Optimization of Nonconventional Well Deployment
Using a Genetic Algorithm

 

SUPRI-B and SUPRI-HW are undergoing active research in the domain of optimization of nonconventional well deployment.  

A method based on a genetic algorithm (Yeten, 2003) has been developed to optimize the number, type and trajectories of nonconventional wells to be deployed. This approach takes into account the uncertainty on the reservoir model and the user’s strategy for risk management.  Economical considerations can be introduced to optimize scenarios in a financial framework.

(a)

(b)

Example of two different optimum production wells depending on the strategy toward geological uncertainty: (a) optimum for a risk neutral attitude and (b) optimum for a risk averse attitude.

NPV over five realizations for the two solutions. While the risk neutral solution (red) optimizes the expected NPV over realization, the risk averse solution (blue) considerably reduces the uncertainty on the performance. From Aitokuehi et al, 2004.