Oral Presentation Australasian Groundwater Conference 2017

GAB aquifer attribution process (106)

Chris Dickinson 1 , Claire Kent 1 , Michael Jamieson 2 , Sanjeev Pandey 3
  1. Klohn Crippen Berger Ltd, South Brisbane, QLD, Australia
  2. Department Natural Resources and Mines, DNRM, Brisbane
  3. Office of Groundwater Impact Assessment, DNRM, Brisbane

A methodology was required as part of the 2016 GAB Hydrogeology Study to determine water source for all bores screened in GAB aquifers across the Qld portion of the basin. 

The development of a master data suite (MDS) which provides an ‘aquifer attribute’ for all active bores in the GAB was critical to the completion of the main study. There are ~96,000 bores (RNs) registered in the DNRM Groundwater Database within the GAB WRP area, and inside a 100km buffer outside the defined limits of the GAB. Aquifer attributes are available from a number of data sources for about half of these RNs: leaving a very large number of records without assignment.

A methodology was developed to populate these missing records.

Original data and results:  A three stage approach was developed:

  • Firstly, hierarchal assessment of existing datasets with highest confidence datasets taking priority.
  • Secondly, for sites with no aquifer information, a spatial approach to assignment of aquifer was developed using a regional geological model and applying nearby water source information.
  • Thirdly, a process was developed to assess and verify results.

The process determined the ‘interval of groundwater intake’ for each bore, and considered multi-aquifer verification, and unique data sets with potential to skew the interpretation (eg, Condamine alluvium). Application of a geological model for aquifer assignment for RNs for no information available was completed, and consideration of external data sources such as QPED was applied.

The process resulted in successful assignment of an aquifer attribute for close to 100,000 bores, using a hierarchical approach with increasing reliance on data with highest confidence. Spatial representation for ‘data-light’ RNs was then applied, and data verified prior to implementation to broader study requirements.

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