Facies Quality Zoning in Shale Gas by Deep Learning Method

IF 1.1 Q3 MINING & MINERAL PROCESSING
Y. A. Nezhad, A. Moradzadeh
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引用次数: 1

Abstract

One of the most essential factors involved in unconventional gas reserves for drilling and production is a suitable quality facies determination. The direct core and geochemical analyses are the most common methods used for studying this quality. Due to the lack of this data and the high cost, the researchers have recently resorted to the indirect methods that use the common data of the reservoir (including petro-physical logs and seismic data). One of the major problems in using these methods is that the complexities of these reproducible repositories cannot be accurately modeled. In this work, the quality of facies in shale gas is zoned using the deep learning technique. The applied method is long short-term memory (LSTM) neural network. In this scheme, the features required for zoning are automatically extracted and used to model the reservoir complexities properly. The results of this work show that zoning is done with an appropriate accuracy (86%) using the LSTM neural network, while it is 78% for a conventional intelligent MLP network. This specifies the superior accuracy of the deep learning method.
基于深度学习方法的页岩气相质量分区研究
确定适合钻井和生产的非常规天然气储量最重要的因素之一是确定合适的质量相。直接岩心法和地球化学分析是研究该质量最常用的方法。由于这些数据的缺乏和高成本,研究人员最近采用了间接方法,即使用储层的常用数据(包括岩石物理测井和地震数据)。使用这些方法的一个主要问题是,这些可重复存储库的复杂性无法精确建模。在这项工作中,使用深度学习技术对页岩气的相质量进行了分区。应用的方法是长短期记忆(LSTM)神经网络。在该方案中,自动提取分区所需的特征,并将其用于油藏复杂性建模。这项工作的结果表明,使用LSTM神经网络以适当的精度(86%)完成分区,而传统智能MLP网络的分区精度为78%。这说明了深度学习方法的优越准确性。
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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0
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