基于多尺度随机森林融合地质、测井和地震资料的煤层含气量预测OA

Accurate estimation of coalbed methane(CBM)content plays a crucial role in assessing and effi-ciently exploiting CBM resources.Deep CBM is influenced by multiple controlling factors and complex genetic mechanisms.Currently,machine-learning approaches for CBM content prediction typically rely on either seis-mic or logging data.As a result,the complex geological conditions of deep coal seam are not fully accounted for.This study proposes an intelligent prediction method for CBM content,which achieves multi-source data fusion through a multi-scale modeling and deep integration strategy.The approach first extracts multi-scale sensitive at-tributes or features relevant to CBM content from geological,logging,and seismic sources.For each dataset of the same scale,adaptive modeling is performed using a Bayesian hyperparameter-optimized random forest(RF)algorithm,which enhances model robustness and prevents overfitting.The prediction results from individual scales are subsequently integrated through the least squares method to construct a multi-scale RF composite model.The proposed method is validated using a field dataset and compare its performance with that of conven-tional approaches,including single-scale RF and linear regression.The results show that,compared with these baseline methods,the proposed method reduces the mean relative error of CBM content prediction on test wells by 3.01%and 4.94%,respectively.This demonstrates that the proposed approach achieves higher accu-racy and stronger generalization capability,enabling precise characterization of the spatial distribution of CBM content.