State-of-the-art machine learning for reservoir characterization: methods, applications, and challenges

Reservoir characterization integrates geological and petrophysical information to estimate key subsurface properties and reduce uncertainty in field development. However, the workflow remains challenging because reservoir systems are inherently heterogeneous and nonlinear, while field datasets are often limited, noisy, and inconsistent across wells and formations. This review synthesizes state-of-the-art machine learning (ML) methods for reservoir characterization using commonly available petroleum datasets, including core and petrophysical measurements, well logs, seismic attributes, and production/well test data. The literature is organized by learning objective (regression, classification, and optimization) and major model families, emphasizing artificial neural networks (ANNs), support vector machines (SVMs), and fuzzy/hybrid frameworks for key tasks such as porosity-permeability prediction, lithofacies identification, saturation estimation, and reservoir connectivity analysis. Several studies have demonstrated that machine-learning-based well log interpretation workflows can achieve high predictive performance for reservoir characterization tasks, with reported R2 values frequently exceeding 0.90 and, in some optimized field-specific applications, approaching 0.98. Optimization-assisted ANN, ensemble-learning, and hybrid machine-learning approaches have further enhanced prediction accuracy, model convergence, and error reduction for applications including porosity, permeability, saturation, and missing-log prediction. Nevertheless, model performance remains highly dependent on reservoir heterogeneity, data quality, feature engineering, and validation strategy, highlighting the need for rigorous model development and independent validation prior to field deployment. Beyond summarizing applications, this review identifies key causes of degraded field performance, including data leakage during validation, and domain shift across wells and fields. The key novelty is that prior work is fragmented across data types and isolated case studies; this review consolidates those results into a unified, task-driven framework that synthesizes methods, typical performance metrics, limitations, and deployment guidance—providing a practical reference for selecting and validating ML workflows for reservoir characterization. • Comparison of experimental and ML methods enhances reservoir characterization. • Machine learning advances production forecasting and well test interpretation. • Hybrid modeling and data integration significantly improve reservoir accuracy.