Co-optimization of CO2-EOR and Storage Processes under Geological Uncertainty. (July 2017)

• Record Type: Journal Article
• Authors: Ampomah, William; Balch, Robert; Will, Robert; Cather, Martha; Gunda, Dhiraj; Dai, Zhenxue
• Is Part Of: Energy procedia. Volume 114(2017)
• Page Start: 6928
• Page End: 6941
• Publication Date: 2017-07
• Subjects: Reservoir simulation -- co-optimization -- CO2 storage -- Enhanced oil recovery -- neural network -- reduce order model; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.1835
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: This paper presents an integrated numerical framework to co-optimize EOR and CO2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO2 storage in the FWU. The optimization process predicted more than 94% of CO2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO2 storage. The Farnsworth CO2 project will serve as a benchmark for future CO2 –EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.