Abstract
In the realm of petroleum extraction, well productivity declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this, artificial lift techniques are employed, with gas injection being a prevalent method. Ideally, unrestricted gas injection could maximize oil output. However, gas scarcity necessitates judicious resource management to optimize oil production while minimizing gas usage. Gas injection serves to alleviate hydrostatic pressure within wells, thereby enhancing oil recovery. Conventional gas allocation strategies often prove inadequate when confronted with the complex, non-linear constraints of real-world scenarios, particularly under gas supply limitations. This research introduces an innovative approach to gas allocation optimization, leveraging Internet of Things (IoT) technology in conjunction with advanced computational methods. The study melds two optimization algorithms: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO). This hybrid technique harnesses IoT capabilities for real-time data acquisition and processing, enabling more precise and adaptive optimization. The proposed methodology incorporates PSO's individual and collective learning mechanisms into the ASO framework, accelerating the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. The algorithm's efficacy is further enhanced by implementing an adaptive force constant for each "atom" (solution candidate), which evolves based on the atom's performance over successive iterations. Empirical evaluation of this novel approach demonstrated significant improvements in both energy efficiency and gas utilization. Specifically, the hybrid method achieved average reductions of 12.12% in energy consumption and 18.05% in gas injection volume compared to existing techniques. Also, the results showed that battery life and cost are better than the other methods and have been improved by an average of 7.67% and 9.48%, respectively.