Practical application of drill hole spacing analysis in coal resource estimation

Transcription

1 Practical application of drill hole spacing analysis in coal resource estimation C.M.Williams 1, K.Henderson 2 and S.Summers 2 1. HDR mining consultants, Level 23, 12 Creek Street, Brisbane, Qld, Peabody Energy Australia, 100 Melbourne Street, South Brisbane, Qld, 4101 Abstract According to the recently released Australian Guidelines for the Estimation and Classification of Coal Resources (2014), geostatistics is listed as one of the methods which can be used to assess confidence in a coal resource estimate. In this paper, several case studies are presented in which geostatistics has been successfully used to assess confidence for two operating coal mines and one exploration prospect, all of which are owned and operated by Peabody Energy Australia. The method used to apply geostatistics to the assessment of confidence and coal resource classification is known as Drilll Hole Spacing Analysis or DHSA. An easy to use approach to DHSA, specifically adapted to coal, is presented with reference to freely available software, thereby enabling this methodology to be more widely used within the industry. The results of the three DHSA studies conducted by HDR mining consultants for Peabody Energy Australia are presented and discussed. These results clearly show that the main criteria in determining classification distance using the DHSA method are; Population/Global variability. Spatial continuity, one measure of which is the variogram range. Size of the study area, which in turn is a function of working section thickness and mining rate. It is shown that the DHSA method represents a relatively simple way of using geostatistics to assess confidence in coal resource estimates. The case studies presented show that DHSA can be applied to both mature mining projects as well as early stage exploration projects. It should be noted however that the approach to DHSA in the case of exploration projects needs to be more conservative, recognising that data gaps potentially exist. This is illustrated in an example from the West Burton study. 1

2 Introduction One of the main aims of this paper is to present an easy to use method for the use of geostatistics in assessing confidence in coal resource estimates. The recently updated (2014) Australian Guidelines for the Estimation and Classification of Coal Resources (Coal Guidelines) requires that coal resource classification be based on an assessment of the confidence in the underlying resource estimate. A number of criteria are set out in the Coal Guidelines that can be used in varying combinations to assess confidence, namely; Critical assessment of relevant local, geographical and geological settings Identifying critical data Data analysis QAQC Domaining Statistical analysis Geostatistical analysis Geological modelling Many of the criteria listed above are fairly subjective and the only method able to give a truly quantitative estimate of confidence is considered to be geostatistics. This should however not negate the importance of the other factors; Exploratory Data Analysis (EDA) prior to conducting an estimate in order to identify spurious data or to separate out mixed domains is a critical step. Global or population statistics in the form of histograms, minimum, maximum and mean values, standard deviation, coefficient of variation etc. is important for identifying spurious data and mixed domains and it also serves to quantity global variability in variables considered. The lack of suitable QAQC implemented during logging, sampling and analysis will result in a low level of confidence in the resulting estimate, regardless of any subsequent geostatistical analysis. However it is considered that only geostatistical analysis can give a truly quantitative measure of confidence and hence it is considered to be the least subjective method. Not withstanding this, geostatistical methods are not without problems when applied to coal. This paper presents a number of case studies where the DHSA method has been successfully used as a basis for coal resource classification. Examples have been drawn from these case studies which illustrate potential solutions to common problems encountered in the application of geostatistics to coal. It should be noted at the outset that the use of geostatistics to calculate estimation variance as a basis for determining confidence in an estimate does not necessarily imply that geostatistics has to be used as an interpolator in the estimate. It has been shown that commonly used interpolators for coal resource estimates, such as inverse distance squared (IDW2), are quite good at getting close to the level of precision achieved using Kriging in most cases (Williams 2

3 et al 2010). Hence it is considered that estimation variances calculated through the method presented in this paper can be used for estimates which employ other interpolators as long as the attributes being estimated are not highly variable and that the drilling grids used are fairly regular (irregular drilling patterns and highly variable attributes being estimated both result in a significant increase in the interpolation performance of Kriging over IDW). A thorough understanding of the coal geology and the accurate modelling thereof is also of vital importance to any estimate. It is for this reason that this paper is being written as it presents an easy to use method for the application of geostatistics to the assessment of confidence in coal resource estimates. This allows the geologist, who is likely to be most familiar with the coal geology, to do the geostatistics, rather than relying on specialist geostatisticians who may not be as familiar with the geological characteristics of the deposit. Theory behind DHSA The method that this paper uses for determining estimation variance for use in the assessment of resource estimation confidence is known as Drill Hole Spacing Analysis or DHSA. This method involves the calculation of the global estimation variance, discussed in detail by Journel and Huijbregts (1978), pp Figure 1 Calculation of estimation variance into a regular square region V, of increasing size V2 and V3 Estimation Variance The estimation variance or extension variance is defined as the variance associated with using the known average grade for a small volume v, to estimate the grade for a much larger region V. The first step in the DHSA process is to calculate the estimation variance for a given block size. The equation for the calculation of the estimation variance, Ơ 2 E, is shown below;,=2,,,

