(147f) Optimization of CO2-Enhanced Oil Recovery with CO2 Storage in a Mature Oil Field
After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. The PRSM utilized an objective function that maximized both oil recovery and CO2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO2 purchase, CO2 recycle and adding infill wells and/or patterns as well as compressor capacity. The PRSM proxy model was âtrainedâ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO2 storage.
The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO2 storage within partially depleted oil reservoirs such as FWU.
This study may serve as a benchmark for potential CO2âEOR projects in the Anadarko basin and/or geologically similar basins throughout the world.