利用基于起始模块的卷积神经网络进行双套管井成像

Siqi Zhang, Zhoumo Zeng, Xiaocen Wang, Shili Chen, Yang Liu
{"title":"利用基于起始模块的卷积神经网络进行双套管井成像","authors":"Siqi Zhang, Zhoumo Zeng, Xiaocen Wang, Shili Chen, Yang Liu","doi":"10.1063/5.0191452","DOIUrl":null,"url":null,"abstract":"The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"26 13","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Imaging in double-casing wells with convolutional neural network based on inception module\",\"authors\":\"Siqi Zhang, Zhoumo Zeng, Xiaocen Wang, Shili Chen, Yang Liu\",\"doi\":\"10.1063/5.0191452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.\",\"PeriodicalId\":502250,\"journal\":{\"name\":\"APL Machine Learning\",\"volume\":\"26 13\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"APL Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0191452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"APL Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/5.0191452","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

在整个油井开发和生产过程中,评估双套管井的油井完整性对于确保油井稳定性、防止油气泄漏、避免污染以及确保安全至关重要。然而,目前主流的固井质量评估方法主要针对单套管井,针对双套管井的工作十分有限。本研究介绍了一种使用卷积神经网络 Inception 模块评估固井质量的新方法。首先,采用有限差分法生成与各种模型配置相对应的井眼声波数据,并利用这些数据训练神经网络,该网络从井眼声波数据中学习空间特征,从而重建慢度模型。通过调整网络结构和参数,发现在全连接层中有两个区块和 4096 个节点的神经网络具有最佳成像效果和较强的抗噪能力。利用实际井筒尺寸模型对所提出的方法进行了验证,结果表明效果极佳,为评估双套管井的井筒完整性提供了更有效的方法。此外,利用偶极声波测井数据对双套管井中的压缩(P)波和剪切(S)波进行慢度模型成像,以验证固井质量评价的可行性。所开发的方法有助于油气行业更准确地评估井筒完整性,从而改善安全和环境状况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Imaging in double-casing wells with convolutional neural network based on inception module
The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.
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
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