| Popis: |
Decisions made during the reservoir development phase have major impact on the project economics. Due to reservoir complexity and severe nonlinear interactions between decision parameters, automated optimization algorithms may be needed to supplement intuitive reservoir engineering analysis. Previous algorithms, proposed for well placement optimization, search for optimum well locations without full consideration of engineering knowledge and practice. This may result in degradation of optimization efficiency (number of simulations) and output unrealistic suboptimal well configurations. This study proposes an approach that superimposes engineering knowledge and practice on an automated optimization routine (the Hybrid Genetic Algorithm, HGA) to maximize the value and efficiency of field development optimization. The new approach is called the Fixed Pattern Approach (FPA), since it enforces the HGA to locate thewells on a user defined pattern inspiring from the idea that the wells are more likely to be drilled in specific patterns (line, staggered-line, five-spot etc.). The FPA essentially incorporates reservoir engineering concepts within the parameterization of optimization problem and reduces the search-space size considerably by eliminating the simulation of infeasible development scenarios. The FPA was applied to a deepwater field to get a quick understanding about the possible optimum development scenarios. In the scope of this study, finding the optimum development plan was defined as determining the optimum number and location of producers and injectors, as well as production rates and the starting time of injection that maximizes the Net Present Value (NPV) of the project. These parameters were optimized simultaneously using a black oil reservoir simulator as the objective function evaluator. Comparison with respect to the conventional window approach showed that FPA significantly reduced the CPU time and resulted in practical and economical well locations. Uncertainty in the geological properties (porosity, permeability) and reservoir structure was also addressed for this field by performing optimization on five different models constructed by a Plackett-Burman experimental design. The new methodology recommended an optimum field development plan while taking the uncertainty of property distributions and reservoir structure into account. |
