A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow. (October 2021)
- Record Type:
- Journal Article
- Title:
- A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow. (October 2021)
- Main Title:
- A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow
- Authors:
- You, Junyu
Ampomah, William
Morgan, Anthony
Sun, Qian
Huang, Xiaoliang - Abstract:
- Highlights: A comprehensive techno-eco-assessment of an active CO2 -EOR project is carried out. A ML-based protocol is developed to optimize oil recovery, CO2 storage and NPV. Average reservoir pressure is utilized as an optimization constraint. Effects of CO2 credit and oil price on Pareto optimum results are studied. Abstract: Carbon dioxide enhanced oil recovery (CO2 -EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. 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 ofHighlights: A comprehensive techno-eco-assessment of an active CO2 -EOR project is carried out. A ML-based protocol is developed to optimize oil recovery, CO2 storage and NPV. Average reservoir pressure is utilized as an optimization constraint. Effects of CO2 credit and oil price on Pareto optimum results are studied. Abstract: Carbon dioxide enhanced oil recovery (CO2 -EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. 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. … (more)
- Is Part Of:
- International journal of greenhouse gas control. Volume 111(2021)
- Journal:
- International journal of greenhouse gas control
- Issue:
- Volume 111(2021)
- Issue Display:
- Volume 111, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 111
- Issue:
- 2021
- Issue Sort Value:
- 2021-0111-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-10
- Subjects:
- CCUS -- CO2-EOR -- Multi-objective optimization -- Economics assessment -- Machine learning
Greenhouse gases -- Environmental aspects -- Periodicals
Air -- Purification -- Technological innovations -- Periodicals
Gaz à effet de serre -- Périodiques
Gaz à effet de serre -- Réduction -- Périodiques
Air -- Purification -- Technological innovations
Greenhouse gases -- Environmental aspects
Periodicals
363.73874605 - Journal URLs:
- http://rave.ohiolink.edu/ejournals/issn/17505836/ ↗
http://www.sciencedirect.com/science/journal/17505836 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ijggc.2021.103480 ↗
- Languages:
- English
- ISSNs:
- 1750-5836
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.268600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 19618.xml