Blog icon

The challenge

Characterising and modelling aquifers and aquitards

Understanding the spatial characteristics and relationships of aquifers and aquitards, including their geometry, internal architecture and hydraulic properties, is key to any groundwater resource assessment.

Large datasets of lithology (driller's logs), stratigraphy (stratigraphic logs) and hydraulic data (e.g. water level or pumping test data) are readily available in State and Territory government databases. However, their inclusion in groundwater resource assessments and modelling is often not straightforward due to variable data quality and the lack of tools to facilitate data interpretation.

Furthermore, traditional methods of estimating subsurface properties, such as constant rate pump testing, can be restrictive due to high costs. Other existing methods, such as core permeability testing, can also be hampered by their limited spatial scale of observation.

Our response

New methods and tools

We apply complementary methods that allow us to make best use of existing or newly collected data and overcome limitations commonly associated with these large datasets. This involves development of:

3-D geological model and groundwater chemistry of the Lockyer Valley in southeast Queensland.

  • 3D geological models
  • 3D lithofacies models
  • methods of estimating scale-appropriate hydraulic and poroelastic properties from groundwater pressure responses to actively and passively generated periodic signals.

3D geological models

We develop sophisticated 3D geological models from local (e.g. catchment or sub-catchment) to regional scales (sedimentary basins) using 3D geological model software packages that integrate and honour all available geoscientific data (e.g. lithological, stratigraphic and geophysical data).

These models often represent both shallow alluvial aquifers and deep sedimentary basin aquifers and help to understand how these components interact. We also often integrate them with additional data sources such as groundwater hydraulics and hydrochemistry.

We have developed 3D geological models for many parts of north-eastern NSW (e.g. the Richmond River catchment), Southeast Queensland, South Australia, Norfolk Island and Chile.

3D lithofacies models

We apply machine learning techniques to streamline the process of extracting useful information, such as lithology and hydraulic conductivity, from the descriptive driller's logs and geophysical data to support groundwater system characterisation. The developed workflow combines the processing of lithology descriptions and supervised machine learning to interpolate and delineate lithology classes in 3D space.

3-D lithofacies model of the coastal plain in the Peel region (Western Australia). It shows the distribution of different lithology classes (e.g. sand or clay) derived from driller’s logs using machine learning techniques.

This reduces the effort for manual interpretation and classification of traditional and emerging data for hydrogeological system characterisation. The novel techniques can facilitate the development of geological models and groundwater models. We have applied this tool in the Peel region in Western Australia and are currently exploring its use in other regions.

Estimating scale-appropriate hydraulic and poroelastic properties

We are developing methods of estimating scale-appropriate hydraulic and poroelastic properties from groundwater pressure responses to actively and passively generated periodic signals.

Active signal generation involves the use of pumps or slugs to induce periodic responses. Passive signal analysis involves interpreting groundwater pressure responses to barometric pressure fluctuations (caused by the movement of weather systems) and Earth or ocean tides (both caused by the movement of the Sun and Moon).

These active and passive methods of periodic testing provide cost-effective (and in some cases, non-invasive) alternatives to traditional hydraulic testing. These analyses also add considerable value to routine measurements of groundwater pressures for hydrograph analysis.

Analyses of groundwater pressure responses to passive signals have been used to estimate hydraulic properties in the Mary-Wildman rivers area of the Northern Territory, the Peel region of Western Australia, and on Norfolk Island in the South Pacific Ocean.

We are currently working in partnership with researchers at Flinders University, South Australia, the Karlsruhe Institute of Technology, Germany, and the University of Georgia, USA to develop publicly available implementations of active and passive periodic signal testing methods and analyses.

The results

Applying the science

The application and iterative integration of geological modelling, lithofacies modelling and estimation of hydraulic properties of aquifers and aquitards, supported by additional lines of geoscientific evidence (e.g. geophysics and hydrochemistry), leads to an improved conceptualisation of groundwater systems.

This improved conceptual understand of groundwater systems and their interactions with surface water systems provides a more robust model structure and framework that can be directly applied in regional water resource assessments in support of groundwater management (e.g. Peel Integrated Water Initiative and Norfolk Island).

Do business with us to help your organisation thrive

We partner with small and large companies, government and industry in Australia and around the world.

Contact us now to start doing business

Contact us

Find out how we can help you and your business. Get in touch using the form below and our experts will get in contact soon!

CSIRO will handle your personal information in accordance with the Privacy Act 1988 (Cth) and our Privacy Policy.

First name must be filled in

Surname must be filled in

I am representing *

Please choose an option

Please provide a subject for the enquriy

0 / 100

We'll need to know what you want to contact us about so we can give you an answer

0 / 1900

You shouldn't be able to see this field. Please try again and leave the field blank.