利用电成像测井图像识别岩性:中国济阳凹陷案例研究

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Juan Liu , Xuanlin Min , Zhongli Qi , Jun Yi , Wei Zhou
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引用次数: 0

摘要

岩性识别在地层评价和地质分析中发挥着重要作用。传统的岩性识别方法是建立测井与岩性之间的关系模型。然而,测井并不总是足以识别岩性,因为有时不同岩性的测井曲线是相似的。最近,高分辨率的电成像测井图像(EILI)在测井解释中发挥着越来越重要的作用,因为 EILI 可以直观地反映岩性的特征。与传统的测井岩性识别方法不同,本文通过对 EILI 进行深度学习,提出了一种新颖的多维度岩性自动识别方法。首先,采用 Filtersim 算法填补 EILI 的空白带。然后,设计一个集成卷积神经网络(CNNs)模型,分别提取 EILI 的电阻率特征、纹理特征和整体特征。具体来说,集成卷积神经网络模型可实现对不同地质结构(块状、层状、片状)和岩性(泥岩、砂泥岩、石灰泥岩)的自动识别。最后,结合多维特征可实现岩性识别。在中国济阳凹陷页岩油藏的 EILI 试验中验证了所提出的综合模型的有效性。实验结果表明了集成 CNNs 方法在岩性识别方面的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lithology identification using electrical imaging logging image: A case study in Jiyang Depression, China
Lithology identification plays a significant role in stratigraphic evaluation and geological analysis. Traditional lithology identification method is by modeling the relationship between well logging and lithology. However, well logging are not always sufficient to identify lithology since sometimes the curves are similar for different lithologies. Recently, electrical imaging logging image (EILI) with high resolution plays an increasingly important role in logging interpretation since EILI can intuitively reflect the characteristics of lithology. Unlike traditional lithology identification method by using well logging, in this paper, we propose a novel multi-dimensional automatic lithology identification method by applying deep learning to EILI. First, Filtersim algorithm is employed to fill the blank strip of the EILI. Then, an integrated convolutional neural networks (CNNs) model is designed to extract the resistivity feature, texture feature, and holistic feature of the EILI, respectively. Specifically, the integrated CNNs model can realize automatic recognition for different geological structures (massive, bedded, lamellar) and lithology (mudstone, sand-mudstone, lime-mudstone). Finally, lithology identification can be achieved by combining with multi-dimensional features. The efficacy of proposed integrated model is validated experimentally on the EILI of shale oil reservoir in the Jiyang Depression of China. Experimental results show the effectiveness and superiority of the integrated CNNs method for lithology identification.
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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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