The digitalization and intelligent development of offshore oilfield is an inevitable trend. The soft sensing of multiphase flow measurement in oil and gas wells plays a crucial role in the development of intelligent oilfield. To study the applicability and impact on the application of data-driven soft sensing model of multiphase flow measurement in offshore oilfields, this paper bases its research on approximately 16 000 historical production data from more than 160 wells. Six production measurement parameters were selected as model input auxiliary variables, including electric submersible pump power, differential pressure upstream and downstream of the electric submersible pump, nozzle diameter and oil pressure, to develop a soft sensing model driven by the Extreme Gradient Boosting (XGBoost)algorithm. The trained model was tested on 2 400 oil field production samples and the results show that the average absolute error for measuring oil production, gas production and water production was within 6%. It is concluded that the data-driven soft sensing model based on neural network demonstrates good applicability and application effect for multiphase flow measurement in offshore oilfield and can serve as one of the solutions for singlewell measurement in oilfield. This study results offer significant reference for the development of intelligent oilfields.