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A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization.

Kallol Biswas1, Amril Nazir2, Md Tauhidur Rahman3

  • 1Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia.

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|January 27, 2022
PubMed
Summary
This summary is machine-generated.

A new modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) enhances oil and gas wellbore trajectory optimization. This method improves solution distribution and search capability, leading to more efficient and safer drilling operations.

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Area of Science:

  • Engineering
  • Computer Science
  • Petroleum Engineering

Background:

  • Wellbore trajectory optimization is crucial for cost and safety in the oil and gas industry.
  • Existing metaheuristic algorithms face challenges like local optima and poor solution distribution.

Purpose of the Study:

  • To propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing wellbore trajectory.
  • To enhance optimization by integrating cellular automata (CA) and particle swarm optimization (PSO) into the spotted hyena optimizer (SHO).

Main Methods:

  • The MOCSHOPSO algorithm integrates CA for enhanced exploration and PSO for improved hunting mechanisms.
  • Optimization targets true measured depth, well profile energy, and torque, considering geophysical and operational constraints.
  • Performance is evaluated against standard methods (MOCPSO, MOSHO, MOCGWO) using statistical metrics (IGD, MS, SP, ER).

Main Results:

  • MOCSHOPSO demonstrated superior performance with better non-dominated solution distribution and search capability.
  • The algorithm achieved lower IGD, SP, and ER values, indicating improved optimization accuracy.
  • An adaptive neighborhood mechanism further enhanced performance compared to fixed topologies.

Conclusions:

  • The proposed MOCSHOPSO algorithm offers significant improvements for wellbore trajectory optimization.
  • The integration of CA and PSO effectively addresses limitations of existing metaheuristic approaches.
  • This novel algorithm shows promise for advancing efficiency and safety in oil and gas operations.