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《Oil Geophysical Prospecting》 2010-01
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Studies on application of data mining method in oil exploration and development

Tan Feng-qi1,2,Li Hong-qi1,2,Meng Zhao-xu3,Guo Hai-feng1,2 and Li Xiong-yan1,2.1.Institute of the Earth Recourses and Information,China University of Petroleum,Beijing City,102249,China2.State Key Lab of Petroleum Resources and Prospecting,China University of Petroleum,Beijing City,102249,China3.E&P Research Institute,Xinjiang Oilfield Company of Petrochina,Karamay City,Xinjiang Uighur Autonomous Region,834000,China  
With oil EP(Exploration and Production) developing continually,in order to make new profit from massive oil data,it is necessary to apply the data mining method in oil exploration and development so that a high performance prediction model for geology,reservoir and fluid property evaluation was built up.The method consists of 3 loops which are feature selection,model parameter optimization and performance evaluation,the key technology is to apply generic algorithm in feature selection and model parameter optimization,unbiased estimation for generalized accuracy was obtained through repeating cross validation and the final model was optimized from the several methods.By taking water-flooded formation evaluation for conglomerate reservoir in Karamay oilfield as the example,6 feature subset schemes and 5 learning methods which are decision tree,neural networks,support vector machine,Bayesian network and array learning were optimized,by integratedly considering the accuracy of the prediction model and the operability of the generation rules,decision tree model was selected as final prediction model for conglomerate reservoir water-flooded grade evaluation.Compared with other traditional geophysical methods,the advantages for application of the data mining method are as below:the data in multi-disciplines could be utilized,complete prediction model was built,the regularities were searched and found out,prediction accuracy was raised and oilgas EP could be served better.
【CateGory Index】: P631.4
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【Co-citations】
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