多智能体协同技术在钻井参数动态优化与井下风险预警中的应用研究

Application Research of Multi-Agent Collaborative Technology in Dynamic Drilling Parameter Optimization and Downhole Risk Early Warning

摘要: 深层及非常规油气钻井中，参数优化与风险预警的传统串行处理模式存在优化过程不受安全约束、预警结果难以反控参数调整的结构性缺陷。本文引入多智能体协同技术，构建了适配钻井现场的五层协同架构，提出风险约束型参数动态优化策略，并建立井下多风险耦合预警与参数调控双向闭环机制。在此基础上，系统分析了多智能体相对于传统机器学习与数字孪生技术的定位与优势，从泛化能力、实时性、多任务协同、可解释性及工程落地性五个维度进行了对比分析。分析表明，多智能体协同技术可实现钻井参数动态优化与井下风险预警的双向联动，为破解提速与安全之间的动态平衡难题提供了可行架构。当前主要技术瓶颈在于井下数据传输实时性不足、高质量标注样本稀缺及边缘端算力受限。结合现场需求，提出了机理–数据融合驱动、大模型工具增强及数字孪生虚实联动等重点发展方向。

Abstract: In deep and unconventional oil and gas drilling, the conventional serial processing mode of parameter optimization and risk early warning has a structural defect that the optimization process is not constrained by safety thresholds and the warning results can hardly feed back to adjust drilling parameters in real time. To address this issue, this paper introduces multi-agent collaborative technology and constructs a five-layer collaborative architecture suitable for field drilling operations. A risk-constrained dynamic parameter optimization strategy is proposed, and a bidirectional closed-loop mechanism for coupled multi-risk early warning and parameter regulation is established. On this basis, the positioning and advantages of multi-agent systems relative to traditional machine learning and digital twin technologies are systematically analyzed, and a comparative analysis among the three approaches is conducted from five dimensions: generalization ability, real-time performance, multi-task collaboration, interpretability and engineering deployability. The analysis shows that multi-agent collaborative technology can realize the bidirectional linkage between dynamic parameter optimization and downhole risk early warning, providing a feasible architecture to resolve the dynamic trade-off between drilling acceleration and downhole safety. Current technical bottlenecks mainly lie in insufficient real-time data transmission, scarcity of high-quality labeled samples and limited edge-side computing power. Combined with field application requirements, future development directions including mechanism-data hybrid driving, large-model enhanced agents and digital twin-based virtual-real collaboration are proposed.