{"title":"A review of machine learning applications to geophysical logging inversion of unconventional gas reservoir parameters","authors":"","doi":"10.1016/j.earscirev.2024.104969","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11483,"journal":{"name":"Earth-Science Reviews","volume":null,"pages":null},"PeriodicalIF":10.8000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth-Science Reviews","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0012825224002976","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
储层参数是储层评价和开发的重要指标,可帮助人们深入了解储层的长期行为。评估这些参数的主要方法包括直接岩心观测、实验测试和间接评估技术。地球物理测井自问世以来,由于其覆盖面广,一直被用于评估和反演储层参数。随着人们对非常规天然气储层的日益关注,开发这些储层需要更精细的储层评价和多参数分析,以解决不同于常规石油储层的复杂微观模型问题。地球物理测井在一些非常规油田中非常重要。在非常规气藏中,机器学习(ML)被用作建立参数与测井特征之间关系的有效方法。然而,使用单一的 ML 方法评估储层的准确性有限。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,需要对算法的使用进行标准化,并引入验证比较,如地质方法。在本研究中,我们回顾了测井反演应用中常用的 ML 算法和模型。针对不同的非常规天然气储层参数,分析了当前的研究现状和问题。我们的研究结果强调了使用 ML 结合地质和其他方法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了储层孔隙度、含气量、煤结构和巨岩类型。结合已建立的渗透率和玻璃光泽反射率模型,我们使用因子分析法对研究区域的煤层气储层进行了全面分析和评估。在对基于 ML 的反演所面临的挑战和未来工作进行评估时,我们发现在地质验证方法和实验控制下,ML 算法具有明显的优势。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。
期刊介绍:
Covering a much wider field than the usual specialist journals, Earth Science Reviews publishes review articles dealing with all aspects of Earth Sciences, and is an important vehicle for allowing readers to see their particular interest related to the Earth Sciences as a whole.