(untitled) (INTERNATIONAL JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING, 2025)
This research investigates the gas lift optimization of an oil well using Artificial Neural Networks (ANN). Through statistical analyses of two wells spanning over a two-year period, the critical correlations among wellhead pressure, production rates, and gas compression parameters were reported. Notably, a positive correlation between wellhead pressure and production rate was identified, emphasizing the pivotal role of monitoring and optimizing these operational variables for enhanced efficiency. The research integrates machine learning techniques for gas lift with model recognition and parameter estimation. Leveraging the power of algorithms, like the Levenberg-Marquardt algorithm for the model, the best performance of 0.000000584 was found after 1000 epochs (iterations). The study demonstrates the potential for real-time decision support in oil well operations, offering a pathway for improved responsiveness and adaptability to changing conditions. Practical recommendations derived from the study provide actionable insights for industry practices, facilitating advancements in oil and gas engineering. The validation of machine learning models underscores their reliability in enhancing efficiency and productivity in real-world oil and gas applications. As a culmination of these findings, the research not only advances our understanding of gas lift systems but also provides a roadmap for the implementation of cutting-edge technologies and methodologies in the oil and gas sector.