Toward improved coal density estimation from geophysical logs						*

Binzhong Zhou; Joan Esterle

doi:10.1071/EG08011

RESEARCH ARTICLE

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Toward improved coal density estimation from geophysical logs ^*

Binzhong Zhou ¹ ³ Joan Esterle ¹ ²

+ Author Affiliations

- Author Affiliations

¹ CSIRO Exploration and Mining, PO Box 883, Kenmore, QLD 4069, Australia.

² Present address: GeoGas Pty Ltd, 103 Kenny Street, Wollongong, NSW 2520, Australia.

³ Corresponding author. Email: Binzhong.Zhou@csiro.au

Exploration Geophysics 39(2) 124-132 https://doi.org/10.1071/EG08011
Submitted: 5 December 2007 Accepted: 18 April 2008 Published: 16 June 2008

Abstract

Density plays an important role in coal resource estimation and reconciliation, as well as in defining coal quality. Current practice employs direct density measurements on widely spaced core samples, rather than utilising abundant geophysical logging data. This is mostly due to the perception that the precision and accuracy of density estimation from geophysical logs is unsatisfactory. This paper demonstrates that the density wireline log, supported by other geophysical logs, provides a reliable direct measurement of in-situ coal density. We have produced a consistent and reliable correlation of geophysical log density with a laboratory-derived density to within an accuracy of ±3%. This is achieved through careful constraints such as compensating for lost pore spaces and moisture to bring the laboratory relative density closer to in-situ environmental conditions, matching the laboratory sample depths with geophysical logs, excluding thin, boundary, and stone-band samples from the dataset, and calibrating the geophysical density with laboratory testing data and other geophysical logs by linear regression or Radial Basis Function and Self-Organised Mapping techniques. In addition, we also illustrate that the improved geophysical log density can be used for coal quality estimation.

Key words: coal density, coal quality, geophysical logs.

Acknowledgments

This work was funded by Australian Coal Association Research Program (ACARP), and supported by Rio Tinto Coal Australia Pty Ltd, Anglo Coal Australia Pty Ltd, and BHP Billiton Mitsubishi Alliance (BMA). We thank the following people: Ken Preston (Rio Tinto), Paul Sullivan (Rio Tinto), Mark Biggs (Anglo Coal), Andy Willson (Anglo Coal), Wes Nichols (Anglo Coal), Carrie Moffitt (BMA), Roland Turner (Borehole Logging Consultancy services), Peter Hatherly (University of Sydney) and Denis Mylrea (Weatherford International Ltd) for their various discussions, suggestions and supports during this study. Steve Fraser (CSIRO) is thanked for running the SOM algorithms on the data presented in this paper. Thanks also go to Gary Fallon and Bruce Dickson for their constructive review of the manuscript.

References

Alvarez, D., and Borrego, A.G., 2005, Apparent versus true density for the volume-to-weight transformation in coal blends: Journal of Microscopy 220, 221–228.
| Crossref | GoogleScholarGoogle Scholar | B. P. B. Instruments, 1981, Coal interpretation manual: East Leake, England.

Coléou, T., Poupon, M. and Azbel, K. (2003) Smooth fitting of geophysical data using continuous global surfaces: The Leading Edge 22, 942–953.

Dong, H., Hou, J., Li, N., and Wang, H., 2001, The logging evaluation method for coal quality and methane: Geophysical & Geochemical Exploration 25, 138–143.
Elkington P. A. S. , Samworth J. R. , and Enstone M. C., 1990, Vertical enhancement by combination and transformation of associated responses: Society of Professional Well Log Analysts 31st Logging Symposium, Lafayette, La., Transactions, HH1–HH20.

Essenreiter, R., Karrenbach, M., and Treitel, S., 2001, Identification and classification of multiple reflections with self-organizing maps: Geophysical Prospecting 49, 341–352.
| Crossref | GoogleScholarGoogle Scholar | Fishel K. W. , and Mayer R., 1979, Extremely high resolution density coal logging techniques. In G. O. Argall ed., Coal Exploration: Proceedings of the 2nd International Coal Exploration Symposium, Denver, October 1978, 490–504.

Fletcher I. S. , and Sanders R. H., 2003, Estimation of in-situ moisture of coal seams and product total moisture: Final Report for ACARP Project C10041.

Groves, B., and Bowen, E., 1981, The application of geophysical borehole logging to coal exploration: Australian Coal Geology 3, 51–59.
Kahraman H., 1998, Literature survey on coal quality predicted from the geophysical logs: CSIRO internal report (unpublished).

Meyers A. , Clarkson C. , Wex T. , and Leach B., 2004, Estimation of in-situ density of coal from apparent relative density and relative density analyses: Final Report for ACARP Project C10042.

Mullen M. J. , 1988, Log evaluation in wells drilled for coalbed methane: Geology and Coalbed Methane Resources of the Northern San Juan Basin, Colorada and New Mexico Guidebook, Rocky Mountain Association of Geologists, 113–124.

Mullen M. J., 1989, Coalbed methane resource evaluation from wireline logs in the Northeastern San Juan Basin. A case study: Proceedings: SPE Joint Rocky Mountain Regional/Low Permeability Reservoirs Symposium and Exhibition, March 6–8, 1989, Denver, CO, USA.

Mutton A.J. and Turner R. , 2002, Mt Pleasant Project, Hunt Valley, NSW: Estimation of coal quality from geophysical log data – Stage 1: A report to Coal & Allied Operations Pty Ltd. (Unpublished).

Preston K. B., 2005, Estimating the in-situ relative density of coal – old favourites and new developments: The Proceeding of Bowen Basin Symposium 2005, 13–20.

Preston, K. B., and Sanders, R. H., 1993, Estimating the in-situ relative density of coal: Australian Coal Geology 9, 22–26.
Ronen S. , Schultz P. S. , Hattori M. , and Corbett C., 1994, Seismic-guided estimation of log properties, Part 1, 2, and 3: The Leading Edge, 13, 305–310, 674–678, 770–776.

Russell, B. H., Lines, L. R., and Hampson, D. P., 2003, Application of the radial basis function neural network to the prediction of log properties from seismic attributes: Exploration Geophysics 34, 15–23.
| Crossref | GoogleScholarGoogle Scholar | Schlumberger, 1991, Log Interpretation Principles/Applications: Schlumberger Educational Services.

Sliwa R. , Fraser S. J. , and Dickson B. L. , 2003, Application of self-organising maps to the recognition of specific lithologies from borehole geophysics. In A. C. Hutton, B. G. Jones, P. F. Carr, B. Ackerman, A. D. Switzer, eds., Proceedings of the 35th Sydney Basin Symposium on “Advances in the study of the Sydney Basin”, September 29–30, 2003, University of Wollongong, 105–113.

Standards Australia International, 2002, AS 1038.21.1.1–2002, Coal and coke – analysis and testing Part 21.1.1: Higher rank coal and coke – relative density – analysis sample/density bottle method. Standards Australia International.

Strecker, U., and Uden, R., 2002, Data mining of poststack seismic attribute volumes using Kohonen self-organizing maps: The Leading Edge 21, 1032–1037.
| Crossref | GoogleScholarGoogle Scholar |

^* ^*Paper presented at the 19th ASEG Geophysical Conference & Exhibition, Perth, November 2007.

¹ ¹Vertical Enhancement by Combination and Transformation of Associated Responses (Elkington et al., 1990). This technique is also called alpha processing in the oil industry, which combines a measurement that has a high accuracy but low precision with another measurement of the same quantity that has a high precision but low accuracy in order to produce a result that is better than either alone.