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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm.

Mehdi Darbandi1, Maytham N Meqdad2, Ahmad Hammoud3,4

  • 1Pôle Universitaire Léonard de Vinci, Paris, France.

Scientific Reports
|December 27, 2024
PubMed
Summary
This summary is machine-generated.

This study optimizes gas injection for oil wells using a hybrid Particle Swarm Optimization and Atom Search Optimization algorithm with IoT data. The method improves energy efficiency and gas utilization, reducing costs.

Keywords:
Atom search optimizationFuzzyGas lift allocationInternet of thingsMulti-objective optimization

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Well productivity declines with reservoir depletion, necessitating artificial lift techniques like gas injection.
  • Gas scarcity requires optimized allocation to balance oil production with minimal gas usage.
  • Conventional gas allocation strategies struggle with real-world non-linear constraints and gas supply limitations.

Purpose of the Study:

  • To develop an innovative, optimized gas allocation strategy for petroleum extraction.
  • To leverage Internet of Things (IoT) technology for real-time data acquisition and adaptive optimization.
  • To enhance oil recovery efficiency while minimizing gas consumption and energy usage.

Main Methods:

  • A hybrid optimization algorithm combining Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO).
  • Integration of IoT for real-time data acquisition and processing.
  • Implementation of dynamic parameters and adaptive force constants for enhanced exploration and exploitation.

Main Results:

  • Significant improvements in energy efficiency (12.12% reduction) and gas utilization (18.05% reduction).
  • Demonstrated enhancements in battery life (7.67%) and cost reduction (9.48%) compared to existing methods.
  • The hybrid PSO-ASO approach provides precise and adaptive optimization for gas allocation.

Conclusions:

  • The proposed hybrid optimization technique effectively addresses gas scarcity in petroleum extraction.
  • IoT integration enables adaptive and efficient real-time gas allocation strategies.
  • The method offers substantial economic and operational benefits through improved resource management.