AI-Based Smart Proxy Models for Accurate Oil Rate Prediction and Efficient Pipeline Monitoring

Abstract

This research develops an advanced AI-based smart proxy model to significantly enhance the prediction of oil rates and the monitoring of crucial operational parameters such as temperature and pressure in oil field pipeline management. By integrating real-time data from Multiphase Flow Meters (MPFM) with sophisticated simulation outputs, the study introduces a dual-model approach that overcomes the limitations of traditional methods, improving both efficiency and cost-effectiveness. Model 1 employs high-precision real-time MPFM data to provide accurate oil rate predictions. By focusing on critical control points within expansive pipeline networks, this model strategically reduces dependency on extensive MPFM deployment, achieving substantial cost reductions while maintaining rigorous measurement standards. The incorporation of real-time data ensures immediate responsiveness to operational changes, facilitating accurate and reliable insights essential for effective pipeline management. Model 2 utilizes an AI-driven smart proxy to refine the outputs of conventional flow simulators such as OLGA. This model addresses computational challenges including high runtime and numerical convergence issues by selecting the most reliable and accurate simulation outputs. It provides rapid and dependable insights into flow dynamics, supporting timely operational decisions and proactive management that enhance the safety and efficiency of pipeline networks. The integration of Model 1 and Model 2 ensures localized precision and extends analytical capabilities across the entire pipeline network, significantly enhancing predictive accuracy. This harmonized approach not only sets new standards for flow assurance and pipeline management but also illustrates the transformative impact of AI on operational strategies in the hydrocarbon sector.