{"title":"基于机器学习的感应日志快速正演建模卷积方法","authors":"","doi":"10.30632/pjv64n2-2023a11","DOIUrl":null,"url":null,"abstract":"We built a convolution model using machine learning (ML) to calculate induction log responses for one-dimensional (1D) earth models. Compared to analytical forward modeling, the convolution model is extremely fast. ML-based convolution finds accurate induction tool responses from an earth model with layer resistivity and layer boundaries. For a unit induction tool 2C40, the 101-point, 50-ft window convolution model works satisfactorily for a well deviation angle of 60.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"35 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 Convolution Method for Fast Forward Modeling of Induction Log\",\"authors\":\"\",\"doi\":\"10.30632/pjv64n2-2023a11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We built a convolution model using machine learning (ML) to calculate induction log responses for one-dimensional (1D) earth models. Compared to analytical forward modeling, the convolution model is extremely fast. ML-based convolution finds accurate induction tool responses from an earth model with layer resistivity and layer boundaries. For a unit induction tool 2C40, the 101-point, 50-ft window convolution model works satisfactorily for a well deviation angle of 60.\",\"PeriodicalId\":170688,\"journal\":{\"name\":\"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description\",\"volume\":\"35 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/pjv64n2-2023a11\",\"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/pjv64n2-2023a11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine-Learning-Based Convolution Method for Fast Forward Modeling of Induction Log
We built a convolution model using machine learning (ML) to calculate induction log responses for one-dimensional (1D) earth models. Compared to analytical forward modeling, the convolution model is extremely fast. ML-based convolution finds accurate induction tool responses from an earth model with layer resistivity and layer boundaries. For a unit induction tool 2C40, the 101-point, 50-ft window convolution model works satisfactorily for a well deviation angle of 60.