In this paper, we present a water-cut estimator utilizing the function approximation capability of an artificial neural network (ANN). The inputs to the ANN are optical sensor readings in a Red-Eye water-cut meter, which features the near-infrared (NIR) absorption spectroscopy technology. The initial training of the ANNwas done with a data set acquired from our multi-phase flow-loop test facility, which was filled with live oil, water and gas. The test fluid stream was adjusted with good ranges of water-cut and gas-volume fractions which were supposed to cover the situations that can be foreseen in real production. However, clear discrepancies between the outputs of the ANN and the water-cut values from BS&W measurmentswere observedwhen the ANN was applied to actual production data measured by Red-Eye meters installed at two offshore wells. To address this issue and equip the ANN with self-adapting capability in real application, we propose a Bayesian approach to update the parameters of the ANN based on both initial flow-loop data and collected field data. The performance of the adapted ANN on both the data sets shows the effectiveness of the method.
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Red Eye® Series of Water-Cut Meters - Consistently accurate water-cut measurements in tough oil field conditions. Weatherford Product Manual.
Ukil, A, Bernasconi J.,Braendle H,Buijs Henry and Bonenfant S., "Improved Calibration of Near-Infrared Spectra. by Using Ensembles of Neural Network Models," IEEE Sensors Journal, Vol. 10, No. 3, 578–584, 2010.
Meribout, M., "A near infrared-based downhole water-cut meter using neural network technique," Proc. SPIE 9219, Infrared Remote Sensing and Instrumentation XXII, 92190O.
Pan, S. J., and Yang, Q., A Survey on Transfer Learning, IEEE TransonKnowledgeandDataEngineering, Vol. 22, No. 10, 1345–1359, 2010
Robøle, Kvandal and Schüller, The Norsk Hydro Multi Phase Flow Loop. A highpressure flow loop for real three-phase hydrocarbon systems, Flow Measurements and Instrumentation, 2006
Hagen, M., Neural Network Design (2nd Edition), 2014.
Dan, F. and Hagan, M., "Gauss-Newton approximation to Bayesian learning." Proceedings of the Int. Joint Conf. on Neural Networks, 1997.
Bertsekas, D., "Incremental least squares methods and the extended Kalman filter," SIAM J. Optimization, Vol. 6, No. 3,807–822, 1996.
Skoglund, M. A.,Hendeby, G., and Axehill, D., "Extended Kalman filter modifications based on an optimization view point," Proceedings of 18th Int. Conf. on Information Fusion, 1856–1861, 2015.
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