An approach of data mining for evaluation of complex formation using well logs

Data mining is regarded as one of the ten key techniques for challenging problem of oil exploration and development.A practical approach for evaluation of the complex formation was presented using the predictive data mining techniques.Both feature selection and parameter optimization were performed using the genetic algorithm.The unbiased estimation of generalization error was calculated with the repeated cross-validation.The final optimal model was selected from the results obtained by using the multiple learning algorithms.The water-flooded interval in the Lower Kelamayi Reservoir of Liuzhong area in Karamay Oilfield was evaluated by using eight feature subsets and twelve models obtained from five distinct kinds of classification methods,including Decision Tree (DT),Artificial Neural Network,Support Vector Machines (SVM),Bayesian Network and Ensemble Learning method.The results show that the SVM is superior to others in the prediction accuracy (91.47 % ) and can be used as the final classification model.The DT can be used as the assistant model for discovering knowledge because of its easy understandability.It is suggested that the high-level classification models can be obtained using the data mining approach,and the precision of well log interpretation can be effectively improved in solving the problems such as identification of oil-bearing formation and lithologic discrimination.