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1.发明申请公开(公告)号：US20130080066A1
公开(公告)日：2013-03-28
申请号：US13618327
申请日：2012-09-14
IPC分类号： G01V1/28
摘要： Subsurface reservoir properties are predicted despite limited availability of well log and multiple seismic attribute data. The prediction is achieved by computer modeling with least square regression based on a support vector machine methodology. The computer modeling includes supervised computerized data training, cross-validation and kernel selection and parameter optimization of the support vector machine. An attributes selection technique based on cross-correlation is adopted to select most appropriate attributes used for the computerized training and prediction in the support vector machine
摘要翻译： 尽管井测井和多个地震属性数据的可用性有限，仍预测了地下储层性质。 通过基于支持向量机方法的最小二乘回归的计算机建模来实现预测。 计算机建模包括支持向量机的监督计算机数据训练，交叉验证和内核选择以及参数优化。 采用基于互相关的属性选择技术，在支持向量机中选择用于计算机化训练和预测的最适合的属性
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2.发明授权公开(公告)号：US09128203B2
公开(公告)日：2015-09-08
申请号：US13618327
申请日：2012-09-14
摘要： Subsurface reservoir properties are predicted despite limited availability of well log and multiple seismic attribute data. The prediction is achieved by computer modeling with least square regression based on a support vector machine methodology. The computer modeling includes supervised computerized data training, cross-validation and kernel selection and parameter optimization of the support vector machine. An attributes selection technique based on cross-correlation is adopted to select most appropriate attributes used for the computerized training and prediction in the support vector machine.
摘要翻译： 尽管井测井和多个地震属性数据的可用性有限，仍预测了地下储层性质。 通过基于支持向量机方法的最小二乘回归的计算机建模来实现预测。 计算机建模包括支持向量机的监督计算机数据训练，交叉验证和内核选择以及参数优化。 采用基于互相关的属性选择技术，在支持向量机中选择用于计算机化训练和预测的最适合的属性。
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