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Toward Retrieving Distributed Aquifer Hydraulic Parameters From Distributed Strain Sensing
Author(s) -
Zhang Yi,
Lei Xinglin,
Hashimoto Tsutomu,
Xue Ziqiu
Publication year - 2021
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2020jb020056
Subject(s) - aquifer , geology , permeability (electromagnetism) , compressibility , poromechanics , aquifer test , soil science , geotechnical engineering , slug test , hydraulic conductivity , test data , petroleum engineering , mechanics , groundwater , porous medium , soil water , computer science , porosity , physics , groundwater recharge , membrane , biology , genetics , programming language
Subtle elastic rock deformation during aquifer testing may bear hydraulic parameter (permeability and compressibility) information owing to the poroelastic hydromechanical coupling effect. Here we report that such in situ rock deformations (∼50 µε) during an aquifer pumping test are successfully measured along a vertical well by a high‐resolution fiber optic distributed strain sensing (DSS) tool with an accuracy of 0.5 µε. We investigate the feasibility of hydraulic parameter estimation at meter scale using DSS data through a coupled hydromechanical model. Both synthetic and field cases are tested with sensitivity analysis. The results indicate that the simultaneous estimation of permeability and compressibility using DSS data is possible at low noise levels. However, only non‐global near‐optimal solutions can be obtained using the applied gradient‐based inversion algorithm, because of parameter crosstalk and sensitivity problems when the data contain large noise. In particular, estimation is difficult for zones with relatively low permeability due to the low sensitivity to the strain changes. The estimated permeability/compressibility structures for the field test are largely consistent with other geological information from well logs. Our study suggests that DSS data can be quite useful in aquifer characterization and fluid flow profiling in addition to geomechanical monitoring. The obtained hydraulic information is beneficial for the optimized reservoir management of water and oil/gas storage.