油气勘探开发领域知识图谱构建及应用探讨

doi:10.3969/j.issn.1002-302x.2023.06.007

摘要/Abstract

摘要： 当前大数据和人工智能技术快速发展，新一代人工智能逐步从感知智能向认知智能转化，作为人类意识和概念承载体的知识图谱近年被普遍关注，成为现阶段认知智能主要发展方向之一。结合前人在通用行业和石油领域知识图谱的研究基础，梳理了知识图谱的概念体系，从业务活动角度出发，建立了一套以业务模型为核心的自顶向下的石油勘探开发知识图谱领域本体构建的技术方法；在此基础上，结合圈闭、油气藏及井筒研究等业务具体特点，分别构建了圈闭、油气藏及井筒研究三大领域知识图谱；结合实际业务需求，探索了知识图谱结合人工智能算法在油气勘探开发业务中的知识检索、知识推理和知识评价三类应用场景。应用取得初步成效，知识检索和推理服务极大提高了知识获取质量及效率，知识评价应用初步实现了各种参数的快速获取引用。

关键词: 油气勘探开发；知识图谱；业务模型；智能算法

Abstract: With rapid development of big data and artificial intelligence technology at present, the new-generation artificial intelligence is in transition to cognitive intelligence from perceptual intelligence. Knowledge graph, as a carrier of human consciousness and concepts, has come under wide attention in recent years and becomes one of the main development directions for cognitive intelligence in the current stage. Based on the previous study of knowledge graph in the general industry and petroleum area, this paper elaborates the conceptual system of knowledge graph and, from the perspective of business activities, establishes a set of business model-focused and from-top-to-bottom construction techniques for knowledge graph of oil exploration and development. Furthermore, combined with the study of trap, oil and gas reservoir and wellbore, the paper establishes knowledge graphs in the main three research areas of trap, oil and gas reservoir and wellbore, respectively. In the light of the actual business demands, the study explores three application scenarios of utilizing knowledge graphs combined with AI algorithm in oil and gas exploration and development business – knowledge retrieval, knowledge reasoning and knowledge evaluation. With initial application results achieved, knowledge retrieval and knowledge reasoning services significantly improve the quality and efficiency of knowledge acquisition while knowledge evaluation initially brings about rapid acquisition and quotation of various parameters.

Key words: oil and gas exploration and development, knowledge graph, business model, intelligent algorithm

中图分类号:

TE15
G353.1

徐寅　卫乾　赵世亮　熊伟　王洋洋　刘斌　庞资胜　赵启蒙　许子君. 油气勘探开发领域知识图谱构建及应用探讨[J]. 石油科技论坛, 2023, 42(6): 47-60.

Xu Yin, Wei Qian, Zhao Shiliang, Xiong Wei, Wang Yangyang, Liu Bin, Pang Zisheng, Zhao Qimeng, Xu Zijun. Discussion on Construction and Application of Knowledge Graph in Oil and Gas Exploration and Development Area[J]. Petroleum Science and Technology Forum, 2023, 42(6): 47-60.

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