基于相似规律和神经网络的多级多相混输泵气液增压性能预测

It is very important to accurately predict the gas-liquid pressurization performance of multiphase pumps for the economy and safety of oil-gas production. Existent prediction models and methods are limited by narrow parameter ranges and low pump stages. A gas-liquid experimental platform at the industrial level was built, and the gas-liquid pressurization performances of a 25-stage centrifugal multiphase pump were obtained. A prediction method for gas-liquid pressurization performances was proposed for multiphase pumps with high stages at variable rotational speeds. Firstly, the artificial neural network of gas-liquid boosting pressure in the pump with low stages at a constant rotational speed, was constructed. Then, the boosting pressures at variable rotational speeds were converted to the designed condition by the 2-phase similarity law. Finally, based on the isothermal compression hypothesis, the inter-stage flow parameters were updated and the boosting pressures in pumps with high stages were acquired. The relative errors of prediction results of gas-liquid pressurization were less than 15% in pumps with different stage numbers (3~25 stages) and rotational speeds (2 500~3 500 r·min^-1). The proposed method can be applied to other types of multiphase pumps, to determine the stage numbers of multiphase pumps and make production evaluation in oil-gas industry.