Machine Learning Application for Gas Lift Performance and Well Integrity

Abstract

Constructing and maintaining integrity for different types of wells requires accurate assessment of posed risk level, especially when one barrier element or group of barriers fails. Risk assessment and well integrity (WI) categorization is conducted typically using traditional spreadsheets and in-house software that contain their own inherent errors. This is mainly because they are subjected to the understanding and the interpretation of the assigned team to WI data. Because of these limitations, industrial practices involve the collection and analysis of failure data to estimate risk level through certain established probability/likelihood matrices. However, those matrices have become less efficient due to the possible bias in failure data and consequent misleading assessment. The main objective of this work is to utilize machine learning (ML) algorithms to develop a powerful model and predict WI risk category of gas-lifted wells. ML algorithms implemented in this study are; logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, and gradient boosting algorithms. In addition, those algorithms are used to develop physical equation to predict risk category. Three thousand WI and gas-lift datasets were collected, preprocessed, and fed into the ML model. The newly developed model can predict well risk level and provide a unique methodology to convert associated failure risk of each element in the well envelope into tangible value. This shows the total potential risk and hence the status of well-barrier integrity overall. The implementation of ML can enhance brownfield asset operations, reduce intervention costs, better control WI through the field, improve business performance, and optimize production.