The determination of coalbed methane gas content is often significantly affected by human fac-tors,and the measurement cost is high and the time is long.However,the logging data is continuous and easy to obtain.Through the establishment of a linear relationship with the gas content,six parameters of compensation neutron,density,natural gamma,resistivity,sonic time difference and depth are identified and used to establish the prediction model for the gas content of the coal seam.Three methods,multiple linear regression,BP neural network and random forest,are used to construct a prediction model for coal reservoir gas content.Subsequently,the constructed model is used to test the blind well,and the results show that the above three prediction models can effectively predict the gas content in the coal seam,with the random forest model showing the best prediction effect in this Block.
Coal seamcoalbed methanemultiple linear regressionBP neural networkrandom forestgas content
维普
万方数据
