A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates. Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes. However, substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity, anisotropism, variance in reservoir fluid characteristics at diverse subsurface depths, which introduces complexity in production data. Therefore, the estimation of daily oil and gas production rates is still challenging for the petroleum industry. Recently, hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain. This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke (viz. stacked generalization and voting architectures), followed by an assessment of the impact of input production control variables. Otherwise, machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea. Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting. This study provides a chronological explanation of the data analytics required for the interpretation of production data. The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.