Data Analytics-Driven Optimization of Gas Lift Operations Using Reinforcement Learning forIncreased Production Efficiency

Abstract

Optimizing gas lift techniques is pivotal in boosting the efficiency of oil extraction and maximizing the recovery from reservoirs. Traditional optimization methods often depend on oversimplified models and overlook the complex dynamics of gas lift systems. This paper introduces an innovative method that employs data analytics and reinforcement learning for refining gas lift operations, enhancing production efficiency, and making better decisions. The suggested strategy makes use of past production data, instantaneous sensor readings, and sophisticated analytic methods to build an optimization framework driven by data. This framework involves cleaning data, extracting relevant features, and implementing reinforcement learning algorithms to adjust gas lift injection rates dynamically. A comprehensive dataset of operational parameters used in gas lift and indicators of production performance serves as the training ground for the reinforcement learning model. This model adeptly decides the best course of action by analyzing the current condition of the gas lift system, considering aspects such as characteristics of the well, properties of the reservoir, and operational limitations. The training is steered by a reward system that focuses on increasing oil output while aiming to cut down gas lift expenses. This optimization framework is seamlessly integrated into a system that monitors and controls in real time, continually tweaking gas lift injection rates based on the reinforcement learning model’s advice. This system is capable of adapting to evolving conditions of the well and the dynamics of the reservoir, fostering proactive optimization and minimizing manual adjustments. This methodology, driven by data analytics, represents a significant step forward in optimizing gas lift operations, offering a robust tool for production engineers and decision-makers. Utilizing data analytics and reinforcement learning enables operators to fine-tune gas lift operations, boost production efficiency, and base decisions on insights from real-time data.