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Inaccuracies in spatial modelling of coal properties can impact coal resource and reserve estimates. Geostatistical methods which account for compositional data have previously been recommended as a preferred method for modelling of coal properties. These methods can quantify uncertainty, can use auxiliary information to improve estimates, and ensure that compositional data (such as coal proximate analysis) is modelled in a mathematically consistent way. However, geostatistical methods have drawbacks in that have rigid statistical assumptions about the distribution and stationarity of the target variable and require variogram interpretation which can be onerous when then number of domains and target variables are numerous. Due to these drawback, inverse distance weighting (a simple deterministic method) remains the most popular method for modelling coal properties in Australian resource estimates. To address the drawbacks of geostatistical methods, a machine learning approach based on the quantile regression forest algorithm is proposed as an alternative method to spatially model coal properties. This newly proposed method, accounts for spatial arrangement of data, requires minimal pre-processing steps, can quantify uncertainty and can easily incorporate auxiliary information of various types. To evaluate the performance of this method, the accuracy of predictions of coal proximate analysis properties, and coal density are compared to the predictions of inverse distance weighting and regression kriging. All methods incorporate isometric log ratio transform and back-transform of data in order to account for the compositional nature of coal proximate analysis. Data from an active coal mine in the Bowen Basin, Queensland Australia was used as basis of the results. A unique feature of the mine site in this basin is the presence of intrusion which impacts coal quality properties to various degrees. Using evaluation metrics from leave-one-out cross-validation, this paper demonstrates that quantile regression forest has higher accuracy, lower bias and higher precision than inverses distance weighting across all coal properties. The paper also shows that results of the new method are very similar or better than regression kriging. It is also demonstrated that the prediction maps produced by quantile regression forest could be used to more accurately map out coal impacted by intrusion compared to inverse distance weighting, and that inverse distance weighting overestimates the impact of intrusion and intrusion extent. A drawback of the method compared to inverse distance weighting, is that it is more computationally demanding, less intuitive and is currently not available in existing geological packages. [ABSTRACT FROM AUTHOR]

Abstract: Late Permian age samples of black coal from the coeval Bandanna Formation and Baralaba Coal Measures of Queensland''s Bowen Basin, as well as Carboniferous coal from Germany''s Ruhr Basin, were investigated. This paper presents scanning electron microscope (SEM) analysis of the mode of occurrence of minerals within these coals, and characterisation of the natural fractures present within the samples. The coal samples located proximal to orogenic fronts in both basins are structurally complex, with multiple generations of mineralisation evident. Stable isotope analyses of carbonates from selected samples indicate that methanogenesis-related CO2 reduction has occurred at two of the Bowen Basin sampling locations. Ankerite precipitated from a magmatic fluid or basinal brine is associated with significant sulphide mineralisation within brecciated Ruhr coal proximal to major fault intersections with anticlines, and has been meteorically altered to ferroan calcite and sulphates in coal from immediately below the Carboniferous/Cretaceous unconformity. Similar alteration has occurred within a Bowen Basin coal sample close to the present day land surface. Variation of mineralisation within both the cleats and the coal matrix occurs on a fine scale, with crystals of multiple mineral species often being present within micrometres of each other. [Copyright &y& Elsevier]

This paper introduces the development of a new predictive model in support of proppant injection in naturally fractured coal seam gas (CSG) reservoirs. In the proposed model, the finite element method (FEM) is used for the prediction of proppant embedment and elastoplastic deformation of the coal. The lattice Boltzmann method (LBM) is applied to the modelling of fluid flow through propped fractures, in which the modified partially saturated method (MPSM) is implemented to characterise the fluid–solid interactions. Permeability diagrams of the fractures are then generated as functions of particle packing ratio and effective stress. Finally, these results are incorporated into a radial Darcy flow analytical solution to predict the productivity index of the CSG wells under various proppant injection pressures and cleat compressibilities. The developed model is applied to selected coal samples from the Bowen and Surat Basins in Queensland, Australia. Modelling results indicate that proppant injection leads to increased fracture permeabilities and enhanced well productivity indexes. The elastoplastic deformation results in smaller permeability increase and less production enhancement when compared to the traditional linear elastic models. Although focused on Australian coals, the developed workflow can be broadly applicable to the assessment of potential stimulation efficacy in other unconventional reservoirs. In addition, a better understanding and implementation of the proppant injection scheme can effectively improve the post-fracturing performance, particularly in low-permeability coal intervals, which benefits the CSG industry. • Elastoplasticity is incorporated into computational models of proppant embedment. • The deformation of idealised coal cleats is assessed for residual permeability. • Optimal monolayer packing is determined for selected Bowen and Surat Basin coals. • Resulting fracture permeaibilities used in well productivity sensitivity analyses. [ABSTRACT FROM AUTHOR]