Application of Multi Objective Whale Optimization Algorithm for Solving Lift Gas Allocation Problem and Production Enhancement in Oil & Gas Industry

Abstract Gas lift is one of the most commonly used artificial lift method in oil producing wells. The technique requires constant Optimization of injection gas quantity in the well to maximize oil production. High pressure Lift Gas allocation is an important step in optimization process to reduce the investment on costly and scarce lift gas and maximize oil recovery. In this work, an attempt is made to optimize a group of wells with application of an innovative metaheuristic algorithm. The objective function can be either the generated profit or total produced oil as a function of injected gas. To achieve maximum recovery from gas-lifted wells, it's essential to identify the optimum injection rate for specific facility constraints, including gas availability, maximum injection depth, and compression capabilities. Normally, these parameters are unchangeable because of prior selection and installation, except when optimization techniques are implemented in the design phase. The Gas Lift Performance Curves (GLPC) are the main design element used for optimized gas injection. These curves are generated by modelling wells in a multiphase steady-state simulator. After building model, sensitivity analysis is run, and the curves are generated. In this work, the common workflow to generate GLPC is followed. Then, a new correlation for GLPC is suggested with help of a bio-inspired meta heuristic Whale Optimization Algorithm (WOA). The correlation is then used to formulate a case study for few wells located in Caspian Sea. R-score and Root Mean Square Error (RMSE) values compared. Wells and PVT models are used to create a simulation. The optimization problem is mathematically formulated using stochastic optimization techniques. The new correlation is used to fit the GLPC with WOA. A set of Pareto Optimal solutions are derived. Results of WOA is compared with other two potential algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to obtain the global optimum of the distribution of a limited gas lift quantity. The advantages of WOA over PSO and GA is discussed, and the optimum gas allocation is obtained. The correlation outperforms all other models and also bring an injection gas saving of 10-15% and improved oil production to the extent of 5-10% with an automated WOA algorithm application. Compared to the traditionally applied numerical methods for gas allocation and optimization, AI meta-heuristic algorithms are considered as revolutionary methods, offering more solutions to gas allocation problems, which can solve non-linear multi-objective optimization problems quickly and more accurately with fast convergence. WOA is simpler, faster and outperforms PSO and GA and yield maximum production at minimum cost.