关键词: 油气田井开发工程, 近红外光谱, 油页岩, 含油率, PLS建模, 数据预处理, 光谱预处理, 矩阵预处理

Abstract:

An appropriate data pre-processing method is needed to improve the modeling precision for oil yield analysis of oil shale in situ by the near infrared spectroscopy(NIRS)analysis technique. With nature and proportioning samples, the spectra preprocessing and Partial Least Square (PLS) modeling experiments were carried out by two ways (spectra and matrix) of data preprocessing. Results show that the appropriate spectra preprocessing (including first derivative, auto-scaling, mean centralization and some combined methods, such as adding mean centralization after smoothing, denoising and de-baseline, respectively) can enhance the spectrum identity of the same sample. Three combined data preprocessing methods (including combined mean centralization or first derivative spectra preprocessing with auto-scaling matrix preprocessing) can improve the precision of NIRS analysis of PLS model for oil yield from proportioning samples.

Key words: development engineering of oil and gas well, near infrared spectroscopy, oil shale, oil yield, PLS model, data pre-processing, spectrum processing, matrix processing