Lithologic Identification for Imbalanced Logging Data Based on AdaC2-SVM in Drilling Process

To identify some special formation lithology with imbalanced logging data, a framework of Multi-layer lithology identification method is proposed. In this framewoke, some special lithology is divided into one class in the first layer, and each lithology is separated in the second layer. A novel algorithm of AdaCost2-support vector machine (AdaC2-SVM) is put forward using logging data of actual well located in Karamay for training, and the support vector machine-recursive feature elimination (SVM-RFE) is adopted to select attribute, and logging data from another well nearby is used for testing. Experiment result shows the G-mean and accuracy of our method is up to 95.3% and 94.4%, which has better performance than random forest(RF) algorithm, particle swarm optimization-support vector machine (PSO-SVM) algorithm and improved PSO-SVM(IPSO-SVM) algorithm. In the future, the proposed method have a good prospect and give a valuable result for geology research.