4 The equation is based on the average variagram value, where the sample pairs for the calculation of the semi-variogram value, Ƴ, are between the central sample and a series of random positions within the larger region V for the first term (v,v), between random positions within the smaller volume v for the second term (v,v) and between random positions within the larger region V for the third term (V,V). Charts which provide the solution to the above equation for spherical and exponential isotropic (omnidirectional) variogram models are provided by Journel and Huijbregts (1978), pp The chart for the spherical variogram model, estimating a central sample into a square region (pp 131) shows that the estimation variance increases linearly as you increase the block side length from V through V2, V3 etc., until it reaches about 80% of the sill semivariogram value, at a block side length equal to twice the variogram range. After this the estimation variance increases much more slowly as the block side length increases, reaching the sill semi-variogram value at a block side length equal to 10 times the variogram range. When conducting a DHSA, the increase in estimation variance as block size increases is determined by calculating the estimation variance for a number of test block sizes as shown in Figure 1. Global Estimation Variance The next step in the DHSA process is to calculate the estimation variance associated with the resource estimate for a specific area of interest (typically the entire tenement or an area that will be mined in a specific period of time related to the mine planning cycle, typically 1 year or 5 years). If we assume a regular grid across the area we are estimating and we assume the deposit being estimated is only two dimensional (i.e. a coal seam) then we can use an approximation for the estimation variance over the area of interest, also known as the global estimation variance, Ơ 2 EST, which is given by the equation below; = N is the number of blocks at the specific test block size that would fit into the area of interest. The above approximation is based on the direct combination of independent elementary errors and assumes all blocks are roughly square. This approximation is less effective when the number of blocks N is small, which becomes a problem when we want to determine Ơ 2 EST over an area using very large blocks. The final step in the DHSA process is to convert the global estimation variance into a standard deviation by taking the square root and then determining the relative error at the 95 percent confidence interval around the mean, expressed as a percentage of the mean value, using the following equation; 95h % ±! "#$ % & 100% Where ƠEST is the standard deviation and µ z* is the mean value. 4

5 Limitations of Global Estimation Variance and DHSA The DHSA method is normally only used to determine percentage errors for larger study areas, typically at least equivalent to one years mining. Smaller areas than this also suffer from the problem with the low N in the equation for Ơ 2 EST. For smaller study areas, conditional simulation is considered to be a better geostatistical method to use. For larger areas, benchmarking conducted by Bertoli et al (2013) has shown that results obtained from DHSA are very similar to those obtained from conditional simulation. Approach to DHSA used in the three case studies The following section describes an easy to use method for conducting a DHSA. Examples are drawn from three geostatistical studies recently conducted by HDR mining consultants for Peabody Energy Australia s Wilpinjong and Coppabella mines and West Burton exploration tenement respectively. Software used in these studies is the Stanford Geostatistical Modeling Software (SGeMS), a free but user friendly geostatistical modelling software package developed by Stanford University. Selection of Critical Variables Before embarking on the DHSA study, a number of critical variables should be selected for the study. The Coal Guidelines define critical variables as physical and chemical properties of the coal that may potentially limit the reasonable prospects for eventual economic extraction. For the series of DHSA studies conducted for Peabody Energy Australia, two critical variables were selected, namely seam thickness and raw ash %. Seam thickness has a direct influence on coal volume. Raw ash% is indicative of the unwashed, raw coal quality and it is also directly related to other critical coal quality variables such as raw coal density and washed coal yield. EDA The first step in the process is the Exploratory Data Analysis or EDA. It has been found that a combination of histograms and colour ramped spatial plots of the composited seam intersections for each variable, for each seam, are useful tools when conducting the EDA. This is done in conjunction with the tabulated global statistics which present the mean, variance, standard deviation and the coefficient of variation for each seam/domain. In the case of coal exploration data sets, drilling grids are normally fairly evenly distributed and there is no need for declustering prior to generation of global statistics and histograms. However, in cases where clustering of holes is a problem, it is suggested that declustering should be performed prior to generation of global statistics. 5

