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Rock properties prediction, categorization, and recognition from NMR echo-trains using linear and nonlinear regression
Rock properties prediction, categorization, and recognition from NMR echo-trains using linear and nonlinear regression
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机译:使用线性和非线性回归从NMR回波中预测岩石特性,对其进行分类和识别
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摘要
A partial least squares (PLS) regression relates spin echo signals with samples having a known parameter such as bound water (BW), clay bound water (CBW), bound volume irreducible (BVI), porosity (PHI) and effective porosity (PHE). The regression defines a predictive model that is validated and can then be applied to spin echo signals of unknown samples to directly give an estimate of the parameter of interest. The unknown samples may include earth formations in which a NMR sensor assembly is conveyed in a borehole.
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