In upstream oil and gas industry, infill drilling is a vital practice to increase the productivity as well as recovery factor of hydrocarbons. It concerns with the implementation of accurate and reliable reservoir models in order to evaluate the reservoir behaviour response effectively and be able to predict its future performance. Given the nature of reservoir response of naturally fractured reservoirs, optimal well locations are extremely important to ensure the economic viability of infill drilling programs. However, optimal placement of infill wells in fractured fields is challenging.
In this study a reservoir modelling-optimization workflow for fractured reservoir is developed. Firstly, an integrated static and dynamic reservoir model has been developed and validated using history matching process. For the purpose of maximizing economic recovery, an infill well placement optimization project has been considered for this field to find the best possible locations of infill wells, two different optimization approaches were adopted and implemented on reservoir model. The first method is an exhaustive method which uses the concept of design of experiment to search all grids available in the model in order to locate the best possible well locations. The second method is automatic optimization using Genetic Algorithm. That depends on the principle of natural selection as proposed by Darwin
The genetic program was coupled with the reservoir flow model to re-evaluate the chosen wells at each iteration until obtaining the optimal choice. The proposed location of wells has improved Net Present Value (NPV) by + 10% higher than the base case without infill wells. Examining two different optimization approaches used in this work, the genetic algorithm program gave results similar to the results that were obtained by an exhaustive method with much less computation time which is a great issue mainly for large size fields or fields which possess condensate gas and require the use of compositional simulators.