摘要
油气储层的识别和预测是当今热门的研究课题。本文以实地测井数据为基础,提出基于PCA的LS-SVM预测模型对储层油气进行分类预测,并与人工神经网络预测模型对比。结果表明,该模型的性能优于其它模型,具有一定的应用价值。
Identification and prediction ofoil and gas reservoirs are popular research topic today. Based on logging data, the proposed PCA-based LS-SVM forecasting model is applied identify oil and gas reservoirs, comparing with artificial neural network prediction model. The results show that the model＇s performance is stronger than other models, has a certain value.
出处
《计算机系统应用》
2012年第6期167-169,共3页
Computer Systems & Applications
关键词
主成分
最小二乘支持向量机
预测
油气
principal component
least squares support vector machine
forecasts
oil and gas
