Adaboost algorithm combined multiple random forest models (Adaboost-RF) is employed for fluid prediction using well logging data

Abstract

Well logging fluid prediction is of great significance in oil and gas exploration. Based on data mining technology, this paper proposes an adaptive boosting random forest (Adaboost-RF) method for well logging fluid prediction. First, we use the Adaboost algorithm for feature selection, train a weak classifier by repeatedly weighting observations and correcting hard-to-classify samples, and obtain a combination of multiple weak classifiers. This method can effectively improve the accuracy and robustness of the classifier and can reduce the risk of overfitting. Then, we use random forest (RF) as a basic classifier to build an Adaboost-RF model for well logging fluid prediction. The combination of Adaboost and RF can further improve the stability and accuracy of the classifier. To verify the performance of this method, we performed experimental evaluation using real well logging data. Experimental results show that the Adaboost-RF method can have higher accuracy and stability in log fluid prediction than the traditional method (backpropagation neural network) and the method using RF alone. In summary, this method combines the characteristics of Adaboost and RF, which can improve the accuracy and stability of the classifier and is easy to implement and generalize, providing a new, efficient, and accurate fluid prediction method for the field of oil and gas exploration.