Emission Reduction in Oil & Gas Subsurface Characterization Workflow with AI/ML Enabler

• Autorzy: Thanh Thuy Nguyen Thi , Lee Samie , Nguyen The , Duyen Le Quang

Identyfikatory

DOI

10.29227/IM-2023-02-43

Warianty tytułu

PL

Redukcja emisji w procesie wydobycia ropy i gazu za pomocą modułu AI/ML

• Konferencja: POL-VIET 2023 — the 7th International Conference POL-VIET
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

CO2 emission; net zero carbon; machine learning; CCUS; digital transformation; emission reduction; digital subsurface workflow

PL

emisja CO2; transformacja cyfrowa; redukcja

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2023
• Tom: no. 2, vol. 1
• Strony: 289--294
• Opis fizyczny: Bibliogr. 21 poz., zdj.

Twórcy

autor

Thanh Thuy Nguyen Thi

• SLB: Schlumberger Vietnam Services - 7th Floor, Havana Tower, 132 Ham Nghi Street, Ben Thanh Ward, Ho Chi Minh City, Vietnam
• Schlumberger Oilfield Support Sdn. Bhd - Wisma Rohas Purecircle, No.9 Jalan P.Ramlee, 50250 Kuala Lumpur, Malaysia

autor

Lee Samie

• SLB: Schlumberger Vietnam Services - 7th Floor, Havana Tower, 132 Ham Nghi Street, Ben Thanh Ward, Ho Chi Minh City, Vietnam
• Schlumberger Oilfield Support Sdn. Bhd - Wisma Rohas Purecircle, No.9 Jalan P.Ramlee, 50250 Kuala Lumpur, Malaysia

autor

Nguyen The

• SLB: Schlumberger Vietnam Services - 7th Floor, Havana Tower, 132 Ham Nghi Street, Ben Thanh Ward, Ho Chi Minh City, Vietnam
• Schlumberger Oilfield Support Sdn. Bhd - Wisma Rohas Purecircle, No.9 Jalan P.Ramlee, 50250 Kuala Lumpur, Malaysia

autor

Duyen Le Quang

• HUMG: Faculty of Petroleum and Energy, Hanoi University of Mining and Geology, No.18 Vien Street - Duc Thang Ward- Bac Tu Liem District - Ha Noi

Bibliografia

• 1. ADNOC, n.d. [Online] Available at: https://www.youtube.com/watch?v=ruElQ1oInt0
• 2. Circular Computing, n.d. [Online] Available at: https://circularcomputing.com/news/carbon-footprint-laptop/#-Carbon%20Footprint%20During%20Production%20of%20A%20Laptop
• 3. INPEX, n.d. SLB Software. [Online] Available at: https://www.software.slb.com/videos/inpex-leaders-discuss-the-digital-transformation-of-ep
• 4. McKinsey & Company, 2020. The future is now: How oil and gas companies can decarbonize.
• 5. OMV, n.d. [Online] Available at: https://www.omv.com/en/news/201217-schlumberger-and-omv-announce-enterprise-deployment-of-ai-and-digital-solutions-enabled-by-the-delfi-cognitive-ep-environment
• 6. Pertamina Hulu Mahakam, n.d. SLB Software. [Online] Available at: https://www.slb.com/resource-library/casestudy/so/Pertamina%20Hulu%20Mahakam%20utilizes%20machine%20learning%20to%20reduce%20property%20 modeling
• 7. PlanetMark, n.d. [Online] Available at: https://www.planetmark.com/how-to-measure-the-carbon-impact-of-working-from-home/
• 8. Sustainable Energy Development Authority Malaysia, n.d. [Online] Available at: https://www.seda.gov.my/statistics-monitoring/co2-avoidance/
• 9. Woodside Energy, n.d. SLB Software. [Online] Available at: https://www.software.slb.com/videos/schlumberger-announces-enterprise-wide-deployment-of-delfi-environment-for-woodside-energy
• 10. Thuy Nguyen Thi Thanh et al., EAGE Conference on Digital Innovation for a Sustainable Future, 13-15 September 2022, Bangkok. Available at: Reinventing Digital Subsurface of Operator through DELFI PTS cloud digital solution at Enterprise Scale | Earthdoc
• 11. Joy Ugoyah and Anita Mary Igbine (2021), University of Port Harcourt, SPE-207152. Available at: Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes | Request PDF (researchgate.net)
• Other Case Studies Avaialble at:
• 1. Redefining seismic interpretation - Machine learning for fault interpretation, enhancing efficiency, accuracy and auditability through a cloud-based approach
• 2. Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand
• 3. Application of Machine Learning-Assisted Fault Interpretation on Large Carbonate Field with Subtle Throws
• 4. Large Fault Extraction Using Point Cloud Approach to a Seismic Enhanced Discontinuity Cube
• 5. Redefining seismic interpretation: Enhancing fault interpretation efficiency and accuracy with Deep Convolutional Neural Network and elastic cloud compute
• 6. [2011.05561] An application of an Embedded Model Estimator to a synthetic non-stationary reservoir model with multiple secondary variables (arxiv.org)
• 7. Tight Integration of Decision Forests into Geostatistical Modelling | Earthdoc
• 8. Walawska B., Szymanek A, The effect of structure modification of sodium compounds on the efficiency in removing SO2 and HCl from fumes in the conditions of circulating fluid bed, Chemical and Biochemical Engineering Quarterly 31 (3) 261–273 (2017)
• 9. Szymanek A., Nowak W. Mechanically activated limestone, Chemical and process engineering, 28,127-137, 2007. ISSN0208-6425 IF 0,394
• 10. Szymanek A., de las Obras-Loscertales M., Pajdak A.Effect of sorbent reactivity on flue gas desulphurization in fluidized-bed boilers under air firing mode. The Canadian Journal of Chemical Engineering, Volume 96, April 2018, 895-902

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)