Ensemble Based Optimization of EOR Processes Conference Paper uri icon

abstract

  • AbstractThe advent of smart well technology has allowed the control of a hydrocarbon field in all stages of production. This holds great promise in managing EOR processes, especially in terms of applying optimization techniques. However, some procedures for optimizing EOR processes are not based on the physics of the process, which can lead to erroneous results. Additionally, optimization of EOR processes can be difficult if there is no access to the simulator code for computation of the adjoints used for optimization. This work is a general procedure for designing an initial starting point for a surfactant flood and water flood optimization. The method does not rely on a simulator's adjoint computation. Instead of using adjoints for optimization, the Ensemble Kalman Filter optimization (Enkfopt) was developed and used to optimize the net present value (NPV) of a 5 spot surfactant flood and water flood process. Additionally, constrained optimization was created and added to the Enkfopt method. The controls were based on surfactant flood and water flood process dynamics and included production control for four producers, injector injection rate, and surfactant concentration. Field permeability, field porosity, and economic inputs were parameters held constant for the optimization. Once the optimal solution was obtained, multiple realizations of the permeability and economic inputs were used to generate a cumulative probability distribution of the NPV. Preliminary results show an improvement of the NPV of the water flood process (up to 60% increase) and surfactant flood process (up to 150% increase). Results also show that the optimized controls retain the same relationship as the original controls. This work provides a method to manage risk by performing an optimization and then using the optimal solution to assimilate possible geological and economic scenarios. Cumulative distribution curves of NPV provide tools in accessing the probability of project success.

author list (cited authors)

  • Odi, U. O., & Lane, R. H.

publication date

  • January 1, 2010 11:11 AM

publisher

  • SPE  Publisher