1. Texture2Par A Parameterization Utility for IWFM and MODFLOW
Vivek Bedekar
Leland Scantlebury
Marinko Karanovic
Matt Tonkin
CWEMF
Folsom, CA
April 23, 2019
Water, Water, Everywhere
Samuel Coleridge, The Rime of the Ancient Mariner

2. Background Texture2Par is based on Bond and Durbin (2018)
MakePar was the starting point for Texture2Par

3. What is Texture2Par? Texture2Par
Texture2Par is a pre-processing utility program
Uses underlying sediment texture data acquired from stratigraphic logs
Computes aquifer parameter values based on texture data
Creates model parameter input file for IWFM and MODFLOW
Numerical Model
Page, R.W., 1986, Geology of the fresh ground-water basin of the Central Valley, California, with texture maps and sections: U.S. Geological Survey Professional Paper 1401-C, 54 p.

4. Aquifer Parameter Calculation – Power Law
Power-law averaging is used to compute hydraulic conductivity, specific storage, and specific yield (Faunt, 2009)

5. Aquifer Parameter Calculation – Power Law
Power-law averaging is used to compute hydraulic conductivity, specific storage, and specific yield (Faunt, 2009)
Percent coarse
Percent fine

6. Aquifer Parameter Calculation – Power Law
Power-law averaging is used to compute hydraulic conductivity, specific storage, and specific yield (Faunt, 2009)
Percent coarse
Percent fine
Averaging power-mean exponent
Coarse-grained parameter value
Fine-grained parameter value

7. Aquifer Parameter Calculation – Power Law
Power-law averaging is used to compute hydraulic conductivity, specific storage, and specific yield (Faunt, 2009)
Bulk parameter value
Percent coarse
Percent fine
Averaging power-mean exponent
Coarse-grained parameter value
Fine-grained parameter value

8. Aquifer Parameter Calculation – Depth Dependency
Decrease in hydraulic conductivity with depth occurs due to :
Deposits that get compressed as geostatic load increases with depth
More consolidated older deposits
An exponential depth-decay function is used to account for that (Faunt, 2009):

9. Steps in the Workflow of the Texture2Par Program

10. Step 1: Read Basic Model Information
Read Texture2Par input file
Determine model – IWFM/MODFLOW
Read spatial discretization and stratigraphic information
Read pilot point information

11. Step 2: Read Well Log Texture Information
Bond and Durbin (2018)

12. Step 2: Read Well Log Texture Information

13. Step 3: Evaluate Well Log by Model Layer

14. Step 4: Krige Percent Coarse to Model Nodes
Each model cell gets populated with percent coarse information

15. Step 5: Pilot Points for Power Law Bounding Values
Pilot point provides sediment-level parameter values that appear in the Power Law equation:
Kcmin, Kcmax
Kfmin, Kfmax
SsC
SsF
SyC
SyF
Horizontal to vertical hydraulic conductivity anisotropy
Pilot points offer representation of areas with differing cementation, compaction or sorting.
Pilot points may be grouped on geologic basis, depositional history.

16. Step 5: Pilot Points for Power Law Bounding Values
Each bounding parameter value is kriged to each model node/cell
A single set of bounding parameter values may be used if spatial variability is not desired
Optionally, nodes/cells/pilot points can be grouped into zones for independent kriging
Aquitards are handled separately from aquifers with a different set of pilot points

17. Step 6: Calculate Aquifer Parameters, Write Model File
Bulk parameter values calculated for each model node/cell
Input file written for the IWFM or MODFLOW model
Option available for generating intermediate calculations

18. Example - SVSIM

19. Example - SVSIM

20. Example – SVSIM Zoomed

21. Example – SVSIM Zoomed

22. Advantages Process Texture2Par
Parameterization is tethered to local geologic information
Parsimonious pilot point setup needs relatively few calibration parameters
Texture data creates natural heterogeneity
Pilot points offer flexibility in incorporating regional heterogeneity
Texture2Par
Facilitates aquifer parameter calculations as a stand-alone pre-processor
Tool lends itself well to be seamlessly incorporated in work flow
Suitable for parameter estimation tool like PEST

23. Acknowledgements Tim Durbin, Claire Velayas (Timothy J. Durbin, Inc.)
Linda Bond, Chris Bonds (DWR)
Saquib Najmus, Mesut Cayar, Frank Qian (Woodard and Curran)

24. Thank you for your time! Questions? Vivek Bedekar vivekb@sspa.com