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

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 (R-2) 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 (R-2 = 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.