Determining stimulated reservoir volume based on the microseismic continuous fracture network model
LI Qiu-Chen1(), CHEN Dong2, XU Wen-Hao1(), YI Shan-Xin2, XIE Xing-Long1, GUAN Jun-Peng2, CUI Fang-Zi1
1. Hydrogeological and Environmental Geological Survey,China Geological Survey,Baoding 071051,China 2. Nanjing Institute of Geology and Paleontology,Chinese Academy of Sciences,Nanjing 210008,China
Hydraulic fracturing is required during the exploitation and stimulation of hot dry rock (HDR) reservoirs.The stimulated reservoir volume (SRV) is the main criterion for evaluating the hydraulic fracturing performance.As a technical link of hydraulic fracturing,Microseismic monitoring can be used to effectively estimate the SRV.This study explored the calculational method of SRV based on the microseismic continuous fracture network model with the purpose of guiding the hydraulic fracturing of HDRs.First,the continuous fracture network was modeled based on the temporal and spatial distribution and multiple source parameters of microseismic events.Second,the fracture grid length was extracted and the appropriate fracture height and width were selected to calculate the SRV.Last,the method proposed in this study was applied to the dual-well HDR microseismic monitoring data.The application results show that this method can effectively estimate the SRV,thus providing a basis for the subsequent exploitation and stimulation of HDRs.
Yang R Z, Zhao Z G, Wang Z G, et al. Microseismic monitoring technology for rock and gas development[M]. Shanghai: East China University of Science and Technology Press, 2016
Hou J X, Xie F, Ren Y Q, et al. Detection of acoustic emissions associated with the stick-slips of a meter-scale fault in laboratory by using the matched filter technique[J]. Chinese Journal of Geophysics, 2020, 63(4):1630-1641.
[3]
Warpinski N R, Mayerhofer M, Agarwalk, et al. Hydraulic fracture geomechanics and microseismic source mechanisms[J]. SPE Journal, 2013, 18(4):766-780.
doi: 10.2118/158935-PA
[4]
Vlcek J, Fischer T, Vilhelm J. Back projection stacking of P and S-waves to determine location and focal mechanism of microseismic events recorded by a surface array[J]. Geophysical Prospecting, 2016, 64(6):1428-1440.
doi: 10.1111/gpr.2016.64.issue-6
[5]
Rutledge J T, Phillips W S. Hydraulic stimulation of natural fractures as revealed by induced microearthquakes,Carthage Cotton Valley gas field,east Texas hydraulic stimulation of natural fractures[J]. Geophysics, 2003, 68(2):441-452.
doi: 10.1190/1.1567214
Li D J, Yang X, Wang X L, et al. Estimating the fracturing effect and production capacity of the Longmaxi formation of the Lower Silurian in area W,Sichuan Basin[J]. Geophysical Prospecting for Petroleum, 2017, 56(5):735-745.
doi: 10.3969/j.issn.1000-1441.2017.05.014
[7]
Mayerhofer M J, Lolon E P, Warpinski N R, et al. What is stimulated reservoir volume?[J]. Spe Production & Operations, 2010, 25(1):89-98.
Chen X R, Yang K, Zhang G S. Discrete fracture network modeling technology based on shale reservoirs[J]. China Energy and Environmental Protection, 2017, 39(10):172-175.
[10]
许德友. 基于微地震DFN模型的SRV研究[D]. 北京: 中国石油大学, 2019.
[10]
Xu D Y. Research on SRV based on microseismic DFN model[D]. Beijing: China University of Petroleum, 2019.
[11]
Mayerhofer M J, Lolon E P, Youngblood J E, et al. Integration of microseismic fracture mapping results with numerical fracture network production modeling in the Barnett Shale[C]// San Antonio:Paper SPE 102103 presented at the SPE Annual Technical Conference and Exhibition, 2006.
[12]
Warpinski N R, Mayerhofer M J, Vincent M C, et al. Stimulating conventional reservoirs maximizing network growth while optimizing fracture conductivity[C]// Keystone:Paper SPE 114173 presented at the SPE Unconventional Reservoirs Conference, 2008.
[13]
Baidurja R, Avi L, Jianfu M, et al. Unconventional micro-seismicity based enhanced 3D SRV estimator using advanced parameter-free concave methodology[C]// SEG Technical Program Expand Abstracts, 2014:2304-2308.
Zhao Z G, Li L, Zhang H L. Modeling method of hydraulic fracture network based on microseismic event attributes[C]// China Earth Science Joint Academic Annual Meeting, 2020:317-319.
[15]
Dershowitz W S, Fidelibus C. Derivation of equivalent pipe network analogues for three-dimensional discrete fracture networks by the boundary element method[J]. Water Resources Research, 1999, 35(9):2685-2691.
doi: 10.1029/1999WR900118
[16]
Xu W X, Calvez J L, Thiercelin M. Characterization of hydraulically-induced fracture network using treatment and microseismic data in a tight-gas sand formation:A geomechanical approach[C]// SanAntonio:Paper SPE 125237 Presented at the SPE Tight Gas Completions Conference, 2009.
[17]
Xu W Y, Thiercelin M, Ganguly U, et al. Wiremesh:A novel shale fracturing simulator[C]// Bejjing:Paper 140514 presented at the CPS/SPE International Oil&Gas Conference and Exhibition, 2010.
[18]
Meyer B R, Bazan L W. A discrete fracture network model for hydraulically induced fractures:Theory parametric and case studies[C]// Woodlands:Paper 140514 Presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, 2011.
[19]
Meyer B R, Bazan L W, Jacot R H, et al. Optimization of multiple transverse hydraulic fractures in horizontal welbores[C]// Pittsburgh:Paper 131732 Presented at the SPE Unconventional Gas Conference, 2010.
[20]
Alexandre H, Jean C D, Gringarten E, et al. Connecting the dots:microseismic derived connectivity for estimating volumes in low-permeability reservoirs[C]// San Antonio:Unconventional Resources Technology Conference, 2015.
[21]
Seifollahi S, Dowd P A, Xu C, et al. A spatial clustering approach for Stochastic fracture network modelling[J]. Rock Mechanics and Rock Engineering, 2014, 47(4):1225-1235.
doi: 10.1007/s00603-013-0456-x
[22]
余毓敏. 基于震源机制解的DFN模型优化[D]. 北京: 中国石油大学, 2020.
[22]
Yu Y M. Optimization of DFN model based on focal mechanism[D]. Beijing: China University of Petroleum, 2020.
[23]
邵媛媛. 基于水力压裂模拟及微地震监测信息的SRV研究[D]. 成都: 西南石油大学, 2020.
[23]
Shao Y Y. SRV research based on fracking simulation and microseismic monitoring information[D]. Chengdu: Southwest Petroleum University, 2020.