Optimizing Well Placement with Genetic Algorithms: A Case Study

Haider A. Mahmood; Omar Faleh Al-Fatlawi; Mohammed Ahmed Al-Janabi; Dhifaf Jaffar Sadeq; Yousif Mohammed  Al-Jumaah

doi:10.52716/jprs.v14i4.895

Authors

Haider A. Mahmood Iraqi Ministry of Oil, Iraq
Omar Faleh Al-Fatlawi Department of Petroleum Engineering Department, College of Engineering, University of Baghdad, Iraq
Mohammed Ahmed M. Al-Janabi Iraqi Ministry of Oil, Iraq
Dhifaf Jaffar Sadeq Department of Petroleum Engineering Department, College of Engineering, University of Baghdad, Iraq
Yousif Mohammed Al-Jumaah Iraqi Ministry of Oil, Iraq

DOI:

https://doi.org/10.52716/jprs.v14i4.895

Keywords:

Well Placement, Optimization, Genetic Algorithm, Infill Drilling

Abstract

Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a type of stochastic optimization technique that has proven effective in solving various problems. The results of the study show significant improvements in NPV when using genetic algorithms compared to traditional methods, particularly for problems with numerous decision variables. The findings suggest that genetic algorithms are a promising tool for optimizing well placement in oil field development, improving NPV, and reducing the risk of project failure.

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