Petroleum Research (Jun 2022)
Application of machine learning algorithms to predict tubing pressure in intermittent gas lift wells
Abstract
Tubing pressure at gas injection depth in intermittent wells is one of the most critical parameters for production engineers to evaluate the performance of the system. However, monitoring of the tubing pressure is not usually carried out in real time. It has been realized that the generally used correlations are not effective enough due to complexity of the intermittent process which involve many parameters and assumptions to develop such equations. The focus of this study is to utilize machine learning (ML) algorithms to develop a model that can accurately predict tubing pressure in artificial intermittent gas lift wells. intelligent algorithms built on the field data provide a solution that is easy to use and universally applicable to the complex problems. Various non-linear regression ML methods are employed in this study, namely, Decision Tree-regression (DT), Random Forest-regression (RF) and K Nearest Neighbors-regression (KNN). All the tubing pressures obtained from ML models were compared with the actual values to ensure the effectiveness of the work. The developed models show that it can predict the pressure with more than 99.9% accuracy. This is an interesting result, as such outcome accuracy has not been reported usually in the open literature.
