Background 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.
Objective and Methods 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.
Results and Conclusions 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.
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