Subsurface petrophysical properties usually differ between different reservoirs, which affects lithology identification, especially for unconventional reservoirs. Thus, the lithology identification of subsurface reservoirs is a challenging task. Machine learning can be regarded as an effective method for using existing data for lithology prediction. By combining the hidden Markov model and random forests, we have adopted a novel method for lithology identification. The hidden Markov model provides a new hidden feature from elastic parameters, which is associated with unsupervised learning. Because elastic parameters are determined by petrophysical properties, the hidden feature may reveal an inner relationship of the petrophysical properties, which can expand the sample space. Then, with the new feature and the elastic parameters, the random forest method is adopted for lithology identification. In the prediction framework, the parameters of the hidden Markov model are updated until a satisfactory hidden feature is obtained. By analysis of synthetic and well-logging data, the superiority of the proposed method is demonstrated. Field seismic data application further proves the validity of the method. Numerical results show that the predicted lithology and shale content match well with real logging data.

中文翻译：

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一种基于隐马尔可夫模型和随机森林的岩性识别方法

不同储层之间的地下岩石物性通常不同，这会影响岩性识别，特别是对于非常规储层。因此，地下储层的岩性识别是一项艰巨的任务。机器学习可以被视为使用现有数据进行岩性预测的有效方法。通过结合隐马尔可夫模型和随机森林，我们采用了一种新的岩性识别方法。隐马尔可夫模型为弹性参数提供了新的隐匿特征，这与无监督学习相关。由于弹性参数是由岩石物性决定的，因此隐藏的特征可能会揭示岩石物性的内在联系，从而扩大样本空间。然后，借助新功能和弹性参数，采用随机森林法进行岩性识别。在预测框架中，更新隐藏马尔可夫模型的参数，直到获得令人满意的隐藏特征为止。通过对综合和测井数据的分析，证明了该方法的优越性。现场地震数据的应用进一步证明了该方法的有效性。数值结果表明，预测的岩性和页岩含量与实际测井数据吻合良好。