Intelligent Drilling and Completion: A Review

Интеллектуальное бурение и заканчивание скважин: обзор
Gensheng Li, Xianzhi Song, Shouceng Tian, Zhaopeng Zhu
2022-08-30

artificial intelligencedigital twinsintelligent drilling and completionphysics-guided neural networksphysics-informed data fusion
The application of artificial intelligence (AI) has become inevitable in the petroleum industry. In drilling and completion engineering, AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency (DE). In recent years, numerous studies have focused on intelligent algorithms and their application. Advanced technologies, such as digital twins and physics-guided neural networks, are expected to play roles in drilling and completion engineering. However, many challenges remain to be addressed, such as the automatic processing of multi-source and multi-scale data. Additionally, in intelligent drilling and completion, methods for the fusion of data-driven and physics-based models, few-sample learning, uncertainty modeling, and the interpretability and transferability of intelligent algorithms are research frontiers. Based on intelligent application scenarios, this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future. This study aims to enhance the berthing of AI techniques in drilling and completion engineering.
1
AI is identified as a transformative technology for drilling and completion, with potential to reduce costs and substantially improve drilling efficiency.
2
Automatic processing of multi-source, multi-scale data remains a major unresolved challenge for intelligent drilling and completion.
3
Digital twins and physics-guided neural networks are highlighted as advanced technologies expected to support future drilling and completion applications.
4
Key research frontiers include integrating data-driven and physics-based models, few-sample learning, uncertainty modeling, and improving algorithm interpretability and transferability.
5
The review synthesizes current intelligent drilling and completion applications and outlines priorities for advancing AI adoption in the field.

intelligent drilling and completion engineering in the petroleum industry

AI application scenarios, methods, challenges, and future research directions for improving drilling efficiency

Publication Details
Publication Date
2022-08-30
Journal
Publisher
ISSN
Cited by
132
Access Type
Author Information
Authors
Gensheng Li
Xianzhi Song
Shouceng Tian
Zhaopeng Zhu
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat →
Make a presentation
100%