Research And Design Of Crude Oil Water Content Measurement System Based On Capacitance Method

The crude oil water content is an important indicator for measuring the production efficiency of oil wells and formulating the extraction plan,and its accurate measurement is of great significance in optimizing the production process,which is the primary link in the process of oilfield production,dewatering,storage and transportation.Accurate and timely online measurement of crude oil water content can truly reflect the well dynamics,which is of great significance to realize the automated management of oilfield production in China.In this study,we analyzed the existing crude oil water content measurement techniques,summarized the advantages and disadvantages of various methods and instruments at home and abroad,and finally selected the capacitance method for water content measurement,and used temperature compensation to improve the test accuracy.In this paper,the measurement principle of the capacitance method for water content of crude oil is studied,and the system is designed based on this principle.Since the mixing saving constants of crude oil will be different when the water content in crude oil is different,the mixing equivalent dielectric constant model is studied and selected to establish the mathematical relationship between the parallel plate capacitance and the water content of crude oil to provide a theoretical basis for the static calibration test.A system was designed to measure the parallel plate capacitance using STM32F103RCT8 as the core and FDC2241.After the system was built,a static calibration experiment was done to provide a reference for the dynamic experiment,and temperature compensation was added during the experiment to reduce the measurement error.In the actual measurement of parallel plate capacitors there is an edge effect,which can be simulated and analyzed using the physical field simulation software COMSOL,and the measurement results are corrected using this model to get more accurate water content.According to the measurement results,the accuracy of the measurement results is high when the water content is lower than 65%,and the error is within 2.5%;when at higher water content,the error is larger.For this reason,data fusion technology can be used to fuse the sensor data when the water content is high,and construct a neural network model with capacitance sensor and temperature sensor as input and water content as output for predicting the water content of crude oil.The model is trained and predicted,and the results show that the GA-BP model optimized using genetic algorithm has less error and the predicted water content is closer to the actual water content compared to the BP neural network model before optimization.The model can be applied to water content measurement to obtain the water content of crude oil more accurately and has higher application value.