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A lithological sequence classification method with well log via SVM-assisted bi-directional GRU-CRF neural network
Journal of Petroleum Science and Engineering ( IF 5.168 ) Pub Date : 2021-05-06 , DOI: 10.1016/j.petrol.2021.108913
Zhege Liu , Junxing Cao , Jiachun You , Shuna Chen , Yujia Lu , Peng Zhou

When available core samples are limited, logging data becomes important in the lithology classification. For different lithologies, the distribution of logging data usually overlap each other, which increases the solution multiplicity of a single spatial point. The modeling of lithologic sequences depending on the vertical spatial relationship can reduce this multiplicity. To improve the modeling ability of sequences, we propose a lithological sequence classification algorithm modeled by bi-directional Gated Recurrent Units and Conditional Random Field layer (Bi-GRU-CRF) referring the proposed Artificial Neural Networks and Hidden Markov Models (ANN-HMM) hybrid framework. However, due to the limited training data and the difference of lithologic sequences, unlike the point-based algorithms, the generalization performance of the sequence-based algorithms can be significantly reduced. For this problem, we concatenate the probability output vector of Support Vector Machine (SVM) with original data as input to Bi-GRU-CRF, and the overall structure is named SVM + Bi-GRU-CRF. In the cross-validation with field data, whenever the sequences of training and test dataset are similar or dissimilar, SVM + Bi-GRU-CRF can generally achieve the best results comparing with all point-based and other sequence-based algorithms. Furthermore, the applicable conditions of this algorithm are discussed in three aspects: the relationship between the algorithm and data, the function of each module, and the influence of step size parameters. This work is progressed in three designed experiments with two groups of comparative synthetic datasets, which are generated with Gaussian Mixture Models (GMMs). Finally, a convictive and comprehensive evaluation of SVM + Bi-GRU-CRF is given out.



中文翻译:

SVM辅助双向GRU-CRF神经网络的测井岩性序列分类方法

当可用的岩心样品受到限制时,测井数据在岩性分类中变得很重要。对于不同的岩性,测井数据的分布通常彼此重叠,这增加了单个空间点的解多重性。根据垂直空间关系对岩性序列进行建模可以减少这种多样性。为了提高序列的建模能力,我们提出了一种用双向门控递归单元和条件随机场层(Bi-GRU-CRF)建模的岩性序列分类算法,并参考了拟议的人工神经网络和隐马尔可夫模型(ANN-HMM)混合框架。但是,由于训练数据有限且岩性序列不同,因此与基于点的算法不同,基于序列的算法的泛化性能会大大降低。针对此问题,我们将支持向量机(SVM)的概率输出向量与原始数据作为Bi-GRU-CRF的输入进行连接,整体结构称为SVM + Bi-GRU-CRF。在与现场数据的交叉验证中,只要训练和测试数据集的序列相似或不相似,与所有基于点的算法和其他基于序列的算法相比,SVM + Bi-GRU-CRF通常都能获得最佳结果。此外,从三个方面讨论了该算法的适用条件:算法与数据之间的关系,每个模块的功能以及步长参数的影响。这项工作是在三组设计的实验中进行的,使用了两组比较的合成数据集,用高斯混合模型(GMM)生成。最后,对SVM + Bi-GRU-CRF进行了定性和全面的评估。

更新日期:2021-05-13
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