Summary: Oil well productivity classification and abundance prediction are important for estimating economic benefit of a well. However, it is difficult to predict because well logs are complex and the amount of data collected today has far exceeded our ability to refine and analyze without the use of automated analysis techniques. In response to the problem above mentioned, data mining technology in recent years has shown the ability for discovering information and effectively extracts information from massive observational data sets that can be used to decisions. Especially, classification and prediction methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E\&P) in China. Therefore, data mining is regarded as one of the ten key techniques for challenging problem of oil exploration and development. In this paper, four distinct kinds of classification and prediction methods in data mining, including decision tree (DT), artificial neural network (ANN), support vector machine (SVM) and Bayesian network are used to two real-world case studies. One is hydrocarbon reservoir productivity classification with 21 samples from 16 wells logging data in Karamay Oilfield $8^{th }$ district reservoir. The results show that SVM and Bayesian are superior in the classification accuracy (95.2\%) to DT, ANN and SVM, and can be considered as a prominent classification model. Another is reservoir abundance prediction with 17 mature accumulation systems samples in JiYang depression basin. The results show that SVM is superior in the prediction accuracy (91.92\%) to DT, ANN and Bayesian, and can be taken as an excellent prediction model.