油气勘探开发的智能化已成为油气工业发展趋势与研究热点。人工智能测井(Artificial intelligence logging，AIL)具有解决非常规油气资源及深地深海等复杂环境勘探开发难题的巨大潜力。然而，AIL技术发展的驱动模式，以及技术发展的基础、实现原理、技术组成以及应用场景还没有开展研究。

为构建完善的AIL测井体系生态，充分挖掘并展现AIL技术的潜力与价值，采用文献分析、理论研究、技术分析以及实例验证的方法。首先从多个维度出发，深入剖析了测井技术与人工智能(Artificial intelligence，AI)融合发展的关键因素，并据此定义了AIL。随后，系统探讨了AIL的基础理论框架、硬件算力需求以及数据物理模型，并通过知识发现的视角，详细阐述了测井方法、仪器、岩石物理及解释等环节在AIL体系中的功能实现机制。在技术层面，深入分析了包括测井大数据技术、智能与快速算法、测井知识图谱、数字孪生、智能仪器及测井物联网在内的多项关键技术，并指出物理模型与智能算法是推动AIL技术发展的核心驱动力。根据AI算法的原理与特性，系统梳理了AIL在测井方法、仪器、采集作业及解释等方面的关键技术，并构建了测井知识图谱树状图及其求解流程。

通过实证研究，验证了AIL在致密砂岩岩性识别及测井模拟中的优势，其精度达到93.8%，明显优于传统方法。在测井评价方面，AIL可同时实现储层和流体的识别，这充分说明了AIL技术的巨大发展潜力与应用优势。基于AIL技术的关键节点，展望了测井技术发展的第五个发展阶段，即智能测井。研究成果为AI在测井领域的深度融合与广泛应用提供坚实的理论基础与实践指导，对促进人工智能测井技术的推广及发展具有重要意义。

Intelligent hydrocarbon exploration and exploitation have become a trend and hot research topic in the oil and gas industry. Artificial intelligence logging (AIL) exhibits considerable potential to address challenges in the explore-exploit of unconventional hydrocarbon resources, as well as resources in complex environments in the deep earth and deep ocean. However, the driving mode, fundamental, implementation principle, structure, and application scenarios of AIL remain understudied.

To build a comprehensive ecology of the AIL system and thoroughly explore and reveal the potential and value of AIL, this study employed methods like literature analysis, theoretical research, technical analysis, and verification using cases. First, this study delved into the critical factors influencing the integrated development of logging technology and AI from multiple dimensions, defining AIL accordingly. Subsequently, it systematically explored the general theoretical framework, hardware arithmetic requirements, and data and physical models of AI. From the perspective of knowledge discovery, this study detailed the function implementation mechanisms of logging technology, instrumentation, petrophysics, and interpretation in the AIL system. Furthermore, it conducted an in-depth analysis of several critical technologies including log-related big data techniques, intelligent and fast algorithms, log knowledge graph, digital twins, intelligent instrumentation, and the Internet of things (IoT) of logs. Accordingly, this study posited that physical models and intelligent algorithms emerge as the core force driving the development of AIL. Based on the principles and characteristics of AI algorithms, this study systematically organized critical AIL technologies in terms of logging technology, instrumentation, acquisition operations, and interpretation, constructing the dendrogram and solving process of log knowledge graph.

The empirical research reveals that AIL enjoys advantages in terms of the lithologic identification of tight sandstones and logging simulations, accuracies of up to 93.8%, respectively, significantly exceeding those of conventional methods. Regarding log-based assessment, AIL can simultaneously identify reservoirs and fluids, sufficiently proving the considerable development potential and application advantages of AIL. Based on the critical links of AIL, this study envisions the fifth development stage of logging technology, i.e., artificial intelligence logging. The results of this study provide a solid theoretical foundation and practical guidance for the deep integration and extensive application of AI in the field of logging, holding great significance for the promotion and development of AIL technology.