Application of 1D paleo‐fluvial process modelling at a basin scale to augment sparse borehole data: example of a Permian formation in the Galilee Basin,Australia |
| |
| Authors: | Zhenjiao Jiang Gregoire Mariethoz Matthias Raiber Wendy Timms Malcolm Cox |
| |
| Institution: | 1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of Environment and Resources, Jilin University, Changchun, China;2. School of Earth, Environmental and Biological Sciences, Queensland University of Technology, Brisbane, Queensland, Australia;3. Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland;4. School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Walesz, Australia;5. CSIRO Land and Water, Brisbane, Queensland, Australia;6. School of Mining Engineering, University of New South Wales, Sydney, New South Wales, Australia |
| |
| Abstract: | The heterogeneous hydraulic conductivity (K) in water‐bearing formations controls subsurface flow and solute transport processes. Geostatistical techniques are often employed to characterize the K distribution in space based on the correlation between K measurements. However, at the basin scale, there are often insufficient measurements for inferring the spatial correlation. This is a widespread problem that we address in this study using the example of the Betts Creek Beds (BCB) in the Galilee Basin, Australia. To address the lack of data, we use a 1D stochastic fluvial process‐based model (SFPM) to quantify the total sediment thickness, Z( x ), and the sandstone proportion over the total thickness, Ps( x ), in the BCB. The semivariograms of Z( x ) and Ps( x ) are then extracted and used in sequential Gaussian simulation to construct the 2D spatial distribution of Z( x ) and Ps( x ). Ps( x ) can be converted to a K distribution based on classical averaging methods. The results demonstrate that the combination of SFPM and geostatistical simulation allows for the evaluation of upscaled K distribution with a limited number of K measurements. Copyright © 2015 John Wiley & Sons, Ltd. |
| |
| Keywords: | heterogeneity fluvial process‐based model sequential Gaussian simulation semivariogram |
|
|
