- Tytuł:
- Emission Reduction in Oil & Gas Subsurface Characterization Workflow with AI/ML Enabler
- Autorzy:
-
Thanh, Thuy Nguyen Thi
Lee, Samie
Nguyen, The
Duyen, Le Quang - Powiązania:
- https://bibliotekanauki.pl/articles/27323253.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polskie Towarzystwo Przeróbki Kopalin
- Tematy:
-
CO2 emission
net zero carbon
machine learning
CCUS
digital transformation
emission reduction
digital subsurface workflow
emisja CO2
transformacja cyfrowa
redukcja - Opis:
- According to (McKinsey & Company, 2020), drilling and extraction operations are responsible for 10% of approximately 4 billion tons of CO2 emitted yearly by Oil and Gas sector. To lower carbon emissions, companies used different strategies including electrifying equipment, changing power sources, rebalancing portfolios, and expanding carbon-capture-utilization-storage (CCUS). Technology evolution with digital transformation strategy is essential for reinventing and optimizing existing workflow, reducing lengthy processes and driving efficiency for sustainable operations. Details subsurface studies take up-to 6–12 months, including seismic & static analysis, reserve estimation and simulation to support drilling and extraction operations. Manual and repetitive processes, aging infrastructure with limited computing-engine are factors for long computation hours. To address subsurface complexity, hundred-thousand scenarios are simulated that lead to tremendous power consumption. Excluding additional simulation hours, each workstation uses 24k kWh/month for regular 40 hours/month and produces 6.1kg CO2. Machine Learning (ML) become crucial in digital transformation, not only saving time but supporting wiser decision-making. An 80%-time-reduction with ML Seismic and Static modeling deployed in a reservoir study. Significant time reduction from days-tohours-to-minutes with cloud-computing deployed to simulate hundreds-thousands of scenarios. These time savings help to reduce CO2-emissions resulting in a more sustainable subsurface workflow to support the 2050 goal.
- Źródło:
-
Inżynieria Mineralna; 2023, 2; 289--294
1640-4920 - Pojawia się w:
- Inżynieria Mineralna
- Dostawca treści:
- Biblioteka Nauki
Artykuł