A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow
Abstract
Carbon dioxide enhanced oil recovery (CO2-EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. Here, this study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO2-EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO2 (CO2-WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.
- Authors:
-
- Chongqing Univ. of Science and Technology (China); New Mexico Institute of Mining and Technology, Socorro, NM (United States)
- New Mexico Institute of Mining and Technology, Socorro, NM (United States)
- New Mexico Institute of Mining and Technology, Socorro, NM (United States); China University of Geosciences (Beijing) (CUGB), (China)
- Chongqing Univ. of Science and Technology (China)
- Publication Date:
- Research Org.:
- New Mexico Institute of Mining and Technology, Socorro, NM (United States)
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE)
- OSTI Identifier:
- 1977177
- Grant/Contract Number:
- FC26-05NT42591
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Journal of Greenhouse Gas Control
- Additional Journal Information:
- Journal Volume: 111; Journal Issue: C; Journal ID: ISSN 1750-5836
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 03 NATURAL GAS; CCUS; CO2-EOR; Multi-objective optimization; Economics assessment; Machine learning
Citation Formats
You, Junyu, Ampomah, William, Morgan, Anthony, Sun, Qian, and Huang, Xiaoliang. A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow. United States: N. p., 2021.
Web. doi:10.1016/j.ijggc.2021.103480.
You, Junyu, Ampomah, William, Morgan, Anthony, Sun, Qian, & Huang, Xiaoliang. A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow. United States. https://doi.org/10.1016/j.ijggc.2021.103480
You, Junyu, Ampomah, William, Morgan, Anthony, Sun, Qian, and Huang, Xiaoliang. Fri .
"A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow". United States. https://doi.org/10.1016/j.ijggc.2021.103480. https://www.osti.gov/servlets/purl/1977177.
@article{osti_1977177,
title = {A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow},
author = {You, Junyu and Ampomah, William and Morgan, Anthony and Sun, Qian and Huang, Xiaoliang},
abstractNote = {Carbon dioxide enhanced oil recovery (CO2-EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. Here, this study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO2-EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO2 (CO2-WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.},
doi = {10.1016/j.ijggc.2021.103480},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 111,
place = {United States},
year = {Fri Oct 08 00:00:00 EDT 2021},
month = {Fri Oct 08 00:00:00 EDT 2021}
}
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