Hui Liu, XiaLin Zhang, ZhangLin Li, ZhengPing Weng, YunPeng Song
{"title":"利用测井数据进行岩性识别的基于井眼聚类的方法","authors":"Hui Liu, XiaLin Zhang, ZhangLin Li, ZhengPing Weng, YunPeng Song","doi":"10.1007/s12145-024-01376-6","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"33 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A borehole clustering based method for lithological identification using logging data\",\"authors\":\"Hui Liu, XiaLin Zhang, ZhangLin Li, ZhengPing Weng, YunPeng Song\",\"doi\":\"10.1007/s12145-024-01376-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01376-6\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01376-6","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.