Non-Dominated Ranked Based Genetic Algorithm Multi-Objective Well Placement Optimization

In the early stages of an oilfield development finding the optimum location for injection and production well is a highly crucial task. Locating these wells in the improper locations will damage the benefit of a company in million-dollars scale. Therefore, well placement should be studied accurately. Regarding to the complicated nature of the field development plan, considering well placement optimization as a single objective optimization problem could not properly satisfy all other objectives which define along with a field development plan, therefore recently, the application of multi-objective well placement optimization is introduced. In this work, for the first time Non-Dominated Rank Based Genetic Algorithm (NRGA) is implemented in order to find the optimum well arrangement in the reservoir. Comparison between the optimized and the original model shows significant improvement in recover factor and net present value (our objectives) respectively, which indicates efficiency and power of NRGA to enhance objective's values. Moreover, the final generated Pareto front enables decision makers to select a solution which entails a full development scenario for this model. This scenario could be selected based on the highest value of net present value or the highest value of recovery factor or the balance of these two objectives.