ProxInLAS, a software program for detecting coal layers and estimating parameters of layers, using geophysical well-logs

The present paper describes the algorithm and working method for detection and estimation of proximate parameters of coal beds based on digital well-log data. Designed and developed by the authors in Visual Studio using C#, ProxInLAS is used to define a threshold value on each log to distinguish between coal and non-coal layers, followed by estimating each of the coal proximate parameters by importing the data pertaining to a particular borehole: the reference borehole. Inputs of the software include the reference borehole data, target functions for accepting a layer as coal, and the methods employed to estimate the proximate parameter. The software relies mainly on two algorithms, a detection algorithm for detecting coal layers, and an estimation algorithm for estimating proximate parameters of the detected layers. Loaded by the well-log and core-sampling data, the detection algorithm calculates a set of threshold values to distinguish between the coal and non-coal layers based on the frequency distribution function of the well-log values near coal beds. ProxInLAS offers four methods for estimating proximate parameters: (1) a method based on the regression between the considered parameter and a particular log, (2) a method based on a combination of several linear relationships, (3) radial basis function (RBF) method, and (4) geostatistical interpolation method. In addition to numeric records, the software can present its outputs graphically to provide a simple and instant view of the results. The case study used to evaluate the performance of ProxInLAS.