利用测井数据进行岩性识别的基于井眼聚类的方法

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-06-26 DOI:10.1007/s12145-024-01376-6
Hui Liu, XiaLin Zhang, ZhangLin Li, ZhengPing Weng, YunPeng Song
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引用次数: 0

摘要

近年来,地球科学家一直在利用机器学习技术,通过整合测井数据实现岩性识别自动化。然而,在地质复杂的地区,很少有人考虑到井眼之间的差异和岩性分布的不均匀性。此外,在解决这些问题时,根据地层序列区分同一地区井孔的工作也很有限。我们提出了一种基于机器学习的岩性自动识别工作流程。我们利用结构深度聚类网络(SDCN)算法进行深度聚类,以地质层为聚类尺度区分测井采样点,将每个采样点分配到相应的地层。为了获取每个钻孔的地层信息,我们设计了一个钻孔聚类结果处理层。通过分割测井数据窗口,我们提取了每个井眼的地层信息,并将井眼地层信息的独特性作为井眼分类的基础。随后,我们使用四种机器学习方法评估岩性分类对每个井眼类别的测井数据的影响,这四种方法分别是极梯度提升(XGBoost)、自适应提升(AdaBoost)、双向长短期记忆(Bi-LSTM)和双向门控递归单元(Bi-GRU)。实验结果表明,与未对钻孔进行分类的情况相比,大多数钻孔类别的岩性分类性能提高了 1%-6%。不过,也有一类钻孔的分类性能不够理想,原因是在电阻率测井中,古近纪地层中所含的辉绿岩变化很大。
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A borehole clustering based method for lithological identification using logging data

In recent years, geoscientists have been employing machine learning techniques to automate lithological identification by integrating well logging data. However, in geologically complex regions, few have taken into consideration the differences between boreholes and the uneven distribution of lithology. Additionally, there has been limited effort to differentiate boreholes in the same region based on stratigraphic sequences when addressing these issues. We propose a workflow for machine learning-based automated lithological identification. Utilizing the Structural Deep Clustering Network (SDCN) algorithm for deep clustering, we differentiate logging sampling points with geological strata as the clustering scale, assigning each sampling point to its corresponding stratum. In order to obtain stratum information for each borehole, we have devised a Borehole Cluster Result Processing Layer. By segmenting logging data windows, we extract stratum information for each borehole, using the distinctiveness of borehole stratum information as the basis for borehole classification. Subsequently, we assess the impact of lithological classification on logging data for each borehole category using four machine learning methods: extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bidirectional long short-term memory (Bi-LSTM), and bidirectional gated recurrent unit (Bi-GRU). The experimental results indicate that, compared to the case where boreholes are not classified, the lithological classification performance for the majority of borehole categories has improved by 1% to 6%. However, there is also a category of boreholes where the classification performance is less than ideal due to the significant variability of diabase contained within the Paleogene strata in the electrical resistivity logging.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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