TITLE:
Algorithms for the Optimization of Well Placements—A Comparative Study
AUTHORS:
Stella Unwana Udoeyop, Innocent Oseribho Oboh, Maurice Oscar Afiakinye
KEYWORDS:
Artificial Bee Colony, Optimization, Well Placement, Stochastic Algorithm, Particle Swarm Optimization
JOURNAL NAME:
Advances in Chemical Engineering and Science,
Vol.8 No.2,
April
26,
2018
ABSTRACT: The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms
for optimization that has been written for solving constrained and unconstrained
optimization problems. This novel optimization algorithm is very efficient
and as promising as it is; it can be favourably compared to other optimization
algorithms and in some cases, it has been proven to be better than
some known algorithms (like Particle Swarm Optimization (PSO)), especially
when used in Well placement optimization problems that can be encountered
in the Petroleum industry. In this paper, the ABC algorithm has been modified
to improve its speed and convergence in finding the optimum solution to
a well placement optimization problem. The effects of variations of the control
parameters for both algorithms were studied, as well as the algorithms’
performances in the cases studied. The modified ABC (MABC) algorithm
gave better results than the Artificial Bee Colony algorithm. It was noticed
that the performance of the ABC algorithm increased with increase in the
number of its optimization agents for both algorithms studied. The modified
ABC algorithm overcame the challenge posed by the use of uniformly generated
random numbers with very rough NPV surface. This new modified ABC
algorithm proposed in this work will be a great tool in optimization for the
Petroleum industry as it involves Well placements for optimum oil production.