]. 南水北调与水利科技(中英文),2022,20(3):478 − 486. [ZHOU Nianqing,ZHANG Ruicheng,JIANG Simin,et al. Joint inversion of contaminant source and aquifer parameters by assimilating ERT data with the ES-MDA algorithm[J]. South-to-North Water Transfers and Water Science & Technology,2022,20(3):478 − 486. (in Chinese with English abstract)] Google Scholar ZHOU Nianqing, ZHANG Ruicheng, JIANG Simin, et al. Joint inversion of contaminant source and aquifer parameters by assimilating ERT data with the ES-MDA algorithm[J]. South-to-North Water Transfers and Water Science & Technology, 2022, 20（3）: 478 − 486. （in Chinese with English abstract） Google Scholar
[17]	HAN Zheng,KANG Xueyuan,WU Jichun,et al. Characterization of the non-Gaussian hydraulic conductivity field via deep learning-based inversion of hydraulic-head and self-potential data[J]. Journal of Hydrology,2022,610(11):127830. Google Scholar
[18]	CUI Fan,BAO Jichao,CAO Zhendan,et al. Soil hydraulic parameters estimation using ground penetrating radar data via ensemble smoother with multiple data assimilation[J]. Journal of Hydrology,2020,583(12):124552. Google Scholar
[19]	CAMPORESE M,CASSIANI G,DEIANA R,et al. Coupled and uncoupled hydrogeophysical inversions using ensemble Kalman filter assimilation of ERT-monitored tracer test data[J]. Water Resources Research,2015,51(5):3277 − 3291. doi: 10.1002/2014WR016017 CrossRef Google Scholar
[20]	JARDANI A,REVIL A,BOLÈVE A,et al. Tomography of the Darcy velocity from self-potential measurements[J]. Geophysical Research Letters,2007,34(12):L24403. Google Scholar
[21]	STREBELLE S,JOURNEL A. Reservoir modeling using multiple-point statistics[C]//Proceedings of SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers,2001:1-11. Google Scholar
[22]	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 CrossRef Google Scholar
[23]	ARPAT G B,CAERS J. Conditional simulation with patterns[J]. Mathematical Geology,2007,39(2):177 − 203. doi: 10.1007/s11004-006-9075-3 CrossRef Google Scholar
[24]	ZHANG Tuanfeng,SWITZER P,JOURNEL A. Filter-based classification of training image patterns for spatial simulation[J]. Mathematical Geology,2006,38(1):63 − 80. doi: 10.1007/s11004-005-9004-x CrossRef Google Scholar
[25]	MARIETHOZ G,RENARD P,STRAUBHAAR J. The direct sampling method to perform multiple-point geostatistical simulations[J]. Water Resources Research,2010,46(11):W11536. Google Scholar
[26]	CAO Zhendan,LI Liangping,CHEN Kang. Bridging iterative Ensemble Smoother and multiple-point geostatistics for better flow and transport modeling[J]. Journal of Hydrology,2018,565:411 − 421. doi: 10.1016/j.jhydrol.2018.08.023 CrossRef Google Scholar
[27]	宗成元,康学远,施小清,等. 基于多点地质统计与集合平滑数据同化方法识别非高斯渗透系数场[J]. 水文地质工程地质,2020,47(2):1 − 8. [ZONG Chengyuan,KANG Xueyuan,SHI Xiaoqing,et al. Characterization of non-Gaussian hydraulic conductivity fields using multiple-point geostatistics and ensemble smoother with multiple data assimilation method[J]. Hydrogeology & Engineering Geology,2020,47(2):1 − 8. (in Chinese with English abstract)] Google Scholar ZONG Chengyuan, KANG Xueyuan, SHI Xiaoqing, et al. Characterization of non-Gaussian hydraulic conductivity fields using multiple-point geostatistics and ensemble smoother with multiple data assimilation method[J]. Hydrogeology & Engineering Geology, 2020, 47（2）: 1 − 8. （in Chinese with English abstract） Google Scholar
[28]	JOUGNOT D,LINDE N,REVIL A,et al. Derivation of soil-specific streaming potential electrical parameters from hydrodynamic characteristics of partially saturated soils[J]. Vadose Zone Journal,2012,11(1):272 − 286. Google Scholar
[29]	REVIL A. Transport of water and ions in partially water-saturated porous media. Part 1. Constitutive equations[J]. Advances in Water Resources,2017,103:119 − 138. doi: 10.1016/j.advwatres.2016.02.006 CrossRef Google Scholar
