Establishment of Technical Standard Database for Surface Engineering Construction of Oil and Gas Field

Abstract

In recent years, oil and gas field surface engineering construction projects tend to be large in scale, large in quantity, and short in cycle. The task of surface construction management has increased significantly. In the process of project construction, corresponding standards and specifications are required to provide sufficient technical guidance and support for design, construction, and management personnel to ensure project management and control towards compliance, safety, and quality. However, the oil and gas field engineering standards are numerous and specialized, involving different levels of national standards, enterprise standards, and industry standards, which leads to the inefficiency of the actual use of standards and specifications. To solve them, this paper uses knowledge graph technology, OCR recognition, and natural language processing technology to conduct systematic research on the knowledge classification mechanism, data extraction, database construction mechanism, data structuring, and intelligent retrieval matching of oil-gas field surface engineering construction standards. In this study, the structured identification, storage, and information warehousing of standards are realized, and a highly sharable library of standards and specifications is formed, which realizes the intelligent retrieval and pushing of technical standards for surface engineering construction. This paper creates conditions for the realization of intelligent push and benchmarking management of standards and specifications, providing support for digital transformation and intelligent development of oil–gas fields.

Applications and Challenges of Deep Learning in Oil and Gas Field Development

“…Through the analysis of production data, the real-time monitoring and prediction of key parameters such as production and pressure can be realized, which can provide decision support for optimization adjustment of oil and gas wells [10,11]. At the same time, depth learning can also be applied to the design and optimization of oil and gas field development plans.…”