Application of Genetic-Algorithm-Based Data Reconciliation on Offshore Virtual Flow Metering of Gas-Condensate Field Production

Abstract

During present offshore gas-condensate production, flow meters, due to its exceedingly high cost, are being substituted by Virtual Flow Metering (VFM) Technology for monitoring total and single-well flow rates through sensor measurements and physical models. In this work, the inverse problem is solved by Data Reconciliation (DR), minimizing weighted sum of errors with constraints integrating multiple two-phase flow models. The DR problem is solved by Parallel Genetic Algorithm, without complex calculations required by conventional optimization. The newly developed VFM method is tested by data from a realistic gas-condensate production system. The results show good accuracy for the total mass flow rate with model calibration. Meanwhile, recommended single-well flow rate can be provided without physical meters. The method is proved of good robustness with individual pressure sensor invalid, even total flow rate measurements unavailable. The time cost of each reconciliation process can meet the demand of engineering application.