An improved method for lithology identification based on a hidden Markov model and random forests
An improved method for lithology identification based on a hidden Markov model and random forests
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基于隐马尔可夫模型和随机森林的改进岩性识别方法
DOI：
10.1190/geo2020-0108.1
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发表时间：
2020-11
期刊：
影响因子：
3.3
通讯作者：
Dai Hengchang
中科院分区：
文献类型：
--
作者：
Wang Pu;Chen Xiaohong;Wang Benfeng;Li Jingye;Dai Hengchang
不同储层地下岩石物理性质往往存在差异，这影响了岩性识别，特别是对非常规储层。因此，地下储层的岩性识别是一项具有挑战性的任务。机器学习可以被视为利用现有数据进行岩性预测的有效方法。将隐马尔可夫模型与随机森林相结合，提出了一种新的岩性识别方法。隐马尔可夫模型提供了一个新的隐藏功能，从弹性参数，这是与无监督学习。由于弹性参数是由岩石物理性质决定的，因此隐藏的特征可以揭示岩石物理性质之间的内在联系，从而扩大样本空间。然后，利用新特征和弹性参数，采用随机森林法进行岩性识别。在预测框架中，隐马尔可夫模型的参数被更新，直到获得满意的隐藏特征。通过对合成资料和测井资料的分析，论证了该方法的优越性。野外地震资料应用进一步证明了该方法的有效性。数值结果表明，预测的岩性和泥质含量与真实的测井资料吻合较好。
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.
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