Аннотация
The article examines the application of artificial intelligence and machine learning technologies in the development of oil and gas fields under conditions of high reservoir heterogeneity. The analysis is structured around four main areas: reservoir characterization and facies interpretation, reservoir simulation and proxy modeling, field development management and interwell connectivity assessment, and screening and optimization of enhanced oil recovery methods. The study shows that machine learning methods can improve the accuracy of geological and geophysical data interpretation, reduce computational time, identify hidden patterns in production data, and support engineering decision-making. Attention is given to hybrid and physics-informed models that combine data-driven learning with physical constraints of flow processes. At the same time, the analysis demonstrates that most AI/ML models cannot fully replace conventional reservoir simulators and require mandatory validation using independent field data. The main limitations include dependence on the quality of training datasets, risk of overfitting, poor transferability to other fields, class imbalance, and insufficient physical interpretability of results. The paper concludes that the most promising direction is the development of hybrid approaches capable of accounting for geological heterogeneity and improving the reliability of development forecasts for complex reservoirs.