6 Figure 2 Seam thickness histogram and colour ramped plot showing distribution of points for the E12E22 seam In Figure 2, the seam thickness histogram for the E12E22 seam at Wilpinjong shows a thin seam tail to the histogram. Closer inspection shows that all thin seam composites below 1.2 m involve intersections truncated by weathering. The colour ramped plot shows that the thin seam intersections (blue) are not grouped spatially and are therefore not part of a separate domain. These samples were therefore excluded prior to conducting the variogram analysis. Figure 3 Seam thickness histogram and colour ramped plot showing distribution of points for the D2 seam In Figure 3, the seam thickness for the D2 seam at Wilpinjong shows a wide range of values and examination of the colour ramp plot shows that there is a possibility that two domains may be present, a thin seam domain in the south and a thick seam domain in the north. However the variogram for D2 seam thickness in Figure 4 shows significant trend in the data, which importantly only starts at a lag distance beyond the sill of the variogram. As a result it was possible to successfully model the variogram for D2 thickness without the need for domaining. 6

7 Variography The second step in the process is variography on a cleaned data set where outlier values due to sampling or database/laboratory issues have been identified and fixed/removed and mixed domains separated. In the three case studies presented, variography was conducted using omnidirectional variograms for a number of reasons; Firstly it can be shown that the range of an omnidirectional variogram is the same as the average range of the maximum and minimum axis of continuity in a deposit that exhibits directional anisotropy (Table 7.2 and 7.4 in Isaaks and Srivastava, 1989). As a result, whether a single omnidirectional or two anisotropic variograms are used, the estimation variance calculated for a square block is the same. Secondly, in coal deposits, there is often a shortage of valid data points for variogram analysis, particularly in the down dip direction which often corresponds to the direction of minimum continuity. Use of an omnidirectional variagram allows for pairs to be identified in all directions, there bye making maximum use of available data. Nevertheless, it should be noted that use of an omnidirectional variogram is an approximation to deal principally with a low number of pairs in the down dip direction. In cases where directional anisotropy is pronounced, this approximation becomes less tenable and variograms should be constructed and drill hole spacings determined for both axes of continuity. Figure 4 Variogram for D2 seam thickness showing prominent trend in the data When modelling variograms, it is important to do this in conjunction with the final (clean data set) histogram for the attribute being modelled as the population variance for the attribute gives a guide to which semi-variogram value to set the sill of the variogram model at (the population variance should be similar to the variogram model sill). 7

8 Apart from the common problem of lack of sufficient data points to allow for a robust variogram to be constructed, discussed above with reference to the use of omnidirectional variograms, other commonly encountered difficulties associated with variogram modelling of coal deposits are; Trend in the data Uncertainty around what nugget effect to use In the case of trend in the data, normally a sill to the experimental variogram is seen before the trend sets in, as shown in Figure 4, in which case the trend does not impact on the variogram modelling. If this is not the case then trend in the data must be removed by correcting for the drift in the data. Another common problem encountered in coal variography is uncertainty in what nugget effect to use, principally due to the common lack of close spaced drilling. This problem can be exacerbated by use of a too long lag distance between pairs as illustrated in Figure 5 and Figure 6. In Figure 5, a lag of 125 m has been used and in Figure 6 a lag of 50 m has been used. It can be seen that at a lag of 125 m, one could be tempted to model a nugget effect of around 50% of the sill value. However at the shorter lag, the nugget effect of 10% of the sill, which was used in the variogram model in this case, can be seen to be a better reflection of the actual nugget effect. When is doubt as to where to set the nugget effect, experience gained from modelling variograms for a large number of coal deposits has shown that a nugget of around 10% of the sill value is a good approximation. Figure 5 Variogram for the B23 seam thickness at Wilpinjong using a 125 m lag 8

