Geophysical logging series are valuable geological data that record the physical and chemical information of borehole walls and in-situ formations, and are widely used by geologists for interpreting geological problems due to their continuity, high resolution, and ease of access. Recently, machine learning methods are gradually bringing data science and geoscience closer together, and Intelligent Recognition using Logging Data (IRLD) is increasingly becoming an important interpretation task. However, due to the specificity of geological information, relatively low data quality makes the direct application of machine learning models to IRLD often not optimal. And to the best of our knowledge, IRLDs are not highly generalizable and technical surveys are still lacking. Therefore, this paper presents a comprehensive review of IRLD. Specifically, after systematically reviewing geophysical well logging and machine learning techniques, the main applications and general processes for the cross-discipline task of IRLD are summarized. More importantly, the key challenges of IRLD in the four stages of data acquisition, feature engineering, model building, and practical application are discussed in this review. The potential risks of these challenges are visualized by using real logging data from a study area in the South China Sea and the example of a lithology identification task. For these challenges, we give the current state-of-the-art methods and feasible strategies in conjunction with published research. This comprehensive review is expected to provide insights for practitioners to construct more robust models and achieve more effective application results in IRLD.

地球物理测井系列是记录井壁和原位地层物理和化学信息的宝贵地质数据，由于其连续性、高分辨率和易于获取而被地质学家广泛用于解释地质问题。近年来，机器学习方法逐渐将数据科学和地球科学紧密结合在一起，使用日志数据的智能识别（IRLD）日益成为一项重要的解释任务。然而，由于地质信息的特殊性，数据质量相对较低，使得机器学习模型直接应用于IRLD往往不是最优的。据我们所知，IRLD 的普遍性并不高，而且仍然缺乏技术调查。因此，本文对IRLD进行了全面的综述。具体而言，在系统回顾地球物理测井和机器学习技术的基础上，总结了IRLD跨学科任务的主要应用和一般流程。更重要的是，本文讨论了 IRLD 在数据获取、特征工程、模型构建和实际应用四个阶段的关键挑战。通过使用南海研究区的真实测井数据和岩性识别任务的示例，可以直观地看到这些挑战的潜在风险。针对这些挑战，我们结合已发表的研究给出了当前最先进的方法和可行的策略。这项全面的综述有望为从业者构建更稳健的模型并在 IRLD 中取得更有效的应用结果提供见解。