@article{LIU2026, 
author = {Xuquan LIU and Meng CUI and Yan DING and Yanlong ZHANG and Guang YANG and Yi CUI and Yang YU},
title = {Research status and future prospects of intelligent drilling and completion technology},
year = {2026},
journal = {Petroleum Science Bulletin},
volume = {11},
number = {1},
pages = {164-178},
keywords = {closed-loop, intelligent drilling, intelligent drilling and completion technology, intelligent cementing, cooperative large and small models},
url = {https://www.sciopen.com/article/10.3969/j.issn.2096-1693.2026.03.001},
doi = {10.3969/j.issn.2096-1693.2026.03.001},
abstract = {The ongoing Fourth Industrial Revolution, characterized by artificial intelligence (AI), is driving a wave of intelligent transformation across the oil and gas industry. International oilfield service and operating companies in regions such as the United States, Norway, and the Middle East are investing heavily in digital and intelligent transformation to secure a competitive advantage in the future landscape. As a cutting-edge technology in oil and gas engineering, intelligent drilling and completion is poised to yield disruptive and leapfrog innovations, empowering the development of new quality productive forces. This paper elaborates on the conceptual framework of intelligent drilling and completion technologies and provides a comprehensive review of the current domestic and international development status and technical disparities across five key areas: surface equipment, measurement-transmission-conduction tools, drilling fluids, cementing, and software. It further identifies four major existing problems and four key challenges. Furthermore, it proposes the “1244” development direction: focusing on one goal—empowering technological innovation and industrial upgrading through “Drilling and Completion + AI”; concentrating on two major fields—intelligent drilling and intelligent cementing; tackling four key technological directions—equipment, tools, fluids, and software; and realizing four typical application scenarios—fully automated wellsite operations, autonomous drilling control, intelligent drilling fluid regulation, and intelligent cementing operations. Simultaneously, the article advocates building a technical system with the geology-engineering knowledge base as the foundation, large/small AI models as the backend support, and “Drilling and Completion + AI” as the front-end application carrier. This aims to accelerate the transition of intelligent drilling and completion technology from unit-level closed-loop to global closed-loop and remote-controlled operations.}
}