基于ReliefF和LSBoost集成树核磁有效孔隙度频谱预测及分辨率匹配研究

doi:10.7657/XJPG20250612

摘要/Abstract

摘要：

核磁测井技术是获取页岩油储层孔隙度的关键手段，但不同仪器的纵向分辨率差异严重影响油层厚度划分与甜点刻画。基于傅里叶变换，分析不同核磁有效孔隙度曲线的频谱特征，利用ReliefF算法，选取微球聚焦电阻率、声波时差、中子孔隙度、P型核磁有效孔隙度等曲线频谱幅度作为机器学习模型的输入特征，以CMR型核磁有效孔隙度曲线的频谱幅度为目标值，构建了决策树与LSBoost集成树的核磁有效孔隙度频谱幅度预测模型，对比不同机器学习模型的预测结果，LSBoost集成树模型具有更高的预测精度。为实现不同系列核磁测井间的分辨率匹配，创新性的融合时频分析法与分辨率匹配法，提出基于频谱幅度移植提高核磁有效孔隙度分辨率的方法，显著提升了低分辨率核磁有效孔隙度曲线的分辨率。重构核磁有效孔隙度曲线在油层厚度划分等应用中优势显著，充分证明了该分辨率匹配研究方法具有较高的应用价值，为准噶尔盆地吉木萨尔凹陷二叠系芦草沟组甜点分布的精准刻画以及页岩油的高效开采奠定了基础，但在高黄铁矿含量地层及薄层中存在适应性局限。

关键词: 准噶尔盆地, 吉木萨尔凹陷, 芦草沟组, 核磁有效孔隙度, 傅里叶变换, 频谱幅度, 纵向分辨率, LSBoost集成树

Abstract:

Nuclear magnetic resonance (NMR) logging is a critical method for obtaining the porosity of shale oil reservoirs, but the varying vertical resolutions of different logging tools severely affect the division of oil layer thickness and the characterization of sweet spots. The spectral characteristics of different NMR effective porosity curves were analyzed through the Fourier transform. With the spectral amplitudes of the logs including microspherically focused resistivity, acoustic, neutron porosity, and P-type NMR effective porosities selected using the ReliefF algorithm as the input features for the machine learning (ML) model, and the spectral amplitude of CMR-type NMR effective porosity log as the target value, a prediction model for the spectral amplitude of NMR effective porosity was constructed using decision tree (DT) and LSBoost ensemble tree. The prediction results of different ML models were compared, showing that the LSBoost ensemble tree model is the most accurate. For purpose of resolution matching among different NMR logs, the time-frequency analysis was innovatively integrated with the resolution matching to form a method for improving the resolution of NMR effective porosity log through spectral amplitude transplantation. This method has been validated to significantly enhance the resolution of low-resolution NMR effective porosity logs. The reconstructed NMR effective porosity logs are significantly superior in oil layer thickness division, fully proving that this resolution matching method is highly promising, laying a foundation for the precise characterization of sweet spot distribution and the efficient extraction of shale oil in the Permian Luocaogou formation in the Jimsar sag of the Junggar Basin. However, this method is limited when applied in strata with high pyrite content or small thickness.

Key words: Junggar Basin, Jimsar sag, Lucaogou formation, NMR effective porosity, Fourier transform, spectral amplitude, vertical resolution, LSBoost ensemble tree

中图分类号:

P631.8

杜雪彪, 张金风, 肖佃师, 冉阳, 刘英杰, 秦嘉敏, 王良哲. 基于ReliefF和LSBoost集成树核磁有效孔隙度频谱预测及分辨率匹配研究[J]. 新疆石油地质, 2025, 46(6): 762-772.

DU Xuebiao, ZHANG Jinfeng, XIAO Dianshi, RAN Yang, LIU Yingjie, QIN Jiamin, WANG Liangzhe. NMR Effective Porosity Spectrum Prediction and Resolution Matching Based on ReliefF and LSBoost Ensemble Tree[J]. Xinjiang Petroleum Geology, 2025, 46(6): 762-772.

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