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Coalsim - Predict gas production from coal seam gas reservoirs

Invention Number: 
An invention that predicts gas production from coal seam gas reservoirs with greater accuracy to improve gas yield calculations. Coal seam gas accounts for 27% of Australian gas reserves; is set to supply at least 30% of Australia's domestic market by 2030, and 50% of gas demand in eastern Australia.

The Technology is available under licence for free

UNSW researchers have developed a new method to accurately calculate the petrophysical properties of coal. In particular, the team is able to predict gas production from coal seam gas reservoirs with significantly greater accuracy improving gas yield calculations. The method is also capable of optimising degassing in coal mining to improve safety.  

This new method uses CT images of coal cores to visualise the coal cleat system not visible with other imaging methods.

The software is then able to use these images to reconstruct the coal cleat system as a whole for use in larger scale simulation of the mining operation and gas production.  

The software has been tested in the lab and is ready for development into a commercial demonstration system. UNSW is seeking a partner to help further develop and commercialise the technology.

Key Benefits

  • Can accurately reconstruct the properties of a coal body from limited sampled data
  • Lab tested and ready for commercial development
  • Provides more accurate yield simulation results than current methods
  • Solves key problems in simulating coal seam gas reservoirs and degassing coal mines

Potential Applications

  • Coal mine safety - more accurate gas prediction
  • Improved coal seam gas yield prediction
  • Computer simulation of coal deposits

Scientific Data


  • A method for identifying face and butt cleat families based on CT images is developed
  • An advanced image analysis method is presented to extract statistics of fracture structural parameters.
  • Discrete fracture network models are constructed that are representative of bright coal.
  • Permeability is calculated directly on voxelised DFN models to evaluate coal heterogeneity and anisotropy
  • The developed framework resolves CT imaging resolution limitation and segmentation errors


Coal seam gas (CSG) is gaining global interests due to its natural abundance and environmental benefits in comparison to more traditional energy sources. However, due to its significant heterogeneity and complex porous structure, it is challenging to characterise and thus predict petrophysical properties. Moreover, the fracture network of coal poses a major challenge for direct numerical simulations on segmented images collected from X-ray micro-computed tomography (μCT). The segmentation of coal images is problematic and often results in misclassification of coal features that subsequently causes numerical instabilities. This paper aims to develop an advanced image analysis method and a novel discrete fracture network model to circumvent these issues. Coal μCT data are utilised for the acquisition of structural parameters and then discrete fracture networks are built to reconstruct representative coal images. The modelling method mimics the cleat formation process and reproduces particular cleat network patterns. The reconstructed network preserves the key attributes of coal, i.e. connectivity and cleat structure, while not being limited in terms of size and/or resolution. Furthermore, direct numerical simulations based on lattice Boltzmann method are performed on the cleat network realisations to evaluate coal permeability. We find that directional permeabilities result in different system scaling effects because of the dependence on the underlying structure of the cleat network. The developed method facilitates the evaluation of the relationship between coal cleat structure and resulting flow properties, which are steps forward in the evaluation of coal petrophysical properties at the core scale.

For more technical details, please refer to the research team's recent publication here >> 

The Opportunity

UNSW is seeking a partner to license this technology or to work with the researchers to further develop this technology.