Abstract: 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.

Key words: formation evaluation, data mining approach, prediction model, parameter optimization, feature selection, support vector machine, water-flooded interval, decision tree

摘要： 数据挖掘是应对石油勘探开发未来挑战的10项关键技术之一。提出了一种将预测性数据挖掘技术应用于复杂储层测井评价的方法。将遗传算法应用于特征子集选择和建模参数优化,利用重复交叉验证得到泛化误差的无偏估计,并从选定的多学习算法建模结果中优选出最终预测模型。以克拉玛依油田六中区克下组油藏水淹级别划分为例,在该方法框架内对比研究了8种特征子集方案和决策树、神经网络、支持向量机、贝叶斯网络、组合学习等5种分类方法12种预测模型。结果表明支持向量机预测准确率最高,达91.47%,选择其作为最终预测模型,而决策树模型容易理解,作为辅助参考模型。利用该数据挖掘方法解决油、气、水层识别和岩性划分等问题时,能够获得高性能的分类模型,从而将有效地提高解释精度和符合率。

关键词: 储层评价, 数据挖掘, 预测建模, 参数优化, 特征选择, 支持向量机, 水淹层, 决策树