NMR Data Compression Method Based on Principal Component Analysis

Abstract

Hundreds of thousands of echo data are collected in nuclear magnetic resonance (NMR) logging. In order to get the formation information, such as porosity, permeability, fluid type, fluid saturation, pore size distribution, etc., those NMR data need to be inversed. Generally, compression is implemented to the gathered significant amounts of NMR echo data before they are inversed to reduce the inversion computation. This paper puts forward a new kind of NMR echo data compression method based on the principle of principal component analysis (PCA). Aiming at losing the minimum information, original echo data were compressed by retaining those who contribute the largest amounts of information for reflecting the formation characteristics, and eliminating those who contribute little or even are redundant. One-dimensional and two-dimensional NMR echo data were simulated, and then compressed, respectively, using the PCA method. The NMR echo data before and after PCA compression were inversed respectively, and the inversion results of compressed and uncompressed were compared. The result showed that the PCA method could be used to compress the NMR echo data without losing much information even under a high compression ratio.