Well Log Generation Based on Hybrid Ensemble Learning Model
Well logs play a crucial role in reservoir evaluation and oil and gas resource assessment.However,the shortage issue of some well logs always exists in practical applications.The well log data are remeasured through a drilling procedure involving high cost and difficult implementation.To supplement the missing well logs without increasing economic costs,this study develops an approach for generating well logs based on the hybrid ensemble learning model.This approach efficiently and intelligently completes the missing well logging curves.The hybrid ensemble learning model combines the structural advantages of random forest model and extreme gradient boosting model,enabling deep exploration of the nonlinear mapping relationships among well log data and achieving accurate predictive results of log wells.The proposed hybrid ensemble learning model is applied to real well log data,and the generated results are compared with those of fully connected neural network model and multivariate linear regression model.Experimental results show that the well logs synthesized by the hybrid ensemble learning model exhibit higher accuracy,indicating the suitability of the hybrid ensemble learning model for well log generation.This study provides a new perspective for synthesizing artificial well logs.
well loglog generationmultivariate linear regressionfully connected neural networkhybrid ensemble learning
国家科技期刊平台
NSTL
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