White Paper: A Machine Learning Approach to Automate Gas Lift Optimization

White Paper: A Machine Learning Approach to Automate Gas Lift Optimization

Authors:
Venkat Putcha, PhD, Nhan C. Le and Mike Pennell

Abstract:
This paper presents a methodology for providing automated set-point recommendations to optimize gas lift well operation, by establishing a live synergy between physics-based simulation and real-time field data through the employment of machine learning models. The machine learning models serve two distinct purposes in this approach: 1. Accelerate simulation learning to enable real-time solutions 2. Probabilistic estimation of likely downhole and reservoir operating conditions of a well, with live updating of the probability based on the response to set-point changes, thus improving accuracy with each transition. 

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