Prediction of gas-liquid two-phase choke flow using Gaussian process regression. (October 2021) - Jiang, Youshi; Jia, Jing; Li, Yongming; Kou, Yan; Sun, Shaolin
Abstract: In the process of shale gas production, it is of great significance to select an appropriate mathematical method to accurately predict the gas flow rate of wellhead choke for the rational formulation of shale gas well production plan. Gas-liquid two-phase flow occurs in most of the time from the flowback to the production period in shale gas wells. Wellhead chokes play key roles in regulating the flowing rates of both the flowback fluid and shale gas. Therefore, it is important to study the law of two-phase choke flow clearly so as to accurately predict gas flow rate through wellhead chokes. Up to now, previous studies have proposed a variety of applicable empirical methods, including Gilbert-type correlation (GC), artificial neural network (ANN) and support vector machine (SVM). The analysis of training data and the establishment of accurate prediction models determine the accuracy of prediction. In this study, Gaussian process regression (GPR) was adopted to learn and predict the behavior of gas-liquid two-phase flow through wellhead chokes, and huge amounts of data collected from Chuannan Shale gas wells were used to verify the effectiveness of the GPR method. The prediction accuracy of the GPR method was compared with those of other methods like GC, ANN and SVM. In addition, we also compared the prediction accuracy of different kernel functions to select the best kernel function for GPR. The kernel functions considered are exponential function, squared exponential function, rational quadratic function and Matérn function. The results showed that GPR method is accurate and applicable for analyzing the behavior of gas-liquid two-phase flow through wellhead chokes, and GPR method with exponential kernel function could achieve greater prediction accuracy than other kernel functions. Highlights: Gaussian process regression (GPR) can be used to accurately predict gas flow rate through wellhead chokes. Exponential kernel function helps GPR achieve greater prediction accuracy than other analyzed kernel functions. Under the condition of upstream pressure (6–40 MPa) and LGR (10 −4 -10 −1 ), GPR-Ex obtains lower relative error.