石油与天然气化工2025，Vol.54Issue(5)：1-16,16.DOI:10.3969/j.issn.1007-3426.2025.05.001
智能钻井液研究现状与展望:智能响应材料与算法
Current research status and future prospects of intelligent drilling fluids:intelligent responsive materials and algorithms
摘要
Abstract
The advancement of oil and gas exploration into complex environments such as deepreservoir,deepwater,and unconventional reservoirs has imposed significant challenges on traditional drilling fluids.The traditional drilling fluid performance in deep complex geologic environment cannot meet the stringent requirements of modern drilling technology.Intelligent-responsive materials,capable of adaptively modulating their properties in response to environmental stimuli,have emerged as pivotal technologies for enhancing drilling fluid performance.Concurrently,machine learning,as a powerful data-driven approach,has gained widespread application in material design and performance prediction,offering novel insights for optimizing intelligent-responsive materials.The integration of machine learning techniques into the study of intelligent-responsive drilling fluid materials not only expedites material screening and performance enhancement but also fosters the intelligent evolution of drilling.This advancement significantly boosts the efficiency and safety of oil and gas drilling operations,holding substantial theoretical value and broad application prospects.This paper systematically summarizes the classification and functional characteristics of intelligent responsive drilling fluid materials,with a focus on analyzing the structural composition and response mechanism of intelligent materials.It encompasses mainstream machine-learning algorithms such as artificial neural networks,support vector machines,and random forests,delineating their respective advantages.Through a comprehensive evaluation of existing research,the paper identifies critical challenges in the design of intelligent-responsive materials,including data insufficiency,limited generalizability of models,and challenges in experimental validation.Furthermore,it examines the potential of deep learning and multimodal data fusion technologies to enhance model accuracy and interpretability,underscoring the necessity of interdisciplinary collaboration in advancing the intelligent development of drilling fluids.In response to practical application requirements,the paper proposes future research directions for the integration of intelligent-responsive materials with machine-learning technologies,thereby providing a robust theoretical foundation and technical guidance for achieving efficient,safe,and environmentally sustainable oil and gas drilling operations.关键词
智能钻井液/智能响应材料/智能设计算法/机器学习/钻井液智能化Key words
intelligent drilling fluid/intelligent-responsive materials/intelligent design algorithm/machine learning/drilling fluid intelligence引用本文复制引用
孙金声,薛乐,廖波,吕馨頔,王金堂,吕开河,王建华,王建龙,闫丽丽..智能钻井液研究现状与展望:智能响应材料与算法[J].石油与天然气化工,2025,54(5):1-16,16.基金项目
新型油气勘探开发国家科技重大专项课题"高效能井筒工作液"(2025ZD1401307) （2025ZD1401307）
中国博士后科学基金资助项目"超深特深层钻井液水活度响应型降滤失剂研制及作用机制研究"(2024M763644) （2024M763644）
国家资助博士后研究人员计划"万米深井钻井液与超高温地层热交换对井壁失稳的影响机制研究"(GZC20251945) （GZC20251945）
山东省重点研发计划"渤海湾盆地东营凹陷沙河街组页岩油富集机理与储层保护技术研究"(2024CXPT076) （2024CXPT076）
