深度神经网络对常规测井记录岩性分类的适用性:综合方法

Q1 Earth and Planetary Sciences
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引用次数: 0

摘要

通过传统方法,可以对具体的地层特征进行参数化理解。储层岩性属性对于油气勘探具有重要意义。测井是一种常规方法,与地球物理建模和岩石物理分析相比,由于成本效益高且解释时间合适,可用于有效预测岩性。然而,通过测井数据进行岩性识别的人工解释需要专业领域的知识和较长的测量时间。因此,本研究采用了深度神经网络(DNN)来实现测井数据岩性识别过程的自动化,从而提高岩性监测的时间效率。为预测地层岩性,开发了 DNN 模型,通过全面评估最佳参数和超参数(包括神经元数、层数、优化器、学习率、辍学值和激活函数),对模型进行了优化。通过将数据集划分为训练、验证和测试子域,利用不同的评价指标来检验模型的准确性。此外,还尝试消除地层岩性预测的拦截,同时利用类权重解决相关数据集在训练过程中的不平衡问题。据评估,准确率并不是评价岩性分类模型的唯一可靠指标。使用类权重的模型能识别所有类别,但准确率低,F1 分数也低,而基于 LSTM 的模型准确率高,F1 分数也高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

Parametric understanding for specifying formation characteristics can be perceived through conventional approaches. Significantly, attributes of reservoir lithology are practiced for hydrocarbon exploration. Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time. However, manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement. Therefore, in this study, Deep Neural Network (DNN) has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology. DNN model has been developed for predicting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons, number of layers, optimizer, learning rate, dropout values, and activation functions. Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training, validation and testing. Additionally, an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight. It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model. The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracy as well as a high F1-score.

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来源期刊
Petroleum Research
Petroleum Research Earth and Planetary Sciences-Geology
CiteScore
7.10
自引率
0.00%
发文量
90
审稿时长
35 weeks
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