A Robust Data-Driven Method Using Multiphase Flow Meter Measurements for a Fully Autonomous Gas-Lift Optimization

Traditional simulation-based approaches for Gas-Lift Optimization depend heavily on the quality of reservoir and fluid data. Excessive OPEX and man-hours are required to maintain data values and to ensure the models are calibrated. Even then, pseudo-steady-state models do not consider losses due to multi-pointing condition and slugging behavior; and for dynamic multiphase flow simulation, the added complexity and man-hours required to assert accurate results cannot be sustained on a full field scale deployment. Gas-Lift Optimization essentially relies primarily on the relationship between the Well Production Rate and the Gas-Lift Injection Rate. This presentation will show you how this robust Data Driven approach remove well models, correlations and personnel from the optimization process and improve drastically the wells’ performance in a fully autonomous manner.

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