Optimization of well locations in tight oil reservoir based on genetic algorithm

In recent years, energy demand has constantly grown worldwide, promoting the oil and gas sector to develop innovative solutions for improving productivity from unconventional reservoirs such as tight oil reservoirs. One of the significant issues in this area is determining the optimal well locations to maximize net present value (NPV). This procedure involves analysing several factors such as reservoir geometry, permeability, porosity distribution or fluids contact, and other factors that affect locations and number of infill wells. The difficulty of these considerations, combined with economic concerns and reservoir related risks, makes it even more challenging to identify the optimum development program for a given field. In this context, this study provides a useful optimization approach for identifying the optimal well location in the Halfaya oil field, a southern Iraqi tight oil field. This approach aims to overcome the issues related to optimizing reservoir development. This study employed the Genetic Algorithm as the main optimization engine due to its effectiveness in solving multidimensional and nonlinear problems. Multiple scenarios were developed with specified well configurations to identify the best scenario for maximizing NPV. This involves conducting multiple optimization runs using the Petrel/Eclipse software to develop a reliable field plan. Consequently, cumulative production for this oilfield has been comprehensively defined and reviewed. The results showed that the genetic algorithm gives acceptable values in optimization problems with relatively few decision variables. This led to an increase in NPV by 5.63%, 19.63 % and 29.30% for scenarios of one, three and five infill wells, respectively.