Construction Method and Practical Application of Oil and Gas Field Surface Engineering Case Database Based on Knowledge Graph

• Published in: Processes Vol. 12; no. 6; p. 1088
• Main Authors: Xia, Taiwu, Dai, Zhixiang, Zhang, Yihua, Wang, Feng, Zhang, Wei, Xu, Li, Zhou, Dan, Zhou, Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Artificial intelligence; Carbon; Computational linguistics; Data processing; Decision making; Decomposition; Efficiency; Energy industry; Engineering; Gas fields; Gas industry; Graphs; Industrial project management; Information management; Information technology; Knowledge representation; Language processing; Methods; Mines; Natural gas; Natural language interfaces; Natural language processing; Oil and gas industry; Optical character recognition; Petroleum industry; Program development software; Project management; Trends; China
• DOI: 10.3390/pr12061088
• ISSN: 2227-9717

To address the challenge of quickly and efficiently accessing relevant management experience for a wide range of ground engineering construction projects, supporting project management with information technology is crucial. This includes the establishment of a case database and an application platform for intelligent search and recommendations. The article leverages Optical Character Recognition (OCR) technology, knowledge graph technology, and Natural Language Processing (NLP) technology. It explores the mechanisms for classifying construction cases, methods for constructing a case database, structuring case data, intelligently retrieving and matching cases, and intelligent recommendation methods. This research forms a complete, feasible, and scalable method for deconstructing, storing, intelligently retrieving, and recommending construction cases, providing a theoretical basis for the establishment of a construction case database. It aims to meet the needs of digital project management and intelligent decision-making support in the oil and gas sector, thereby enhancing the efficiency and accuracy of project construction. This work offers a theoretical foundation for the development of an intelligent management platform for ground engineering projects in the oil and gas industry, supporting the sector’s digital transformation and intelligent development.