Coal Seam Gas reservoirs are naturally fractured and the flow of fluid throughout the coal occurs by diffusion through the coal matrix and then via the cleats (network of fractures) towards the wellbore. Effective permeability of the cleats and Langmuir isotherm parameters of the matrix coal blocks are the key parameters in determining the economic viability of producing from a CSG reservoir. Welltest analysis in CSG wells is more complicated than conventional reservoirs due to the stress sensitive permeability of the cleats and the great heterogeneity of coal. Fluid pressure inside the cleats initially controls the stress sensitive permeability. During production and at the reduced pressure near the wellbore region, the cleats have smaller aperture and less permeability than the cleats away from the wellbore due to the in-situ stresses. Conversely during injection, the cleats near the wellbore have higher permeability than the cleats away from the wellbore. The common well test analysis methods normally provide undefined values of average permeability and skin factor, rather than addressing the effective permeability of the cleats. This study presents how to interpret the skin factor in a CSG welltest, and also proposes a methodology using integration of welltest analysis and image log data processing results for estimation of cleat characteristics in CSG reservoirs to be input in a dual porosity CSG reservoir simulation model.

The recent shale gas developments in the US have encouraged exploration for shale gas resource in WA. In the largely unexplored Carnarvon Basin, the Merlinleigh Sub-basin is predominately of Permian strata and has been shown to contain high-quality gas-prone source rocks from geochemical data. Three main potential shale layers, the Gneudna Formation, Wooramel Group and the Byro Group, were identified based on the shale ranking parameters. Geochemical data was collected and analysed for the type of kerogen, total organic content (TOC), generation potential and thermal maturity. These parameters enabled a gas-in-place resource estimation to be made for each of the formations. The TOC data from various wells were validated by using petrophysical logs and the ΔlogR method. In comparison with the geochemical data, both values produced a good match, validating both sets of data. The three layers were ranked according to their geochemical parameters and any petrophysical or geomechanical characteristics. It was identified that the Wooramel Group contains the best quality source rocks, followed by the Byro Group. The Gneudna Formation was found to have poor quality source rocks. The Monte Carlo method by Crystal Ball was selected to estimate the probabilistic resources of these three layers. According to the P50 estimations, the Byro Group, Wooramel Group and the Gneudna Formation contained resources of 51.6 tcf, 40.1 tcf and 1.4 tcf, respectively.

The recent developments of shale gas in the US have greatly encouraged exploration for the shale gas resource in Western Australia. The Merlinleigh Sub-basin of the Southern Carnarvon Basin in Western Australia has been found to contain high quality gas-prone source rocks from existing geochemical data. This paper demonstrates how log data can be used in the absence of geochemical data to increase the accuracy of resource assessments. An assessment of the Merlinleigh Sub-basin was used to calibrate and test the reliability and accuracy of the log method. A major parameter for resource estimation is the thickness of a source rock layer. This is usually determined by geochemical analysis and identifying high TOC intervals from cores. Undeveloped shale gas basins however, are limited in geochemical data whereas most of the drilled wells have wireline log data. Passey's (et al. 1990) ΔlogR method encompasses a multitude of algorithms that predicts TOC from well logs and precisely determines the thickness of a layer. The method is first calibrated with known TOC from cores before applying to wells without geochemical data. Prior studies identified three formation layers in the Merlinleigh Sub-basin; Byro Group, Wooramel Group and Gneudna Formation; potentially containing high quality source rocks. After applying Passey's delta log R method to determine thicknesses, each layer was ranked according to rock characterisation factors such as TOC, generation potential, vitrinite reflectance, porosity and permeability. The Monte Carlo method by Crystal Ball software was then selected to estimate the probabilistic resources of all layers. The production ability of each formation can also be estimated using standard open-hole log data for estimating the Ultimate Recovery in shale gas resources. In comparison, the TOC calculated from Passey's method produced results consistent with the known geochemical data, thereby validating both sets of data. The Wooramel Group was identified to contain the best quality source rocks, followed by the Byro Group, and lastly, the Gneudna Formation. According to P50 estimations from Crystal Ball, the Byro Group, Wooramel Group and Gneudna Formation contained resources of 51.6tcf, 40.7tcf and 1.4tcf respectively. The Ultimate Recovery for the above was found to be 4.69tcf, 3.71tcf and 0.13tcf respectively. Due to the low estimations for the Gneudna Formation, this paper will focus on the Byro and Wooramel Groups. This paper explores a unique way of using Passey's method for undeveloped resources having limited log data but no geochemical data. The log data was also used to estimate the Ultimate Recovery based on the production ability of each well. The exploration potential of the Carnarvon Basin in Western Australia for unconventional gas has been identified and is the first geochemical assessment for the Merlinleigh Sub-basin.

The recent shale gas developments in the US have encouraged exploration for shale gas resource in WA. In the largely unexplored Carnarvon Basin, the Merlinleigh Sub-basin is predominately of Permian strata and has been shown to contain high-quality gas-prone source rocks from geochemical data. Three main potential shale layers, the Gneudna Formation, Wooramel Group and the Byro Group, were identified based on the shale ranking parameters. Geochemical data was collected and analysed for the type of kerogen, total organic content (TOC), generation potential and thermal maturity. These parameters enabled a gas-in-place resource estimation to be made for each of the formations. The TOC data from various wells were validated by using petrophysical logs and the ΔlogR method. In comparison with the geochemical data, both values produced a good match, validating both sets of data. The three layers were ranked according to their geochemical parameters and any petrophysical or geomechanical characteristics. It was identified that the Wooramel Group contains the best quality source rocks, followed by the Byro Group. The Gneudna Formation was found to have poor quality source rocks. The Monte Carlo method by Crystal Ball was selected to estimate the probabilistic resources of these three layers. According to the P50 estimations, the Byro Group, Wooramel Group and the Gneudna Formation contained resources of 51.6 tcf, 40.1 tcf and 1.4 tcf, respectively.