在较弱地质先验条件下建立钻孔岩性模型的部分域自适应方法

Jing Li;Jichen Wang;Zerui Li;Yu Kang;Wenjun Lv
{"title":"在较弱地质先验条件下建立钻孔岩性模型的部分域自适应方法","authors":"Jing Li;Jichen Wang;Zerui Li;Yu Kang;Wenjun Lv","doi":"10.1109/TAI.2024.3476434","DOIUrl":null,"url":null,"abstract":"Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells; 2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples; 3) developing a module, convolutional neural network with integrated channel attention mechanism (CG\n<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>\nCA), to serve as the backbone network for feature extraction; and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6645-6658"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Partial Domain Adaptation for Building Borehole Lithology Model Under Weaker Geological Prior\",\"authors\":\"Jing Li;Jichen Wang;Zerui Li;Yu Kang;Wenjun Lv\",\"doi\":\"10.1109/TAI.2024.3476434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells; 2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples; 3) developing a module, convolutional neural network with integrated channel attention mechanism (CG\\n<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>\\nCA), to serve as the backbone network for feature extraction; and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.\",\"PeriodicalId\":73305,\"journal\":{\"name\":\"IEEE transactions on artificial intelligence\",\"volume\":\"5 12\",\"pages\":\"6645-6658\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on artificial intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10707638/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10707638/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partial Domain Adaptation for Building Borehole Lithology Model Under Weaker Geological Prior
Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells; 2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples; 3) developing a module, convolutional neural network with integrated channel attention mechanism (CG ${}^{2}$ CA), to serve as the backbone network for feature extraction; and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信