Improved Aziz Prediction Model of Pressure Gradient for Multiphase Flow in Wells

Abstract

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-I 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.

A neural tool for the prediction of the experimental dynamics of two-phase flows

“…Moreover, even when such descriptions are obtained, it is not easy to generalize their validity to account for other systems, due to the fundamental role played by the thermo-physical properties of the two phases, by geometrical parameters, such as the pipe inclination, the cross-section and the hydraulic diameter, but also by the nature of the observed variables and of the measurement technique correspondingly adopted. This justifies the great effort towards the possibility of realizing "black box" identification tools, often based on a variety of strategies assessed within the field of artificial intelligence, as summarized in [1]. In fact, on the basis of limited information on the regime of a given system, the identification approach is often able to accurately predict some fundamental characteristics of the two-phase process, such as the holdup, the pressure drop or the type of flow pattern.…”

Experimental study and application of seepage characteristics of chang 72 tight oil reserves in ordos basin

“…There are abundant tight oil reserves in Ordos Basin, Junggar Basin, Sichuan Basin, Songliao Basin and Bohai Bay Basin, the total volume from these basins being 111.5 x 10 8 t (Yanget al, 2015;Duet al, 2014). Due to the complexity and uniqueness of tight oil reservoirs, it is difficult to be satisfy the production needs using conventional development methods (Wei et al, 2016;Li et al, 2016). In recent years, the development of efficient exploitation of tight oil is the focus of research.…”