Data-Driven Identification of Coal Reservoir Families

Dr Tauqir Moughal1, Dr  Irina Emelyanova1, Mr David S. Warner2, Dr Erik C. Dunlop2, Dr Mohinudeen Faiz3Ms Prue E.R. Warner2, Dr Marina Pervukhina1

1CSIRO Energy, 26 Dick Perry Avenue, Kensington, Australia, 2Deep Coal Technologies Pty Ltd, Torrens Park, SA, Australia, 3CSIRO Energy, 1 Technology Court, Pullanvale, QLD, Australia


Identification of coal reservoir families is important for the prediction of gas in place and potential sweet spots. This task is challenging especially for ultra-deep coal seams. In this study, we propose an approach for identifying coal reservoir families which is fully data-driven and based on hierarchical clustering of target coals.

Study Area and Data
The Cooper Basin is selected as a study area which is a world-class unconventional gas resource located in the South Australia and Queensland. A large amount of information about the physical parameters of the deep coal seams from 1194 wells is available. We proceeded with ten relevant target coal characterisation parameters (DCCPs).

In order to identify the coal gas reservoir families, we develop a decision tree (DT) for partitioning the original dataset into clusters that can be interpreted as reservoir families. At each node of the DT, the quality of the cluster is evaluated and clustering is conducted.  The partitioning process stops when the cluster quality is acceptable. The cluster quality is assessed statistically through a cluster validity criterion.

Ten meaningful clusters are generated to identify coal reservoir families in the Cooper Basin. Furthermore, these clusters are analysed and interpreted by a geologist.

A hierarchical clustering approach has been applied to identify coal reservoir families in the Cooper Basin. The method can assist geologists in detection and mapping of potential sweet spots. This application demonstrates how traditional practices of reservoir modelling can be improved using machine learning.


Tauqir Moughal is a data scientist with significant experience in machine learning. He is a member of CSIRO Geoscience Data Analytics team, a team of computational scientists who develop and implement data-driven techniques in various geoscience applications.


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