Аңдатпа
Gas hydrate applications range from energy extraction, carbon mitigation (CO 2 hydrates), hydrogen (H 2 ) storage, flow assurance to geohazards, but the thermodynamics, kinetics, and geology of gas hydrate systems are complex and lack sufficient experimental data. This article highlights recent contributions in the application of artificial intelligence and machine learning to manage gas hydrates in four main categories: (i) phase-equilibrium and hydrate formation pressure and temperature estimation for CH 4 , CO 2 , and H 2 hydrates; (ii) kinetics and induction time, plug risk prediction in pipelines and flow loops; (iii) in-situ detection, reservoir assessment and saturation determination from well logs, seismic, and XRD; and (iv) process/system simulation such as hydrate desalination, riser behavior, pore-pressure estimation, and slope stability. Techniques employed include support vector regression and least-squares support vector machines, ensemble models such as random forest, gradient boosting/gradient-boosted decision trees, XGBoost, LightGBM, Extra Trees, AdaBoost, and CatBoost; deep learning models including artificial neural networks, convolutional neural networks, recurrent neural networks/stacked autoencoders, and optimized deep neural networks; symbolic and evolutionary computing approaches such as gplearn, gene expression programming, genetic algorithms, grey wolf optimization, and whale optimization algorithms; physics-guided neural networks; and label-efficient log analysis using masked autoencoders. The ensemble tree model and kernel methods often perform with R 2 ≥0.9 in both equilibrium/pressure tabular data; convolutional neural network is proficient in predicting XRD/time series data. Self-supervised learning drastically reduces the amount of data required in labeling, while retaining high F1 scores for reservoir detection. Physics-informed learning enhances extrapolation capabilities and interpretability compared to pure data-driven learning approaches, whereas SHAP/sensitivity analysis consistently highlights pressure, salinity/ion composition, and operational parameters as the driving factors. Despite these gains, field-scale validation, uncertainty quantification, and standardized benchmarks remain limited. We consolidate best-fit model families by application and outline a research agenda that couples physics with data, prioritizes open multi-basin datasets, and integrates machine learning surrogates into digital-twin workflows for real-time gas hydrate decision support.