抄録
As oilfield development progresses into deeper and more complex formations, sand intrusion has become one of the primary challenges affecting well production stability and lifespan. Traditional sand control technologies often fall short in addressing complex well conditions, while introducing artificial intelligence (AI) provides a novel approach to optimizing oilfield sand control. This paper systematically outlines the key technical framework of AI in oilfield sand control, including machine learning-based historical data analysis, real-time optimization design, and multivariable dynamic simulation. By leveraging data-driven predictive models, AI can rapidly identify sand control technology combinations suited for different well conditions. Through dynamic simulation and real-time parameter updates, sand control strategies can continuously adapt to changing well conditions, enhancing the scientific basis and decision-making effectiveness. Combined with practical case analyses, this paper further explores the practical effectiveness of AI in sand control and the associated technical and management challenges, offering new perspectives and technical support for oilfield production management. The aim is to establish a scientific and systematic AI-driven framework for optimizing oilfield sand control technology, providing guidance and technical references for achieving intelligent and efficient oilfield production management and promoting sustainable industry development.
