基于大语言模型的钻井智能系统构建技术研究

Research on Construction Technology of Intelligent Drilling Systems Based on Large Language Models

摘要: 近年来，随着大语言模型(后文简称“大模型”)的出现，为解决石油钻井领域的复杂问题提供了技术基础。然而，现有AI (如DeepSeek)存在着多模态能力缺失、功能模块隔离和知识失效性边界、文件长度限制、文件格式与内存兼容性等问题，其作用多停留在通用任务优化，难以精准响应钻井工程设计、故障诊断等专业问题。针对通用开源大语言模型现存的专业术语理解偏差、行业知识融合不足导致场景适配性差等问题，为实现大模型与钻井专业知识的深度耦合，本文基于Python语言和MaxKB等开源平台，创新采用“钻井智能体–工作流”技术体系，构建了类ChatGPT的石油钻井业内智能系统(DrillingGPT)，也即钻井智能体，有效提升了大模型在钻井专业问答、方案生成等任务中的准确率与逻辑合规性，旨在为通用大模型向垂直工程领域的行业落地提供方法思路与技术支持。

Abstract: In recent years, the emergence of Large Language Model (LLM, hereinafter referred to as “large model”) has provided a technological foundation for addressing complex problems in the field of petroleum drilling. However, existing AI solutions (such as DeepSeek) face challenges such as a lack of multimodal capabilities, isolated functional modules, knowledge recency boundaries, file length limitations, and file format and memory compatibility issues. Consequently, their application largely remains confined to general task optimization, falling short in accurately responding to specialized problems like drilling engineering design and fault diagnosis. To address the limitations of general-purpose open-source LLM—such as comprehension deviations of professional terminology and insufficient integration of domain knowledge leading to poor scenario adaptability—this research aims to achieve a deep coupling of LLMs with drilling expertise. Based on the Python programming language and open-source platforms like MaxKB, this paper innovatively adopts a “Drilling Agent-Workflow” technical framework to construct DrillingGPT, a ChatGPT-like intelligent system for the petroleum drilling industry, also referred to as the Drilling Agent. This system effectively enhances the accuracy, logical soundness, and compliance of LLM in specialized drilling tasks, including professional Q&A and solution generation. The work aims to provide methodological insights and technical support for adapting general-purpose large models to vertical, engineering-specific domains.