Design of the Intelligent Agent System for Drilling Equivalent Density Analysis Based on the Kunlun Large Model

In drilling engineering, the accuracy of downhole pressure control directly affects the safety and efficiency of operations. Traditional equivalent density analysis methods rely on professional software, and there are problems such as complex operation and lagging response. This paper proposes an intelligent agent system for drilling equivalent density analysis based on the Kunlun large model. Through the combination of natural language interaction and a microservice-based calculation engine, the full-process automation of “parameter input - model call - result visualization” is achieved. The system adopts a three-layer architecture: (1) The interaction layer parses unstructured input through the large model; (2) The calculation layer encapsulates the hydraulics algorithm as a microservice; (3) The data layer integrates real-time wellbore data (EISC). Experiments show that the system significantly simplifies the traditional analysis process, and the calculation results are highly consistent with industry standards. This study provides a reusable technical paradigm for the intelligentization of drilling engineering, significantly reduces the professional threshold, and improves decision-making efficiency.