基于深度学习方法的海相页岩岩相分类

IF 2.1 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Yufang Xue , Bing Luo , Yalin Chen , Jun Qin , Lanpu Chen , Yuhao Yi
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引用次数: 0

摘要

页岩岩相控制着生烃潜力、储层物性和各向异性,从而确定页岩“甜点”的分布,指导水平井靶窗的选择。因此,页岩岩相预测对于页岩储层的精细评价、高效勘探和开发至关重要。然而,川东红星地区二叠系吴家坪组岩相预测由于矿物组成复杂,非均质性强,面临较大挑战。根据总有机碳含量和矿物组合特征,将储层内页岩及其夹层划分为5个岩相,并将mh4确定为有利岩相。此外,选择6条测井曲线(GR、AC、DEN、CNL、CAL和LLD)作为输入特征,训练和测试用于自动岩相预测的深度学习模型。为了研究深度学习模型在测井岩相识别中的适用性,采用了CNN、CNN- lstm、CNN- bilstm、CNN- gru、CNN- bigru和Transformer 6种模型。值得注意的是,Transformer模型在页岩岩相识别方面的精度为0.8776,精度为0.9152。其中,对于有利岩相f4, Transformer模型预测效果最好,F1−得分为0.89。该研究表明,深度学习模型可以有效地从常规测井资料中识别页岩岩相,为开发识别岩相的实用方法提供了有价值的技术见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven lithofacies classification of marine shale based on deep learning approaches
Shale lithofacies control hydrocarbon generation potential, reservoir properties, and anisotropy, thereby determining the distribution of shale “sweet spots” and guiding the selection of horizontal well target windows. Consequently, predicting shale lithofacies is crucial for the detailed evaluation, efficient exploration, and development of shale reservoirs. However, predicting lithofacies within the Wujiaping Formation of the Permian in the Hongxing area, eastern Sichuan Basin, presents significant challenges due to complex mineral compositions and strong heterogeneity. In this study, we classified the shale and its interlayers within the reservoir into five lithofacies based on total organic carbon content and mineral assemblages, with MF4 identified as the favorable lithofacies. Additionally, six logging curves (GR, AC, DEN, CNL, CAL, and LLD) were selected as input features to train and test deep learning models for automatic lithofacies prediction. To investigate the applicability of deep learning models for lithofacies identifications using well logs, six models were employed, including CNN, CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, and Transformer. Notably, the Transformer model outperformed the others, achieving an Accuracy of 0.8776 and Precision of 0.9152 in shale lithofacies identification. Specifically, for the favorable lithofacies MF4, the Transformer model yielded the highest prediction performance, with F1score of 0.89. This study demonstrates that deep learning models can effectively identify the shale lithofacies from conventional well logs, providing valuable technical insights for developing practical approaches to identify lithofacies.
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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