Wireline logging plays a critical role in coalbed methane exploration. However, the lack of crucial log data, such as neutron and sonic logs, makes coalbed methane exploration difficult. In this paper, we propose a principal component regression model incorporating a multiscale wavelet analysis, a histogram calibration, a principal component analysis, and a multivariate regression to reconstruct essential neutron and sonic logs from conventional logs (i.e., density, resistivity, gamma ray, spontaneous potential, and caliper logs). Our proposed model does not need core-related correlation, and there is no local optimization. We have applied the model to evaluate coalbed methane content in a real case. Firstly, we use the multiscale wavelet analysis and histogram calibration to improve logs’ reliability and lateral comparability. Then, we apply principal component analysis to transform the well-correlated wireline logs into linearly independent components and regress reconstruction functions for neutron and sonic logs with multivariate regression. The reconstructed logs are like the measured logs in trend, mean, and scale. Finally, we apply the reconstructed neutron logs to predict the coalbed methane-content distribution. The predicted distribution is not only following the regional distribution characteristics of coalbed methane enrichment zones but also validated by the coalbed methane production data. In summary, the successful applications of wireline-log reconstruction and regional coalbed methane-content prediction have demonstrated the reliability of the proposed principal component regression model.

中文翻译：

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用主成分回归模型重建临界煤层气测井：一个案例研究

电缆测井在煤层气勘探中起着至关重要的作用。然而，缺乏关键的测井数据，如中子和声波测井数据，使得煤层气勘探变得困难。在本文中，我们提出了一种主成分回归模型，该模型结合了多尺度小波分析、直方图校准、主成分分析和多元回归，以从常规测井（即密度、电阻率、伽马射线、自发电位和卡尺记录）。我们提出的模型不需要与核心相关的相关性，也没有局部优化。我们已应用该模型来评估实际案例中的煤层气含量。首先，我们使用多尺度小波分析和直方图校准来提高测井的可靠性和横向可比性。然后，我们应用主成分分析将相关的电缆测井转换为线性无关的分量，并使用多元回归回归重建中子和声波测井函数。重建的日志在趋势、均值和尺度上类似于测量日志。最后，我们应用重建的中子测井来预测煤层气含量分布。预测分布不仅符合煤层气富集区的区域分布特征，而且得到了煤层气产量数据的验证。总之，电缆测井重建和区域煤层气含量预测的成功应用证明了所提出的主成分回归模型的可靠性。