An hybrid ensemble based approach for process parameter estimation in offshore oil platforms

International audienceThe valve flow coefficient is commonly used as a parameter to assess the erosion state of choke valves in offshore oil platforms. In particular, the difference between the theoretical value of the valve flow coefficient and its actual value calculated during operation is retained as the valve health indicator. The actual valve flow coefficient is analytically calculated from the oil, water and gas mass flow rates. These quantities, which are allocated on a daily basis based on the measured total production from a number of wells, on physical parameters (pressures and temperatures) related to the specific well, and on a physical model of the process, can be affected by large uncertainties. Based on such values, the evaluation of the health indicator becomes unreliable and undermines the possibility of using it for prognostic purposes. Similar situations arise every time health monitoring rely on unreliable measurement taken by sensors subject to hard working condition, as often happen in the nuclear industry. This paper proposes a method to obtain more accurate daily estimates of the actual values of the oil, water and gas flow rates, from which improved estimates of the flow coefficient will follow. In this respect, an hybrid ensemble aggregating the physical model with data-driven models built using the Kernel Regression (KR) method has been used. Ensemble diversity is ensured by using different training sets;a local procedure based on the historical performance of the models is adopted to aggregate their predictions. The method is verified on real measurements performed on a number of similar offshore choke valves

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

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Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

This paper considers the problem of erosion in choke valves used on offshore oil platforms. A parameter commonly used to assess the valve erosion state is the flow coefficient, which can be analytically calculated as a function of both measured and allocated parameters. Since the allocated parameter estimation is unreliable, the obtained evaluation of the valve erosion level becomes inaccurate and undermines the possibility of achieving good prognostic results. In this work, cluster analysis is used to verify the allocated parameter values and an ensemble of Kernel Regression models is used to correct the valve flow coefficient estimates