Recovery Factor

Recovery factor (RF), representing the proportion of hydrocarbons extracted from a reservoir, is a crucial parameter for optimizing oil and gas production strategies. Current research heavily emphasizes the use of machine learning algorithms, such as XGBoost, support vector machines, and Bayesian networks, to predict RF using readily available reservoir characteristics, aiming to improve accuracy and reduce reliance on extensive, often unavailable, data. While these models show promise in estimating RF, studies consistently highlight the significant impact of data quality and the need for robust methods to handle data scarcity, particularly in early reservoir development stages. Accurate RF prediction is vital for informed decision-making in exploration, development, and production, ultimately impacting economic viability and resource management.