Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site-specific ones (e. g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site-specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site-specific large-scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometerscale- fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.U.S. Environmental Security Technology Certification Program (ESTCP) [ER-201212]; NSF EAR [1014594]; National Natural Science Foundation of China [51609173]; Key Laboratory for Groundwater and Ecology in Arid and Semi-Arid Areas, CGS [KLGEAS201601]; Outstanding Overseas Professorship award through Jilin University by the Department of Education, China; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City; Natural Sciences & Engineering Research Council of Canada; [MOST 103-2221-E-224-054]; [MOST 104-2221-E-224-039]; [MOST 105-2625-M-224-002]6 month embargo; First published: 8 April 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site-specific ones (e. g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site-specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site-specific large-scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometerscale- fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.U.S. Environmental Security Technology Certification Program (ESTCP) [ER-201212]; NSF EAR [1014594]; National Natural Science Foundation of China [51609173]; Key Laboratory for Groundwater and Ecology in Arid and Semi-Arid Areas, CGS [KLGEAS201601]; Outstanding Overseas Professorship award through Jilin University by the Department of Education, China; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City; Natural Sciences & Engineering Research Council of Canada; [MOST 103-2221-E-224-054]; [MOST 104-2221-E-224-039]; [MOST 105-2625-M-224-002]6 month embargo; First published: 8 April 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]