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A New Gas Lift Allocation Method in the IoT Environment Using a Hybrid Optimization Algorithm

Source:Nature 2025-01-02 15:27:03

In the realm of petroleum extraction, well productivity inevitably declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this decline and maximize oil recovery, artificial lift techniques are employed, with gas injection being a prevalent and efficient method. However, the scarcity of gas resources necessitates judicious management to optimize oil production while minimizing gas usage.

Recently, researchers have introduced an innovative approach to gas allocation optimization in the IoT environment. This new method leverages a hybrid optimization algorithm that combines the strengths of two optimization techniques: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO).

The hybrid algorithm harnesses the IoT's capabilities for real-time data acquisition and processing. By integrating PSO's individual and collective learning mechanisms into the ASO framework, the method accelerates the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. Each "atom" (solution candidate) in the algorithm is assigned an adaptive force constant that evolves based on its performance over successive iterations.

Empirical evaluations of this novel approach have 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. These results not only highlight the effectiveness of the new method but also underscore its potential to revolutionize gas allocation strategies in the oil and gas industry.

The integration of IoT technology and advanced computational methods represents a significant advancement in the field of petroleum extraction. By enabling real-time data analysis and optimization, this new gas lift allocation method has the potential to enhance oil recovery rates and extend the economic life of oil wells.

In conclusion, the introduction of a hybrid optimization algorithm for gas lift allocation in the IoT environment marks a step forward in the efficient management of gas resources in the oil and gas industry. With its proven ability to improve energy efficiency and reduce gas usage, this new method holds promise for widespread adoption and further innovation in the field.