利用模糊粗糙法选择地热储层热量提取钻井技术的多标准洞察模型

IF 8.1 1区 计算机科学 N/A COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-08-22 DOI:10.1016/j.ins.2024.121353
{"title":"利用模糊粗糙法选择地热储层热量提取钻井技术的多标准洞察模型","authors":"","doi":"10.1016/j.ins.2024.121353","DOIUrl":null,"url":null,"abstract":"<div><p>Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (<span><math><mi>T</mi><mo>−</mo><mi>SHFR</mi></math></span>) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel <span><math><mi>T</mi><mo>−</mo><mi>SHFR</mi></math></span> concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach\",\"authors\":\"\",\"doi\":\"10.1016/j.ins.2024.121353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (<span><math><mi>T</mi><mo>−</mo><mi>SHFR</mi></math></span>) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel <span><math><mi>T</mi><mo>−</mo><mi>SHFR</mi></math></span> concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.</p></div>\",\"PeriodicalId\":51063,\"journal\":{\"name\":\"Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0020025524012672\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"N/A\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025524012672","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"N/A","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

地热能作为一种特殊的可再生资源在发电领域脱颖而出,它可以提供稳定的电力生产,而不会出现间歇性问题。尽管地热具有按需稳定供电的潜力,但由于成本高昂,地热的应用受到了阻碍。采用最有效的钻探方法可以提高效率,降低运营成本,从而缓解这一难题。本研究的主要目标是确定从地热储层中提取热量的最佳钻探方法。这种优化方法有助于更好地利用地热储层,从而提高热回收率,改善项目可行性。由于存在不确定性,传统方法往往无法评估最佳钻探替代方案。为解决这一问题,我们的研究引入了一种创新范式,该范式整合了新颖的 T-Spherical Hesitant Fuzzy Rough (T-SHFR) 集、用几何平均数消除标准影响的方法以及用标准权重对替代方案进行排序的混合多重标准决策(MCDM)技术。通过利用新颖的 T-SHFR 概念,我们的方法可以对各种因素进行综合评估。这种整体评估确保了对决策环境的全面了解。研究结果表明,储层特征在选择可持续钻井方案中起着重要作用。此外,定向钻井似乎是最有前途的方法,能量产出更高,其次是细孔钻井。通过敏感性分析和比较分析,确定了这些结论的稳健性和可信度,表明这种 MCDM 方法可能适用于不同环境下的类似挑战。使用斯皮尔曼等级相关系数对排序技术的研究结果进行了验证,结果显示存在显著的正相关关系。这项研究将使利益相关者能够做出明智的决定,从而提高地热能源项目的整体效率和可持续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach

Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (TSHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel TSHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.

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
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
期刊最新文献
Ex-RL: Experience-based reinforcement learning Editorial Board Joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering RT-DIFTWD: A novel data-driven intuitionistic fuzzy three-way decision model with regret theory Granular correlation-based label-specific feature augmentation for multi-label classification
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1