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Review of Applications of Artificial Intelligence and Drones in Oil Pollution in Seawater

Science, PUCC, Pondicherry University, Pondicherry, India.
2 Department of Computer Science, Pondicherry University, Pondicherry, India.
DOI: 10.4236/jcc.2025.134002 PDF
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Abstract
An ancient fossil fuel, oil is a crucial energy source for various daily activities, such as electricity generation and vehicle operation. However, its ship transportation poses a significant threat to the marine ecosystem. Oil spills into seawater, harming sea creatures and endangering human life in the event of an accident. The frequency of such oil pollution inci

原文链接：https://www.scirp.org/JOURNAL/paperinformation?paperid=141987

Review of artificial intelligence applications in geothermal energy extraction from hot dry rock

nergy extraction due to their abundant reserves. However, exploitation of the resources is fraught with complexity and technical challenges arising from their unique characteristics of high temperature, high strength, and low permeability. With the continuous advancement of artificial intelligence (AI) technology, intelligent algorithms such as machine learning and evolutionary algorithms are gradually replacing or assisting traditional research methods, providing new solutions for HDR geothermal resource exploitation. This study first provides an overview of HDR geothermal resource exploitati

原文链接：https://journal.hep.com.cn/duse/EN/10.1002/dug2.70018

燃气安全有了“AI大脑”！青岛这一生产大模型入选全国人工智能应用典型案例

型技术，建成统一的企业专属智能知识库，归集管理制度、操作规范、运维案例等核心资产。一线员工通过语音或文字即可实时查询，将原本半小时的
慧公用事业方向，持续深化AI技术与主业融合，加速由传统燃气保供运营商向AI赋能的美好生活保障者迭代升级，以数字科技守护城市燃气安全生命线，赋
能城市高质量发展，彰显国企担当与民生温度。”青岛能源集团泰能天然气有限公司副总经理、安全总监李华章说。（青岛日报/观海新闻记者王冰洁）
本文作者：掌上青岛

原文链接：http://m.toutiao.com/group/7658117583123595816/?upstream_biz=VolcEngine

A critical review of AI applications in geothermal energy: From reservoir exploration to power generation | Semantic Scholar

DOI: 10.1016/j.rser.2026.117396
Corpus ID: 291780327
A critical review of AI applications in geothermal energy: From reservoir exploration to power generation
Osama Baig, Jun Wang, +8 authors Wen-Jie Qin
Published in Renewable & Sustainable…
Environmental Science, Engineering
158 References
158 References
Artificial intelligence empowering geothermal energy development: A full-lifecycle review from exploration to operation
Shan Kun Liang-Han Cong Zi-Wang Yu Xiao-Qi Ye
Environmental Science, Engineering
Renewable & Sustainable Energy Reviews
2026
Graph neural network for multi-physics geothe

原文链接：https://www.semanticscholar.org/paper/A-critical-review-of-AI-applications-in-geothermal-Baig-Wang/589fa838ba161661c92530e8b6be9dc6e208f586

采煤工人“黑领”变“白领” 大模型赋能产业迭代升级

合拳，重新定义生产逻辑，推动产业往更高效、更安全、更新的方向升级迭代。最近，在全国范围的寒潮影响下，天然气供暖需求短时间内激增。在江苏镇江，我国首座盐穴型地下储气库——金坛储气库里，国家管网集团的工作
方案的高效生成，得益于国家管网大模型系统的支撑。系统从管网3800余个核心业务环节中，筛选出约300个可实现“AI替代人工”的关键环节，形成“智能感知—精准决策—高效调度—自主执行”的完整闭环。大模型带来的好处，
本文作者：央广网

原文链接：http://m.toutiao.com/group/7589912058217382463/?upstream_biz=VolcEngine

管网工业世界模型获评2026世界人工智能大会“SAIL之星”_国家石油天然气管网集团有限公司

、主动预演”升级。
该模型已在国家管网集团油气管网生产运行部分场景率先开展落地验证并取得成效。其中，“天然气流体仿真场景”可实现对管网未来运行趋势的快速推演，预测平均精度达98%；“管道智能巡护场景”融合多
可在线推演运行态势并自动优化防腐参数，相较传统人工调节效率大幅提升。
下一步，国家管网集团将大力实施“AI优先”战略，迭代优化管网工业世界模型，扩展管输全业务场景人工智能融合应用，携手产学研各方共同推动能

原文链接：https://www.pipechina.com.cn/front/news/3005686.html

Enhancing energy efficiency and sustainability in offshore drilling through real-time multi-objective optimization: Considering lag effects and formation variability

Enhancing energy efficiency and sustainability in offshore drilling through real-time multi-objective optimization: Considering lag effects and formation variability
Author
Listed:
Song, Yu
Song, Zehua
Yang, Jin
Wei, Longgui
Tang, Jizhou
Registered:
Abstract
Advancements in offshore drilling demand AI-driven solutions to handle unpredictable geological conditions and complex subsurface environments, ensuring reliability and sustainability. Traditional methods relying on historical well data lack accuracy and adaptability, while logging-while-drilling (LWD) causes delays that hinder timely deci

原文链接：https://ideas.repec.org/a/eee/reensy/v261y2025ics0951832025003394.html