9 Figure 6 Variogram for the B23 seam thickness at Wilpinjong using a 50 m lag Calculation of Estimation Variance The third step in the process is to calculate the estimation variance when using a central sample to estimate an attribute value into a square block. A range of test block side lengths should be selected. The range of the variograms should inform the test block sizes used from around 10% of the maximum range up to between 200% or 400% of the maximum range. It is also a good idea to use a regular increment between successive block side lengths. Once the test parameters have been set up, the variogram model parameters can be used to calculate the estimation variance for that attribute for each test block size. Charts which provide the solution to Equation 1 for spherical and exponential isotropic (omnidirectional) variogram models, provided in Journel and Huijbregts (1978), were used for this purpose. Care should be taken to add the nugget effect semi-variogram value to that calculated from the estimation variance equation (Equation 1) for each variogram model structure, to arrive at a total estimation variance for each attribute at each test block size. Converting to the Estimation Variance to a Global Estimation Variance The fourth step involves converting the estimation variance for each attribute at each test block size to a global estimation variance using Equation 2. In order to do this, the size of the study area needs to be determined. This could be the entire resource area (tenement or mine lease) or a sub-set of this equivalent to the area that would be mined within a certain period (typically 1 year or 5 years). The number of test blocks of each size that fit into the study area (N) is calculated and the estimation variance for each test block size is divided by N (Equation 2) to obtain the global estimation variance for each test block size. The higher value of N, the lower the global estimation variance for the study area. A high value for N can be achieved by reducing the block size (in other words reducing the drill spacing, which makes intuitive sense) or by increasing the test size area. This makes less intuitive sense and a standardized study area of 5 years mining is recommended as a result. 9

10 This will allow for direct comparison with other DHSA studies published in the literature such as Bertoli et al (2013) which in turn will allow for benchmarking of results (as was done in each of the three case studies). Calculation of the study area size can be done using Equation 4 which incorporates the mining period, the likely mining rate, the total thickness of seams to be mined and the estimated average raw coal density; Study area = years mining x mining rate / estimated total thickness of seams too be mined x estimated average density 4 Using the Global Estimation Variance to determine confidence in an estimate (resource classification) Global estimation variances at each test block size then need to be converted into relative errors expressed as a percentage of the mean value by applying Equation 3. Relative percentage errors can then be plotted against the test block/drilling grid size and the distances at which the 10%, 20% and 50% relative percentage error thresholds are reached can be used as resource classification distances for Measured, Indicated and Inferred Resources respectively. Two common problems with determining the Inferred distance are illustrated in Figure 7 and Figure 8. In Figure 7, the 50% relative error is not reached at the maximum test grid spacing. In this instance the trend of increasing error with grid spacing is extended until the 50% error threshold is intersected and the associated Inferred classification distance is read off the x-axis by projecting the 50% percentage error intersection point onto the x-axis. In Figure 8, there is a marked inflection in the trend line of relative percentage error against increasing grid spacing associated with the point where N values of 1 are reached. When the block size matches or exceeds the test area size, N = 1 for all remaining test block sizes, resulting in the increase in global estimation variance with increasing block size levelling off. In this case, the trend line is projected from immediately before the inflection point and then the Inferred classification distance is read off the x-axis in the same way as described previously. It should be noted in the first example that it is possible in some cases that a 50% error will never be reached for attributes with a low population variance. In such cases there should theoretically be no limit to Inferred Resources within the tenement (apart from the limit imposed by the margin of the drilling) and projecting the trend to reach a 50% error is therefore seen as a conservative approach. In the second example, the study area is too small to allow for a 50% error to be reached. In this case the projection to a 50% error is purely a theoretical exercise to get a maximum distance for Inferred Resources as Inferred Resources will fill the entire study area anyway, at any test grid size above the one at which N = 1 first occurred. 10

11 The example shown in Figure 9 is of the LCU1 seam in the East Domain at Coppabella Mine. Note firstly the much steeper trend in the thickness relative percentage error curve which reaches a 10% error at only 175 m, whereas in the 5 year case at Wilpinjong Mine for the M4 seam thickness, 10% error is reached at 1250 m. This reflects the much more complex geology and variable seam thickness at Coppabella as compared to Wilpinjong. The problem encountered in this example is that thickness relative percentage errors are greater than that for coal quality (raw ash%) and the convention adopted in such instances is to use the attribute with the highest errors for classification distance determination (which in this case would be thickness). Figure 7 Wilpinjong M4 seam, relative percentage errors for LOM Figure 8 Wilpinjong M4 seam, relative percentage errors for 5 years mining 11

