Prediction of coalbed methane content based on uncertainty clustering method

Abstract

An analysis of factors that influence coalbed methane content showed that seven factors had a major influence: coal thickness, methane concentration, vitrinite reflectance, permeability, cracks, and sealing conditions hydrodynamic conditions were selected as criterion indexes. The prediction model of coalbed methane content was built based on the uncertainty measure theory. Data from six regions in the central and southern Qinshui Basin were used as the training sample. The sample mean was set as the cluster centers, and the index weight was determined by information entropy theory. Calculation of the multi-index comprehensive measure of the sample showed that the sample was classified according to the minimum uncertainty measure distance principle, which was used to predict coalbed methane content. The research results show that: the obtained level by the prediction method is consistent with the practical level, the predictive value is basically consistent with the actual value, coalbed methane content prediction method based on uncertainty measure theory is reliable and practical, as well as showing a new way for the prediction and evaluation of coalbed methane content in the future.