LAUSR

In this paper, the algorithm and working method of the software are described, which allows the detection of depth and thickness of coal beds by digital information in well-logs. The software known as COALINLAS is designed and developed in Visual Studio using C# by the authors. In this software, a boundary value of each log is defined for detection of coal and non-coal layers, by importing the data of a borehole, called the reference borehole. Inputs of the software are the reference borehole data including digital files of values ​​of well logs in “.las” format, and core-sampling data; and target functions for accepting a layer as coal. The main engine of software is an algorithm called the detection algorithm. Importing the well-logging and core-sampling data, this algorithm calculates the boundary values ​​to separate the coal bed from the other by the frequency distribution function for values ​​of well logs near coal beds. The frequency distribution of well logs ​​follows the generalized extreme value (GEV) function. The location of distribution depends on the mode and the scale depends on the standard deviation and the software calculates three boundary values ​​in conditions where the cumulative density function (CDF) is equal to 50%, 70 and 90%. The case study used to test the performance of software shows that the boundary limit calculated for CDF of 70% separates the layers more precisely. In this case study, it is concluded that the software has the ability to detect all coal beds in the boring path using well-logs data. Moreover, COALINLAS can identify fine-scale changes in the characteristics of layers and detect dispersed thin layers neglected in core-sampling.