Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study

• Four distinct machine learning algorithms, namely multilayer perceptron (MLP), radial basis function network (RBF), general regression neural network (GRNN), and cascade feed-forward neural network (CFNN), were utilized in this study. • Based on the modeling results, CFNN performs better than MLPNN, RBFNN, GRNN, and CCD with an error value of 1.75 %. • The optimization methods yield notable enhancements compared to the non-optimized approach. Among these optimization algorithms, grey wolf algorithm (GWO) has the highest net present value and superior performance. • GWO algorithm does not require parameter tuning. Additionally, GWO and PSO exhibit smoother convergence rates and faster converge to the desired value. In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.