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In the process of gas lift design and condition diagnosis, the accuracy and timeliness of wellbore multiphase flow model prediction results are the basis for all subsequent work. However, for the commonly used wellbore multiphase flow pressure drop prediction models, there is a big deviation between the predicted value and the measured one, and the optimization based on the measured data is time-consuming, and it is difficult to obtain the optimal parameters of the model. Therefore, based on the well bore pressure distribution data measured quickly in area R of an oil field in Kazakhstan, a better prediction model of multiphase flow in the well bore was selected at first. Then, the simultaneous perturbation stochastic approximation (SPSA) algorithm was incorporated in the wellbore multiphase flow model to optimize the liquid holdup, which is the leading factor in the model. After repeated single well optimization and greedy selection, the optimal parameters suitable for the whole block were obtained. The example shows that the optimization speed is 10 times faster than that of Particle Swarm Optimization (PSO). After that, the optimized model was used to predict the wellbore pressure distribution, and it was found that the relative error between the measured value and the predicted one was less than 15%. [ABSTRACT FROM AUTHOR]

The Mukherjee-Brill prediction model of pressure gradient is the common calculation model in oil-gas field development. The laboratory experimental results of multiphase flow show that the average prediction relative error is 35% and the maximum relative error reaches 72%. By comparing the prediction residual of the Mukherjee-Brill model with the test condition parameters, this paper suggests that the predicted residual of Mukherjee-Brill model has a close relationship with the gas-liquid ratio and has no clear correlation with the water content of fluid when the volume flow rate of liquid is constant. This paper first builds a cubic regression model of the prediction residual of the Mukherjee-Brill model regarding the gas-liquid ratio, and subsequently, combining the cubic regression model with the Mukherjee-Brill model, builds a new prediction model of pressure gradient, the MBM model. The results for 90 test groups show that the average prediction relative error of the MBM model is reduced to 7.66%. The MBM model improves the prediction accuracy of the pressure gradient for multiphase flow, and assists in advancing the reliability of the design and analysis of oil and gas wells. [ABSTRACT FROM AUTHOR]

It is crucial for the completion parameter design and production performance detection interpretation to have an accurate pressure gradient. The Aziz prediction model of pressure gradient is a common calculation model in oil-gas field development. The laboratory experiment results of multiphase flow show that the average prediction relative error is 29.62% and the maximum relative error reaches 70.1%. By comparing the prediction residual of the Aziz model with the experiment condition parameters, as the volume flow rate of the liquid phase is constant, this paper considers that the prediction residual of the Aziz model is closely related to the gas liquid ratio and has no clear correlation with the water fraction of fluid phase. Based on unifying the orders of magnitude of the prediction residual and the gas liquid ratio, this paper uses a cubic function of the gas liquid ratio to fit the prediction residual of the model to obtain a new pressure prediction method which is called Aziz-1 model. The results for ninety groups of experimental data show that the average prediction relative error of the Aziz-I model is reduced to 10.82%. Hence, the Aziz-I model improves the prediction accuracy of pressure gradient for multiphase flow. [ABSTRACT FROM AUTHOR]

problem of pressure drop for oil-gas-water three phase vertical flow, the prediction accuracy of the existing prediction model is lower based on the high-accuracy data. This paper displays the predicted pressure drop graphically. Considering the geometrical characteristics of the predicted pressure drop, a new prediction model of pressure drop is built based on the function fitting and interpolation. The new model is suitable for the water content from 0 to 100%. The computed results show that the predicted average relative error is decreased from more than 40% to less than 2.33% under different water content conditions with the experimental data. This paper uses the experimental data with water content 30 and 90% to obtain the pressure drop under water content 60% by interpolation. The average relative error is decreased from 41.5 to 12.00%. [ABSTRACT FROM AUTHOR]

In view of the limited conditions of the previous inclined multiphase pipe flow experiments (diameter and flow) with no reliable adaptability of multiphase pipe flow calculation method which is obtained from this experimental conditions when the using conditions are beyond the scope of the experiment (such as a middle to large diameter and large flow), the gas-liquid two-phase flow experiment was carried out using air and water as the medium with different inclined angles 0°, 15°, 30°. 45°, 60°, 75° and 90° in this paper. The analysis of variation of liquid holdup and pressure drop with different gas and liquid quantities and inclination angles was obtained. The calculation method applicability of liquid holdup and pressure drop were verified and selected from the experimental data, and the error variation law of the calculation methods w ere analysed. The applicability of the calculation method was compared with its applicability to liquid holdup and pressure drop as verified by the field test data. According to the analysis of the comparative results, the applicability conditions of the existing pressure calculation methods were obtained and they provided the guidance for the selection of the inclined multiphase pipe flow calculation methods under the condition of middle and high production rates. [ABSTRACT FROM AUTHOR]