%0 Journal Article
%T A programming method to estimate proximate parameters of coal beds from well-logging data using a sequential solving of linear equation systems
%J Journal of Mining and Environment
%I Shahrood University of Technology
%Z 2251-8592
%A Yusefi, A.
%A Ramazi, H. R.
%D 2019
%\ 07/01/2019
%V 10
%N 3
%P 633-647
%! A programming method to estimate proximate parameters of coal beds from well-logging data using a sequential solving of linear equation systems
%K Coal
%K Well-Logging
%K Proximate Parameters
%K Effect Factor
%K System of Equations
%R 10.22044/jme.2019.7702.1633
%X This paper presents an innovative solution for estimating the proximate parameters of coal beds from the well-logs. To implement the solution, the C# programming language was used. The data from four exploratory boreholes was used in a case study to express the method and determine its accuracy. Then two boreholes were selected as the reference, namely the boreholes with available well-logging results and the proximate analysis data. The values of three well-logs were selected to be implemented in a system of equations that was solved, and the effect of each well-log on the estimated values of the proximate parameter was expressed as a coefficient called the effect factor. The coefficients were incorporated in an empirical relationship between the parameter and the three well-logs. To calculate the coefficients used for the most accurate estimation, a total of 22960 systems of equations were defined and solved for every three logs. As there was the possibility of 560 combinations for selecting three logs from all the available 16 logs, the three equation-three variable systems were solved more than 12 million times. The programming methods were utilized to achieve the final results. The results of each system were tested for deviation of the estimated values of volatile matter, ash, and moisture, and the coefficients of the lowest deviation were accepted to be applied in the relation. Implementing this method for estimating the volatile matter resulted in an average deviation of 10.5%. The corresponding estimated values of the ash and moisture contents were 22% and 14%, respectively.
%U https://jme.shahroodut.ac.ir/article_1433_fca2f91ede0159b99f30c0476c8fa491.pdf