oa Identification of deep coal seam families using machine learning

The Cooper Basin of Australia is a world-class unconventional gas resource with estimated gas resources of 29.8 trillion cubic feet. However, the production of this gas is challenging as the significant gas resources are located in deep coal seams, which are poorly cleated and characterised by extremely low matrix permeability. Feasibility of gas production from the Cooper Basin Deep Coal Gas (CBDCG) play was demonstrated by Santos; however, its commercial viability is yet to be proven.

Recent studies provided a new insight into the gas generation ability of Cooper Basin coal seams and showed that multiple environmental features affect gas concentration and flow capacity. Fortunately, a large historical dataset exists and includes wireline and mud log data from wells drilled in the Cooper Basin. Up to 10,000 individual coal seams were identified in 1400 wells and various parameters of individual reservoir intersections, which include gas in place, thermal maturity, temperature and other petrophysical readings, completed the Cooper Basin Deep Coal Reservoir (CBDCR) database. Such a database is suitable for assessing the potential of the ultradeep Permian coal gas reservoirs of the Cooper Basin using machine learning.

In this study, we explore the data using traditional statistical methods and propose a hierarchical clustering procedure to identify various coal seam families. The quality of the identified coal seams families (clusters) is then examined by domain experts. The gas in place, geomechanical parameters, pore pressure and other important for successful production parameters can be further assessed for all confirmed clusters.