Real-Time Production Optimization: A Machine Learning Approach to Virtual Flow Metering

Abstract This study investigates the application of machine learning (ML) techniques for virtual flow metering (VFM) in oil wells. To develop a robust and accurate VFM model, Two Different Fields were tested for the new technique, one field offshore Egypt & the other field is onshore Iraq; This comprehensive dataset included detailed production data, well parameters, and operational information. Various ML algorithms, including Random Forest, Support Vector Regression, and Artificial Neural Networks, were rigorously tested and compared to identify the optimal model for VFM. The selected model was then calibrated against production test data to ensure accurate and reliable predictions. The calibrated ML model enables the daily verification of production allocation, providing real-time insights into the performance of individual wells. By continuously monitoring production rates, anomalies such as production declines, unexpected shutdowns, or changes in reservoir behavior can be promptly detected. These timely insights facilitate efficient decision-making, allowing for timely interventions and optimization of production strategies. Additionally, the ML-based VFM model supports accurate reservoir allocation by providing reliable estimates of individual well contributions, enabling informed decisions on production allocation and field development planning. After rigorous calibration, the model's performance was further validated through a blind test, where it was applied to a new set of wells without prior training. The results of the blind test confirmed the model's usability and its ability to provide accurate predictions. Moreover, the daily application of the model has demonstrated reasonable agreement with well test data, further solidifying its reliability and practical value. The findings of this study demonstrate the significant potential of ML-based VFM to enhance the efficiency and effectiveness of oil production operations in Egypt & Iraq Assets. By leveraging advanced ML techniques and a comprehensive dataset, this approach offers a reliable and cost-effective solution for improving production performance diagnostics, optimizing reservoir management, and ultimately maximizing oil recovery