中国P站

Abstract

This compilation of research addresses critical aspects of oil and gas production forecasting. Studies explore advanced methodologies including machine learning, deep learning, and hybrid data-driven and physics-based models to improve prediction accuracy. Uncertainty quantification using Bayesian methods and Monte Carlo simulations is highlighted for better risk assessment. The influence of reservoir heterogeneity and the application of time-series analysis for mature fields are discussed. Additionally, the integration of AI, big data analytics, ensemble forecasting, and real-time data assimilation techniques are presented as means to enhance forecasting reliability and efficiency in dynamic reservoir systems.