新疆大学地质与矿业工程学院新疆中亚造山带大陆动力学与成矿预测自治区重点实验室
纸质出版:2025
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[1]黄勇波,韩长城,魏亚涛,等.基于小波变换的卷积神经网络岩相预测[J].新疆大学学报(自然科学版中英文),2025,42(03):300-311.
[1]黄勇波,韩长城,魏亚涛,等.基于小波变换的卷积神经网络岩相预测[J].新疆大学学报(自然科学版中英文),2025,42(03):300-311. DOI: 10.13568/j.cnki.651094.651316.2024.12.13.0001.
DOI:10.13568/j.cnki.651094.651316.2024.12.13.0001.
岩相分析是寻找优质储层的基础,但对于无井区域或受限于井间复杂的地质条件,传统技术难以快速、准确识别岩相类型及其空间展布.故通过深度学习实现对岩相的高效识别,提出一种加入连续小波变换(CWT)的卷积神经网络(CNN)岩相识别方法.将该方法应用于准噶尔盆地征沙村地区克拉玛依组,主要步骤包括:依据岩心和测井特征划分典型岩相,基于合成记录的井震匹配对测井岩相与叠后地震资料进行匹配,利用Morlet小波变换将匹配的地震波转化为时频谱图,生成不同岩相的时频谱图数据集,并构建CNN模型进行训练、测试与验证.在层位约束条件下,研究不同岩相的平面展布.结果表明:Morlet小波结合CNN的模型可实现较高识别精度,X2盲井的4种岩相识别率均超过85%,显著提升了岩相识别的效率和精度.
Lithofacies analysis serves as the foundation for identifying high-quality reservoirs. However
in areas devoid of well data or constrained by complex inter-well geological conditions
traditional techniques struggle to rapidly and accurately recognize lithofacies types and their spatial distribution. This paper proposes a convolutional neural network(CNN)-based lithofacies identification method integrated with continuous wavelet transform(CWT)
achieving efficient lithofacies recognition through deep learning. Applied to the Karamay formation in the Zhengshacun area of the Junggar Basin
the methodology involves: classifying typical lithofacies based on core and logging characteristics
performing synthetic record-based well-to-seismic matching to align logging lithofacies with post-stack seismic data
converting the matched seismic waveforms into time-frequency spectrum maps using Morlet wavelet transform
generating a time-frequency spectrum dataset for different lithofacies
and constructing and training a CNN model for validation. Under horizon constraints
the planar distribution of various lithofacies is investigated. Results demonstrate that the Morlet-CNN model achieves high identification accuracy
with recognition rates exceeding 85% for 4 lithofacies types in blind well X2
significantly enhancing both the efficiency and accuracy of lithofacies identification.
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