Reservoir parameters are crucial indicators for reservoir evaluation and development and provide insights into long-term reservoir behavior. The primary methods for evaluating these parameters include direct core observations, experimental testing, and indirect evaluation techniques. Since its introduction, geophysical logging has been used to evaluate and invert reservoir parameters owing to its wide coverage. With an increasing focus on unconventional natural gas reservoirs, more refined reservoir evaluations and multiparameter analyses are required for their development to address the complex and microscopic models differing from those of the conventional petroleum reservoirs. Geophysical logging is important in several unconventional fields. Machine learning (ML) was used in unconventional gas reservoirs as an effective method to establish relationships between parameters and logging features. However, the accuracy of evaluating storage layers using a single ML method is limited. Studies focusing only on algorithm updates and indicator values are problematic in terms of interpretability and production applications. A need to standardize the use of algorithms and introduce validation comparisons such as geological methods is evident. In this study, we reviewed ML algorithms and models commonly used for logging inversion applications. The current research status and issues were analyzed for different unconventional gas reservoir parameters. Our findings emphasize the importance of combining geological and other methods for logging inversion using ML. We also used the random forest algorithm to accurately predict the reservoir porosity, gas content, coal structure, and macrolithotypes. Combined with established permeability and vitrinite reflectance models, factor analysis was used to comprehensively analyze and evaluate the coalbed methane reservoirs in the study area. In our assessment of the challenges and future work on ML-based inversion, we observed a clear advantage for ML algorithms under geologically validated methods and experimental control. ML has great potential for optimizing the application of logging inversion for unconventional reservoir parameters.

中文翻译：

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机器学习在非常规气藏物探反演中的应用综述


储层参数是储层评估和开发的关键指标，并提供了对长期储层行为的见解。评估这些参数的主要方法包括直接核心观测、实验测试和间接评估技术。自引入以来，由于地球物理测井的覆盖范围广泛，一直被用于评估和反转储层参数。随着对非常规天然气储层的日益关注，其开发需要更精细的储层评估和多参数分析，以解决不同于常规油藏的复杂和微观模型。地球物理测井在几个非常规领域都很重要。机器学习 （ML） 在非常规气藏中用作建立参数和测井特征之间关系的有效方法。但是，使用单个 ML 方法评估存储层的准确性是有限的。仅关注算法更新和指标值的研究在可解释性和生产应用方面存在问题。显然需要标准化算法的使用并引入验证比较，例如地质方法。在这项研究中，我们回顾了通常用于记录反转应用程序的 ML 算法和模型。针对不同的非常规气藏参数，分析了目前的研究现状和存在的问题。我们的研究结果强调了结合地质方法和其他方法使用 ML 进行测井反演的重要性。我们还使用随机森林算法准确预测储层孔隙度、含气量、煤结构和宏岩石型。 结合已建立的渗透率和镜质体反射率模型，采用因子分析对研究区煤层气储层进行综合分析和评价。在我们对基于 ML 的反演的挑战和未来工作的评估中，我们观察到 ML 算法在地质验证方法和实验控制下具有明显的优势。ML 在优化非常规储层参数的测井反演应用方面具有巨大潜力。