Abstract:

With the continuous advancement of oil and gas exploration technologies, unconventional hydrocarbon reservoirs have emerged as a pivotal domain for global energy resource augmentation and production enhancement. However, the inherent characteristics of low permeability, dense rock matrix, and complex heterogeneity in these reservoirs pose substantial challenges to conventional logging interpretation methodologies, particularly in constructing theoretical models, deriving empirical formulas, and inverting reservoir parameters, thereby hindering accurate reservoir identification and efficient development. The recent breakthroughs in artificial intelligence (AI) technologies have provided innovative solutions for logging interpretation in unconventional reservoirs. Through systematic analysis of cutting-edge research achievements worldwide, this paper first elucidates the core geological characteristics and evaluation challenges of unconventional reservoirs. Subsequently, it comprehensively summarizes the implementation modalities and operational efficacy of AI techniques, including machine learning and deep learning algorithms, in critical logging interpretation processes such as lithology identification, porosity prediction, permeability estimation, and hydrocarbon-bearing potential assessment. The study particularly highlights the transformative capabilities of convolutional neural networks in processing multi-scale logging data, recurrent neural networks in handling time-series measurements, and ensemble learning approaches in enhancing prediction accuracy under high-dimensional parameter spaces. The research demonstrates that AI-driven approaches achieve remarkable performance improvements compared to conventional methods, with reported accuracy enhancements of 25%~40% in lithofacies classification and 15%~30% reduction in mean absolute error for porosity estimation across various case studies. Furthermore, advanced deep learning architectures have shown exceptional capability in capturing nonlinear relationships between logging responses and reservoir properties, effectively addressing the “low signal-to-noise ratio” dilemma common in unconventional reservoir evaluation. A critical evaluation is conducted from multiple dimensions, including data quality requirements, algorithmic adaptability, computational efficiency, and model interpretability. The analysis reveals that while data-driven models excel in pattern recognition, their physical consistency and generalization capability require further improvement, particularly when dealing with spatially heterogeneous formations and limited training datasets. To address these challenges, the paper proposes three strategic development directions: (1) Hybrid modeling frameworks integrating physical constraints with data-driven approaches. (2) Transfer learning schemes for small-sample learning scenarios. (3) Multi-modal data fusion architectures incorporating logging, core, and seismic information. Moreover, the study emphasizes the necessity of establishing standardized workflows for feature engineering, model validation, and uncertainty quantification in AI-based logging interpretation systems. Special attention is given to emerging technologies such as graph neural networks for 3D reservoir characterization and physics-informed neural networks for incorporating petrophysical laws into machine learning architectures. This comprehensive review not only synthesizes the current state-of-the-art in intelligent logging interpretation but also provides a strategic roadmap for future research endeavors. The findings offer valuable theoretical references and methodological guidance for optimizing AI-based interpretation techniques in unconventional reservoir evaluation, ultimately contributing to more reliable reservoir characterization and enhanced hydrocarbon recovery in complex geological settings.

Key words: unconventional oil and gas reservoirs, artificial intelligence, lithofacies prediction, reservoir parameter prediction, sweet spot evaluation

摘要：

随着油气勘探技术的不断进步，非常规油气储层已成为增储上产的重要领域。然而，非常规油气储层由于其低渗透性、岩石致密性和非均质性，难以通过传统方法构建准确的理论模型和经验公式，形成准确的测井解释，指导储层的识别和挖掘。近年来，人工智能的快速发展为非常规储层的测井解释提供了新的解决思路。本文通过国内外文献调研，首先分析了非常规储层的主要地质特征及评价难点，然后梳理了机器学习、深度学习等技术在岩性识别、物性参数预测、“甜点”预测等测井解释环节的应用情况。已有研究显示，卷积神经网络能更好地处理多尺度测井数据，循环神经网络适用于时间序列测量分析，集成学习方法在复杂参数条件下可提升预测精度。实际案例显示，AI方法相比传统技术有明显改进：岩相分类准确率提高25%~40%，孔隙度估算误差降低15%~30%。深度学习模型还能有效挖掘测井数据与储层参数间的复杂关系，改善低信噪比条件下的解释效果。评估发现，现有数据驱动模型在物理规律匹配性和小样本适应性方面仍有不足，特别是在处理非均质地层时。针对这些问题，建议3个改进方向：(1)构建融合物理约束与数据驱动的混合建模框架；(2)开发面向小样本学习的迁移学习方法；(3)建立集成测井、岩心与地震数据的多模态融合体系，同时建议完善特征工程标准化流程，强化模型验证与不确定性量化机制，并关注图神经网络、物理信息神经网络等新兴技术的应用潜力。本研究通过分析人工智能技术在非常规储层测井解释中的适用性，为其方法选择和技术优化提供了实践参考。

关键词: 非常规油气储层, 人工智能, 岩相预测, 储层参数预测, 甜点评价

CLC Number: 

• TE122.1

• P618.13