A Comprehensive Techno-eco-assessment of CO2 Enhanced Oil Recovery Projects Using a Machine-learning Assisted Workflow

Injection of CO2 gas to partially depleted oil reservoirs not only enhances hydrocarbon production but also take advances of the underground porous media for carbon storage purposes. Therefore, carbon utilization to enhanced oil recovery (EOR) projects become more and more attractive in the petroleum industry. Notably, the use of anthropogenic CO2 in EOR project could be challenging due to the capital and operational costs of carbon capture, transportation, and recycling. In the meantime, the US government offers encouraging tax policies, Section 45 Q, to oil field operators who inject a large volume of CO2 to produce oil. Thus, the project economics of the CO2-EOR project could be even more complex, especially when the oil price is low at this time. In this article, a robust machine-learning assisted CO2-EOR project optimization and design protocol is presented. The workflow aims at simultaneously optimizing the hydrocarbon recovery, carbon storage volume and project economics using a Pareto-front-based multiple-objective optimization (MOO) scheme. A solution repository containing various optimized development strategies will be structured. Moreover, the proposed workflow will investigate the project economics of the solutions by systematically considering impactive factors such as tax credits, project capital costs, oil prices, etc., and provide comprehensive recommendations to the field operators for decision-making purposes. This work employs data collected from the Farnsworth unit (FWU) in west Texas, US, as a field case to demonstrate the workflow. FWU is characterized as partially depleted oil sands undergoing water-alternative-CO2 (CO2-WAG) injection processes. A compositional numerical reservoir simulation model is established to investigate the fluid transportations dynamic of FWU. The numerical model is validated via a rigorous history-matching study using 55-year of primary and secondary (water flooding) recovery data, and 8-year of ternary recovery (CO2-WAG) data. It is worth emphasizing that the history-matched model successfully incorporates eight relative permeability curves to various spatial regions of the field. Such relative permeability curves are measured via laboratory investigations using representative core samples collected from the fields (hydraulic flow units). The history matched numerical model can be used to forecast the hydrocarbon production and CO2 storage volume using different CO2-WAG designs. In order to obtain finer optimization solutions, the field operational parameters, such as water injection rate, gas injection period, water injection periods, production well specifications, etc. of each individual well are considered as control parameters. In this way, the field operators can get detailed guidance to operate each individual well. However, the imposing of MOO requires computationally intensive procedures by totally relying on the high-fidelity numerical simulator, which engages the motivation to employ machine-learning-based proxies in the workflow. In this work, supported vector machine regression (SVR) combined with the Gaussian kernel is utilized to mimic the high-fidelity numerical model. The hyper-parameters of the SVR are optimized using Bayesian Optimization to achieve a better generalization performance. The SVR couples with Multi-objective Particle Swarm Optimization (MOPSO) protocol to structure the Pareto-front solution repository. To further eliminate the uncertainties introduced by the proxy error margins, a self-adapting scheme is integrated into the workflow to justify the Pareto-front solutions. This scheme will automatically add new sampling data to the training dataset for surrogate model development, making the optimization results more correct. Furthermore, this paper will carry comprehensive techno-eco-assessments of the optimized solutions considering the vital economic factors including the carbon capture and transporting costs, field operational cost, tax credits, oil price, etc. With the help of the proxy models, fast uncertainty analysis can be conducted to obtain stochastic results of the project net present value, rate of return and payback period. Field operators can make their decisions to design CO2-WAG projects based on both technical and economical perspectives.