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

Abstract

Under the background of global energy transformation and environmental protection, the application of artificial intelligence technology has become an important trend in oil and gas field development industry. However, how to effectively utilize artificial intelligence technology to improve the efficiency and safety of oil and gas development, while addressing the environmental and economic issues it brings, is a major question that researchers need to consider. Based on the actual needs of oil and gas exploitation, the basic principles and methods of deep learning are studied, and the main models and training methods of deep learning are introduced. The basic process of oil and gas field development is described in detail, and the realization steps and principles of depth learning optimization model for oil and gas field development are studied. The main challenges of depth learning in oil and gas field development are studied, including data security, model complexity, computing resource demand and so on. The results show that as a powerful artificial intelligence tool, deep learning has great potential to improve the efficiency and security of oil and gas exploitation, but it still faces some challenges. Therefore, future research should pay more attention to these problems to promote the application of deep learning in oil and gas development.