{"title":"基于机器学习的反褶积方法提供了感应测井数据的高分辨率快速反演","authors":"","doi":"10.30632/pjv64n2-2023a10","DOIUrl":null,"url":null,"abstract":"We built a deconvolution model for induction log data using machine learning (ML). Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, ML-based deconvolution finds accurate layer resistivity and layer boundaries. For a unit induction tool 2C40, the 21-point, 10-ft window deconvolution model works satisfactorily.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine-Learning-Based Deconvolution Method Provides High-Resolution Fast Inversion of Induction Log Data\",\"authors\":\"\",\"doi\":\"10.30632/pjv64n2-2023a10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We built a deconvolution model for induction log data using machine learning (ML). Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, ML-based deconvolution finds accurate layer resistivity and layer boundaries. For a unit induction tool 2C40, the 21-point, 10-ft window deconvolution model works satisfactorily.\",\"PeriodicalId\":170688,\"journal\":{\"name\":\"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30632/pjv64n2-2023a10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30632/pjv64n2-2023a10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine-Learning-Based Deconvolution Method Provides High-Resolution Fast Inversion of Induction Log Data
We built a deconvolution model for induction log data using machine learning (ML). Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, ML-based deconvolution finds accurate layer resistivity and layer boundaries. For a unit induction tool 2C40, the 21-point, 10-ft window deconvolution model works satisfactorily.
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