Title: Artificial Intelligence Techniques for Gas Lift Network Modeling and Optimization: A Review
Authors: Isaac Eze Ihua-Maduenyi, Zekieni Robert Yelebe
Volume: 9
Issue: 4
Pages: 131-152
Publication Date: 2025/04/28
Abstract:
Gas lift optimization is essential for improving oil production efficiency and economic sustainability in brown fields, where declining reservoir pressure impedes fluid flow. This study critically reviews artificial intelligence (AI) techniques for full field-wide gas lift optimization, highlighting the necessity of advanced methodologies due to the complexity and non-linearity of gas lift systems. Traditional numerical approaches often fail to effectively manage these challenges. AI-driven methods, including machine learning (ML), meta-heuristics, fuzzy logic, and response surface methodology (RSM), offer promising solutions. Machine learning techniques, such as artificial neural networks (ANNs), model intricate relationships using historical data, while meta-heuristic algorithms like genetic algorithms (GAs) provide adaptive solutions. Fuzzy logic facilitates decision-making by managing uncertainty, and RSM applies statistical techniques to optimize operational conditions. However, each approach has inherent limitations: ANNs require extensive datasets and computational power, GAs are computationally demanding and susceptible to local optima, fuzzy logic depends on expert-defined rules, and RSM assumes well-defined variable relationships and can be resource-intensive. To address these limitations, the study advocates for hybrid models that integrate multiple AI techniques or combine AI with numerical methods. These integrated approaches enhance accuracy, efficiency, and reliability, offering a more comprehensive solution for gas lift optimization. By effectively managing the dynamic complexities of oil production systems, such models contribute to improved operational performance and long-term economic viability.