12 Figure 9 Coppabella LCU1 seam, relative percentage error for 5 years mining Practical application of DHSA results in determining resource confidence outlines Section of the Coal Guidelines indicates that Resource confidence outlines should be determined by the merging of Quantity confidence limits (tonnes) with Coal Quality confidence limits. The final confidence limits should be the more constrained of the two. Commonly, quantity points of observation (Pob s) are more numerous than quality Pob s and there has been a tendency in the past to use confidence limits based on only quality Pob s for resource classification. There are however deposits in which there is greater quantity variability than quality variability, in which case the drill hole spacings determined from a DHSA study can be applied to both quality and quantity points of observation (Pob s) to determine resource outlines. Again the final confidence limits should be the more constrained of the two. An example of this is shown below for Coppabella in Figure 10, where a spacing of 350 m is used for quantity points to define the Indicated area. The corresponding spacing for quality points for Indicated Resources would be 600 m (Figure 9) and although not used as the primary classification distance, a polygon using this spacing is constructed around quality points of observation. The area of Indicated Resource is then the intersection between these two polygons. 12

13 Figure 10 Intersection of Indicated polygons for quantity and quality points for the LCU1 seam at Coppabella Quantity Quality Intersection area (green) It is considered that the application of spacings determined using DHSA to resource classification should never be carried out without due consideration to other factors which may affect confidence in the estimate. These factors fall within the seven criteria listed by the Coal Guidelines which may be considered when assessing confidence. It should also be noted that DHSA spacings relate to global estimation variances (and associated relative errors) over the study area considered, at a given average drill hole spacing. It therefore follows that isolated pockets of drill holes, away from the main body of drilling at any given spacing, are associated with higher relative errors within the local isolated polygon. Care should therefore exercised in this regard when using spacings obtained using DHSA in coal resource classification, not to use isolated resource mask polygons. Case Study Results DHSA studies were conducted for three coal properties belonging to Peabody Energy Australia, namely Wilpinjong Coal Mine in NSW, Coppabella Coal Mine in the Bowen Basin and the West Burton exploration tenement in the Bowen Basin. The aims of these studies were different for each property, related to differing operational requirements in each case, namely; Wilpinjong determine drill spacings for resource classification Coppabella determine the drill spacing for a 10% relative error over 1 year and 5 years. West Burton determine drill spacings for classification and further infill drilling 13

14 Figure 11 Total coal thickness distribution at Coppabella Mine and three domain areas (Creek, Central and East) In the case of Coppabella, it was important to determine the confidence limits to the estimate as variation in ROM ash% at the mine was being studied in order to determine whether this was due to the geological model being less accurate in places or due to increased mining dilution. The first step in each of the studies was EDA and domaining of the data. At Coppabella the mine was divided into three domains, namely East Domain, Central Domain and Creek Domain, based on variation in total coal thickness across the deposit and the presence of faulting and intrusives (Figure 11). East Domain contains intrusive sills which increase thickness variability in this area compared to the other two domains. Central Domain has a more consistent total coal thickness of around 10 m which drops to a total coal thickness of around 6 m for Creek Domain. At Wilpinjong it was not necessary to domain any of the seams. Although significant trend is present in thickness for some seams this did not prevent suitable variograms from being modelled in each case. In the case of West Burton, a relatively small localised area of higher raw ash% was found for the GM seam, however it was decided not to domain this out and to rather consider this as part of the variability in the single domain. This is because this is an exploration tenement with relatively wide spaced drilling in places. If the high ash area were to be domained out, this would reduce the population variance for the two resulting domains and increase the classification spacings as a result. It is possible that further infill drilling may find other localised high ash areas within the larger domain currently deemed to be low ash. This would result in the larger drill spacings determined previously due to domaining being incorrect. It 14

15 was therefore decided to err on the side of caution and to consider the GM seam to have higher ash variability from the outset. After completing the EDA and variography on the two critical variables selected for each study, namely thickness and raw ash%, the estimation variances for a range of block sizes and global estimation variances were determined (a selected set of variograms for critical variables are shown in Figures 12 to 14). Test block sizes were smaller for Coppabella due to the shorter ranges exhibited by the variogams. Input parameters used to determine the study area size for 5 years mining for each study are shown in Table 1. It is important to note that the total coal thickness used in the calculation of study area size is not a straight summation of average coal thickness for all seams in the resource. It is based on an assessment of average coal thickness and interburdens in order to determine the number of potential simultaneous mining faces. This in turn allows for an estimate of the potential average total working section thickness per annum. Table 1 Input parameters to determine study area size for 5 years mining Site Coppabella Wilpinjong West Burton Assumed average mining rate (Mtpa) Estimated total working section thickness (m) Study area (m 2 ) 2,000,000 6,385,000 8,000,000 Results from the three studies for some key seams are presented in Table 2. In Table 2, a measure of population variability, the coefficient of variation, is shown for each critical variable for each seam, together with the variogram range for each variable and classification distances determined through DHSA. In the case of Wilpinjong, the M4 seam has greater classification distances than the A12 seam, mainly due to the much higher CV for ash for the A12 seam. The other two main contributing factors to the classification distance, namely study area size and variogram range are similar. For Coppabella, The LCU1 seam in the Central Domain has larger classification distances than for the same seam in the East Domain. This is due to the higher variability of the LCU1 seam thickness in the East Domain as evidenced by the higher CV for thickness of 0.4 and shorter variogram ranges, which results in shorter classification distances. This higher variability is due to the presence of intrusive sills in this domain. 15

