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A Novel Method of Crude Oil Water-Cut Detection Based on Multi-Sensor Fusion


 Water-cut detection of crude oil based on capacitive sensors and conductivity sensors are widely used in practice. However, the above two methods cannot achieve full-scale measurement of water-cut of crude oil. Besides, their measurement accuracy is sensitive to temperature, which greatly limits their application. A novel method of crude oil water-cut detection based on multi-sensor fusion is proposed in the paper.
 The proposed method uses a capacitive sensor and a conductivity sensor to measure the crude oil separately, and uses a temperature sensor to compensate the measurement result at the same time. Finally, the proposed method introduces a neural network for data fusion to predict the water content. The experiment results show that the proposed multi-sensor fusion technique performs better than capacitance and conductivity technique. The accuracy of this method is higher than single capacitance or conductivity method. When the water content is lower than 3%, the prediction error is less than 0.1%. When the water content is in a range from 3% to 10%, the prediction error is less than 0.5%. When the water content is in a range from 10% to 100%, the prediction error is less than 1.5%.