The prediction of gas emissions arising from underground coal mining has been the subject of extensive research for several decades, however calculation techniques remain empirically based and are hence limited to the origin of calculation in both application and resolution. Quantification and management of risk associated with sudden gas release during mining(outbursts) and accumulation of noxious or combustible gases within the mining environment is reliant on such predictions, and unexplained variation correctly requires conservative management practices in response to risk. Over 2500 gas core samples from two southern Sydney basin mines producing metallurgical coal from the Bulli seam have been analysed in various geospatial context including relationships to hydrological features and geological structures. The results suggest variability and limitations associated with the present traditional approaches to gas emission prediction and design of gas management practices may be addressed using predictions derived from improved spatial datasets, and analysis techniques incorporating fundamental physical and energy related principles. The prediction of gas emissions arising from underground coal mining has been the subject of extensive research for several decades, however, calculating techniques remain empirically based and and hence making limited to the origin of calculation in both application and resolution. Quantification and management of risk with sudden gas release during mining (outbursts) and accumulation of noxious or combustible gases within the mining environment is reliant on such predictions, and unexplained variation precisely conservative management practices in response to risk. Over 2500 gas core samples from two southern Sydney basin mines producing metallurgical coal from the Bulli seam have been analyzed in various geospatial contexts including relationships to hydrological features and geological structures. The results suggest variability and limitations associated with the present traditional approaches to gas emission prediction and design of gas management practices may be addre ssed using predictions derived from improved spatial datasets, and analysis techniques incorporating fundamental physical and energy related principles.