Log-based lithology identification of volcanic rocks using random forest method:A case study of Carboniferous strata in the Dixi area,Junggar Basin
The accurate lithologyidentification of volcanic rocksserves as a significant foundation for the efficient exploration and exploi-tation of volcanic reservoirs.However,volcanic reservoirs exhibit intricate lithologies,longitudinalmultistagesuperimposition,and fast transverse phase transition,which reduce the accuracy of crossplots in lithologyidentification ofvolcanic reservoirs.Based on the optimal parameter combination of the model determined through grid search and orthogonal experiments,this study quantitatively evaluatedthe effects of conventional log curves on the lithologyidentification of volcanic rocks.Withthe natural gamma ray,compensated neutron,sonic interval transit time,and formation resistivity as lithologic indicators,this study builtan intelligent model for the lithology identifi-cation of Carboniferous volcanic rocks in the Dixi area in the Junggar Basin using therandom forest method.This study identified the li-thologies of thecored intervalswith a cumulative thickness of 870 m infive cored wells in the study area,with the coincidence ratesof the identification results with thin section identification results and core description resultsreaching 76.67％and 85.98％,respectively.This suggestssignificant identification effects.Therefore,this studysets the stagefor the fine-scale evaluation of volcanic reservoirs in the study area.
random forestlithology identificationvolcanic rockmachine learning
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