16 Table 2 Selected results from the three DHSA studies Mine/Tenement Wilpinjong Coppabella West Burton Seam M4 A12 LCU1 East Dom LCU1 Central Dom GM Thickness CV Ash CV Variogram range Thickness (m) Variogram range (m) ash Measured Spacing (m) Indicated Spacing (m) Inferred Spacing (m) CV: Coefficient of Variation The Coppabella LCU1 seam in the East Domain has a similar CV for thickness to the A12 seam ash CV at Wilpinjong; however classification distances for LCU1 East Domain are much shorter. This is partly a function of the much smaller Coppabella study area size, which in turn is a function of the lower mining rate due to the complexity of the deposit. Secondly the variogram ranges for LCU1 East Domain are much shorter. In the case of West Burton, the study area size is larger than at Wilpinjong and the CV for ash for the GM seam is similar to that of the M4 seam. Despite this, the classification distances for the GM seam are less than that of the M4 seam, especially for Measured and Indicated. This is the result of the much longer variogram range (more than double) for the M4 seam ash as compared to the GM seam ash variogram range. 16

17 Figure 12 Wilpinjong A12 seam raw ash variogram 17

18 Figure 13 Coppabella LCU1 seam, East Domain, thickness variogram 18

19 Figure 14 Coppabella LCU1 seam, Central Domain, thickness variogram 19

20 Discussion The DHSA method is presented as an easy to use process which includes examples from case studies which illustrate solutions to commonly encountered problems in coal. The use of SGeMS geostatistical software and a reference to charts for solving the estimation variance equation, published in Journel and Huijbregts (1978), are mentioned so as to enable geologists without access to sophisticated (and expensive) geostatistical software to conduct their own DHSA studies. The results of the three DHSA studies conducted by HDR mining consultants for Peabody Energy Australia are presented and discussed. These results clearly show that the main criteria in determining classification distance using the DHSA method are; Population variability. Spatial continuity, one measure of which is the variogram range. Size of the study area, which in turn is a function of working section thickness and mining rate. The results from the three studies are consistent with each other and differences in classification distance between seams at the three sites can be explained by variation in the three main criteria listed above. It is considered that this paper shows that the DHSA method, as presented in this paper with specific reference to its application to coal, represents a relatively simple way of using geostatistics to assess confidence in coal resource estimates. The case studies presented show that DHSA can be applied to both mature mining projects as well as early stage exploration projects. It should be noted however that the approach to DHSA in the case of exploration projects needs to be more conservative, recognising that data gaps potentially exist. An example of this is the decision not to domain out a localised high ash area in the GM seam when conducting the West Burton DHSA study. DHSA determined spacings should also not be used in isolation to other factors that may affect confidence in a coal resource estimate, such as degree of faulting for example. 20

21 References BERTOLI, O., PAUL, A., CASLEY, Z., & DUNN, D., 2013: Geostatistical drillhole spacing analysis for coal resource classification in the Bowen Basin, Queensland, International Journal of Coal Geology, 112, pp AUSTRALIAN GUIDELINES FOR THE ESTIMATION AND CLASSIFICATION OF COAL RESOURCES, 2104: The Coalfields Geology Council of New South Wales & the Queensland Resources Council. ISAAKS, H.I., & SRIVASTAVA, R.M., 1989: Applied Geostatistics, Oxford University Press, New York, New York. JOURNEL, A.G., & HUIJBREGTS, CH, J., 1978: Mining Geostatistics, The Blackburn Press, Caldwell, New Jersey. WILLIAMS, C.M., NOPPE, M., & CARPENTER, J., 2010: Coal Quality estimation error Ordinary Kriging challenges inverse distance, Bowen Basin Symposium 2010 Back in (the) Black, Geological Society of Australia Inc. Coal Group and the Bowen Basin Geologists Group, Mackay, October 2010,