Workflow to predict wellhead choke performance during multiphase flow using machine learning

Abstract

Multiphase flow metering in oil and gas production wells is essential for monitoring the production performance from oil and gas reservoirs. Accurate measurements of multiphase flow rate that passes through the chock at the wellhead assembly is highly important as it provides a crucial information to estimate production to asses and optimize the reservoir performance. The current practice is to conduct production rate tests monthly for the entire wells that are connected through the same manifold. Another way to estimate the flow rate at the surface is using any of the several correlations to calculate the flow rate using production and wellhead chock data. The high uncertainty in production rate predictions is very well expected and the main source of this uncertainty is the reliance on sporadic well test data and empirical multiphase flow correlations. The objective of this study is to develop a machine learning model that outperforms the industry's widely used correlations in predicting the flow rate of critical and subcritical multiphase flow through the wellhead choke. This objective is achieved by developing a detailed workflow to generate a choke performance prediction model. Validating the accuracy and reliability of the developed model is done using actual data from more than 4000 production rate tests from different fields to investigate the model's accuracy. Testing the developed models showed that it provides a higher accuracy than the several correlations it was compared against in critical and subcritical flow. The average accuracy (correlation coefficient) of prediction was around 0.92 for critical and subcritical flow models. The developed models using machine learning provided a reliable method to predict flow rate through the wellhead chock by utilizing readily available surface data.