Estimating coal measures associated geothermal resources using Monte Carlo simulation in the Pingdingshan Coalfield, China

Abstract

As a renewable clean energy associated with coal measures, mine geothermal is being widely developed. Accurate evaluation of coal measures associated geothermal resources can help us realize the effective development and utilization of deep minerals and geothermal energy. The most widely used evaluation method in geothermal resources is the static volume method. However, this approach only assigns specific values to the parameters and ignores the dynamics of the input parameters.Therefore, this paper takes the Cambrian limestone heat storage in the eastern mining area of Pingdingshan Coalfield as a case study. Based on the volume method, monte Carlo method is adopted to reduce the uncertainty of input parameters, so as to more accurately estimate the geothermal resources associated with coal measures in the eastern mining area of Pingdingshan coalfield. When Monte Carlo method is applied to the evaluation of heat storage resources, triangle and uniform distribution model are used as input parameters, and the number of simulation iterations is 10000. The results show that the heat contained in geothermal resources is (5.037 ~ 15.82) × 1014J (most likely 9.538 × 1014J, the probability is greater than 7.8%), in which the heat contained in geothermal water is (0.8806 ~ 6.531) × 1014J (most likely 3.196 × 1014J, probability greater than 7.1%), and the heat contained in the rock matrix of Cambrian limestone is (3.287 ~ 10.36) × 1014J (most likely 6.341 × 1014J, probability greater than 7.3%). On this basis, the theoretical and technical framework of coal thermal co mining is proposed to realize the collaborative mining of coal measures associated geothermal resources.