[30]	李华,王东辉,张伟,等. 地球物理探测技术在成都市浅表地质结构调查中的应用研究[J]. 中国地质,2022,49(5):1438 − 1457. [LI Hua,WANG Donghui,ZHANG Wei,et al. Application research of geophysical exploration technology in the investigation of shallow geological structure in Chengdu[J]. Geology in China,2022,49(5):1438 − 1457. (in Chinese with English abstract)] Google Scholar LI Hua, WANG Donghui, ZHANG Wei, et al. Application research of geophysical exploration technology in the investigation of shallow geological structure in Chengdu[J]. Geology in China, 2022, 49（5）: 1438 − 1457. （in Chinese with English abstract） Google Scholar
[31]	赵全升,孔智涵,胡舒娅,等. 柴达木盆地马海盐湖地下卤水地球物理探测及应用[J]. 吉林大学学报(地球科学版),2023,53(5):1560 − 1572. [Zhao Quansheng,Kong Zhihan,Hu Shuya, et al. Geophysical exploration and application of underground brine of Mahai Salt Lake in Qaidam Basin[J]. Journal of Jilin University (Earth Science Edition),2023,53(5):1560 − 1572. (in Chinese with English abstract)] Google Scholar Zhao Quansheng, Kong Zhihan, Hu Shuya, et al. Geophysical exploration and application of underground brine of Mahai Salt Lake in Qaidam Basin[J]. Journal of Jilin University (Earth Science Edition), 2023, 53（5）: 1560 − 1572. （in Chinese with English abstract） Google Scholar
[32]	丁万奇,马振乾,祖自银,等. 基于分形维数的巷道围岩裂隙演化规律研究[J]. 煤田地质与勘探,2021,49(3):167 − 174. [DING Wanqi,MA Zhenqian,ZU Ziyin,et al. Research on the evolution law of roadway surrounding rock fissure based on fractal dimension[J]. Coal Geology & Exploration,2021,49(3):167 − 174. (in Chinese with English abstract)] Google Scholar DING Wanqi, MA Zhenqian, ZU Ziyin, et al. Research on the evolution law of roadway surrounding rock fissure based on fractal dimension[J]. Coal Geology & Exploration, 2021, 49（3）: 167 − 174. （in Chinese with English abstract） Google Scholar
[33]	WANG Libing. Modeling complex reservoir geometries with multiple-point statistics[J]. Mathematical Geology,1996,28(7):895 − 907. doi: 10.1007/BF02066007 CrossRef Google Scholar
[34]	ZHANG Dailu,ZHANG Hongbing,REN Quan,et al. A modified method of multiple point geostatistics for spatial simulation of sedimentary facies for carbonate reservoirs[J]. Journal of Applied Geophysics,2023,215:105112. doi: 10.1016/j.jappgeo.2023.105112 CrossRef Google Scholar
[35]	王鸣川,商晓飞,段太忠. 多点地质统计学建模中训练图像建立方法综述[J]. 高校地质学报,2022,28(1):96 − 103. [WANG Mingchuan,SHANG Xiaofei,DUAN Taizhong. A review of the establishment methods of training image in multiple-point statistics modeling[J]. Geological Journal of China Universities,2022,28(1):96 − 103. (in Chinese with English abstract)] Google Scholar WANG Mingchuan, SHANG Xiaofei, DUAN Taizhong. A review of the establishment methods of training image in multiple-point statistics modeling[J]. Geological Journal of China Universities, 2022, 28（1）: 96 − 103. （in Chinese with English abstract） Google Scholar
[36]	NAN Tongchao,WU Jichun. Groundwater parameter estimation using the ensemble Kalman filter with localization[J]. Hydrogeology Journal,2011,19(3):547 − 561. doi: 10.1007/s10040-010-0679-9 CrossRef Google Scholar
[37]	LEE J,KITANIDIS P K. Large-scale hydraulic tomography and joint inversion of head and tracer data using the Principal Component Geostatistical Approach (PCGA)[J]. Water Resources Research,2014,50(7):5410 − 5427. doi: 10.1002/2014WR015483 CrossRef Google Scholar
[38]	兰天. 基于改进PCM方法反演非高斯水文地质参数[D]. 南京:南京大学,2020. [LAN Tian. Inversion of Non-Gaussian Hydrogeological Parameters by Modified Probability Conditioning Method[D]. Nanjing:Nanjing University,2020. (in Chinese with English abstract)] Google Scholar LAN Tian. Inversion of Non-Gaussian Hydrogeological Parameters by Modified Probability Conditioning Method[D]. Nanjing: Nanjing University, 2020. （in Chinese with English abstract） Google Scholar
[39]	HUANG Xiang,ANDREWS C B,LIU Jie,et al. Assimilation of temperature and hydraulic gradients for quantifying the spatial variability of streambed hydraulics[J]. Water Resources Research,2016,52(8):6419 − 6439. doi: 10.1002/2015WR018408 CrossRef Google Scholar
[40]	ZHAN Chuanjun,DAI Zhenxue,SOLTANIAN M R,et al. Data-worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework[J]. Water Resources Research,2022,58(11):e2022WR033241. doi: 10.1029/2022WR033241 CrossRef Google Scholar