Using borehole geophysical data as soft information in indicator kriging for coal quality estimation

Webber, Tiago; Costa, João Felipe Coimbra Leite; Salvadoretti, Paulo

doi:10.1016/j.coal.2012.11.005

Cited by 29 publications

(9 citation statements)

References 12 publications

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“…Indicator coding has been covered in various papers related to indicator kriging or indicator simulation (e.g., Bastante et al, 2008;de Souza and Costa, 2013;Webber et al, 2013). Indicator data can be modeled, of course, for spatial correlation using semivariograms.…”

Section: Methodology and Workflowmentioning

confidence: 99%

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

Karacan

Olea²

2014

Journal of Applied Geophysics

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"Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation" (2014). USGS Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile. For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs. Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal-Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths. Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas source within their zone of influence, paths may develop and allow methane movement towards sealed or active gobs under pressure differentials. Knowledge gained from this research will better prepare mine operations for potential methane inflows, thus improving mine safety.Published by Elsevier B.V.

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Section: Methodology and Workflowmentioning

confidence: 99%

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

Karacan

Olea²

2014

Journal of Applied Geophysics

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“…In mineral exploration, geophysical surveys are predominantly carried out for anomaly separation and delineation of geological structures. However, there are some studies about grade and reserve estimation by geophysical methods including copper grade estimation in blast holes using prompt gamma neutron activation analysis (PGNAA) in Chuquicamata copper mine in Chile (Charbucinski et al, 2003), investigation of organic pollutions effect on IP-Rs measurements in laboratory based on its results detection of pollution zone in Aveiro, Portugal (Martinho and Almeida, 2006), ore reserve estimation by VES and chemical analyses (Ehinola et al, 2009), correlation between geoelectrical data and aquifer parameters in evaluation of ground water potential (Batte et al, 2010), estimation of Ni grade using crosshole seismic velocity tomography in Canada (Perozzi et al, 2012), coal quality estimation using borehole geophysical data (Webber et al, 2013), reserve estimation of limestone and sand using geoelectrical data (Ushie et al, 2014) and predicting the pyrite oxidation and transport process in coal waste pile using resistivity methods in Iran (Jodeiri et al, 2016), At large, these studies include three subjects: I) estimation of hydraulic parameters of aquifer and hydrogeological parameters by geoelectrical data; II) estimation of grade and chemical parameters using well logging geophysical data and III) evaluation of lithology and dynamic parameters using geophysical methods that used in geotechnical investigations.…”

Section: Introductionmentioning

confidence: 99%

Mineral resource estimation using a combination of drilling and IP&Rs data using statistical and cokriging methods

Mostafaei

Ramazi

2018

Bulletin of the Mineral Research and Exploration

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The aim of this research is the mineral resource estimation using a combination of drilling and IP-Rs data. Therefore the approach of this paper is to study the correlation of induced polarization (IP) and Electrical resistivity (Rs) data with drilling data in order to grade estimation and mineral resource estimation. Reducing the boreholes number and optimization of the boreholes location is another aim of this research. The Abassabad copper mine located in Miami-Sabzevar mineralization belt northeast Iran was chosen as a case study. Within the borehole locations, geophysical profiles were designed and surveyed. After IP-Rs data inversion, 2D sections were prepared. The 3D block models of IP-Rs were constructed by geostatistical methods. The correlation between IP-Rs and drilling data were examined by statistical and geostatistical methods using regression, multivariate regression analysis, and cokriging. Based on the mentioned methods copper grade was estimated and the 3D block models of Cu grade were constructed. Obtained models were checked and compared with real Cu model compiled according to drilling data which was done after geophysical measurements. Results showed that the regression between IP data and Cu grade was more appropriate with least error. Rs data are not suitable for Cu estimation, due to changing intervals which led to increasing estimation error. Based on the suggestions of this paper, we could reduce the number of boreholes to 30% of the initial number and optimize the boreholes locations.

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“…The proximate analysis of coal samples determines the relative amounts of ash, moisture content, volatile matter, and fixed carbon, whereas ultimate analysis is used to determine the chemical constituents of coal samples: carbon, hydrogen, oxygen, sulfur, and other elements. Many regression equations and nonlinear models, including the artificial neural network method, have been developed for predicting the calorific values of a coal sample based on proximate and ultimate analyses. − …”

Section: Introductionmentioning

confidence: 99%

“…The proximate parameters and useful heat values (UHVs) are obtained from the conventional analysis of the coal core samples. Statistical and neural network methods are introduced by the researchers for estimation of proximate parameters from geophysical logs. , Previously, authors had indicated that the coal seams in the Bishrampur coalfield are of high moisture content and of banded type. The overall quality of an Indian non-coking coal seam is determined from proximate analysis and UHVs of coal samples obtained from existing bands in that seam.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coal Proximate Parameters and Useful Heat Value of Coal from Well Logs of the Bishrampur Coalfield, India, Using Regression and Artificial Neural Network Modeling

Ghosh¹,

Chatterjee

Shanker³

2016

Energy Fuels

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The energy of coal is expressed by its useful heat value (UHV), and it is the major key player in coal pricing. The objectives of this paper are to obtain (a) the regression relationship between coal proximate parameters and UHVs, and (b) multilayered feed forward neural network (MLFN) models between geophysical log responses and UHVs. Six wells are used for training the networks, and three wells are used for validating the obtained results in the Bishrampur coalfield. The mean square error (MSE) of MLFN models along with correlation (R2) values at their training, validation, and testing stages are the criteria for selecting the best model for estimation of coal proximate parameters and UHVs using geophysical log responses. The final model is selected based on low MSE (≤0.07) and high R2 values (≥0.80) at training, validation, and testing stages. The predicted UHV obtained from the best MLFN model has excellent correlation (R2 = 0.98) with the laboratory determined UHVs of three major coal seams. The predicted UHV is further implemented to grade the three coal seams of this coalfield.

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Using borehole geophysical data as soft information in indicator kriging for coal quality estimation

Cited by 29 publications

References 12 publications

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

Mineral resource estimation using a combination of drilling and IP&Rs data using statistical and cokriging methods

Prediction of Coal Proximate Parameters and Useful Heat Value of Coal from Well Logs of the Bishrampur Coalfield, India, Using Regression and Artificial Neural Network Modeling

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