Soft Sensing for Gas-Condensate Field Production Using Parallel-Genetic-Algorithm-Based Data Reconciliation

During present offshore gas-condensate production, multiphase flow-meters, due to its exceedingly high cost, are being substituted by a soft sensing (SS) technique for estimating total and single-well flowrates 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 (PGA) without complex calculations required by conventional optimization. The newly developed SS method is tested by data from a realistic gas-condensate production system. The method is proved of good accuracy and robustness with invalid individual pressure sensor or unavailable total flowrate measurements. Meanwhile, the proposed method shows good parallel performance and the time cost of each DR process can meet the demand of engineering application.