摘要：

生成式人工智能(如DeepSeek)的快速发展显著降低了大模型技术的应用门槛，为油气勘探开发这一高度依赖经验与数据的领域注入了新的智能化动能。本研究以DeepSeek系列模型为对象，系统评估其在油气勘探开发领域的多维应用能力。通过设计六维量化评估体系，对领域基础知识、复杂推理能力、计算能力、多模态能力、开放创新性问题及专业工作能力进行评估。测试结果表明，大模型在基础知识覆盖度、处理开放性、创新性问题方面表现优异，展现出较强的领域知识理解能力与跨学科整合潜力；但在处理具体实例和数据时存在幻觉风险，在复杂推理问题中细粒度逻辑推理能力不足，复杂数值计算的准确性与效率存在缺陷；同时，在多模态处理(尤其是专业图像生成与识别)、专业软件操作及实时工程响应等方面存在明显的能力边界。针对测试暴露的局限性，本文提出了一套四维一体技术路径，以实现智能转型，它包括：基于检索增强生成(RAG)的动态知识融合机制，以解决知识滞后与数据幻觉问题；知识图谱驱动的推理引擎，旨在提升复杂问题的逻辑推理精度；专业软件协同架构，通过API网关整合专业工具以扩展模型能力边界；智能体(Agent)赋能的工程系统，以实现复杂任务的自动化分解与执行。对垂直领域知识图谱构建、软件生态协同、智能体实时决策等关键技术瓶颈进行了深入分析，并提出了相应的攻关方向。研究认为，大模型在油气领域的深度应用需与领域知识工程、专业软件生态及边缘计算技术深度耦合，从场景建设入手，突破关键技术，实现“数据—知识—工具”协同应用，逐步实现从单点能力增强到系统工程智能化的转型。

关键词: 油气勘探开发, AI大模型, DeepSeek, 检索增强, 知识图谱, 智能体, 大语言模型

Abstract:

The rapid advancement of generative artificial intelligence (AI), exemplified by models like the DeepSeek series, has substantially lowered the application barrier for large language model (LLM) technology. This progress injects new intelligent capabilities into the field of oil and gas exploration and development, a domain highly dependent on expertise and data-intensive analysis. However, the practical capabilities and implementation pathways of large language models in vertical industry scenarios remain unclear. This study comprehensively and systematically evaluates the multidimensional application capabilities of the DeepSeek model series within this specific domain. A comprehensive six-dimensional quantitative assessment framework was designed to evaluate core competencies, including foundational domain knowledge, complex reasoning, computational proficiency, multimodal processing, performance on open-ended and innovative problems, and professional task execution capabilities. The testing results indicate that LLMs demonstrate exceptional performance in terms of breadth of foundational knowledge coverage and in handling open-ended, innovative questions, revealing strong domain-specific comprehension and application and significant potential for interdisciplinary knowledge integration. However, several critical limitations were identified. The models exhibit hallucination risks when processing specific instances and data, display a lack of sufficient granularity in logical reasoning within complex problem-solving scenarios, and show deficiencies in the accuracy and efficiency of intricate numerical computations. Furthermore, distinct capability boundaries were observed, particularly in multimodal processing - especially the generation and interpretation of professional diagrams and images -, in the operation of specialized software, and responsiveness to real-time engineering demands. To address these identified limitations, this paper proposes an integrated four-dimensional technical pathway to facilitate intelligent transformation. This cohesive strategy comprises: 1) a dynamic knowledge fusion mechanism based on Retrieval-Augmented Generation (RAG) to mitigate knowledge obsolescence and data hallucinations; 2) a knowledge-graph-driven reasoning engine designed to enhance logical reasoning precision for complex problems; 3) a specialized software collaboration architecture that extends the model’s operational boundaries via API gateways integrating domain-specific tools; and 4) an Agent-empowered engineering system for the automated decomposition and execution of complex tasks. The research further delves into key technical challenges, such as the construction of vertical domain knowledge graphs, software ecosystem interoperability, and real-time decision-making by AI agents, proposing targeted directions for technological breakthroughs. In conclusion, the deep integration of LLMs into the oil and gas sector necessitates tight coupling with domain knowledge engineering, specialized software ecosystems, and edge computing technologies. The transition from point solutions to systemic intelligence should be gradual, starting with focused scenario development, overcoming core technical bottlenecks, and ultimately realizing the synergistic application of “Data-Knowledge-Tools.”

Key words: oil and gas exploration and development, generative artificial intelligence, DeepSeek, Retrieval Augmented Generation (RAG), knowledge graph, intelligent agents, large language model

中图分类号: 

• TP18

• TE34