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Abstract

Summary

Introduction:

Sustainability is increasingly considered a key strategic driver across all industries including oil and gas, and its upstream sector. We continuously thrive to maximize hydrocarbon production while minimizing the associated carbon footprint. In water flooding oilfield operations, the primary driver of carbon emissions is actually water – usage, production, and disposal. The required energy to transport and process the water is considerable, and it is therefore the major carbon emitter. Forecasting carbon emissions from oilfield operations challenges our ability to optimize field development plans in light of carbon footprint besides profit or recovery.

Method:

In this work, we present an innovative approach for forecasting the carbon footprint of a reservoir in terms of the associated development and production activities. We use an advanced nonlinear autoregressive neural network approach integrated with time-lapse electromagnetic data to forecast the carbon emissions from the reservoir in real-time under uncertainty. Within this artificial intelligence (AI) framework, we also incorporate the ability to study the adoption of a circular carbon approach. For this scenario, the AI framework allocate the reinjection of produced greenhouse gases while adjusting water injection levels and forecasts the impact of such circular development plan.

Results:

We tested the framework on a synthetic reservoir encompassing a complex fracture system and well setup. The carbon emissions were forecasted in real-time based on the previous production levels and the defined injection levels. The forecasted carbon emissions were then integrated into an optimization technique in order to adjust injection levels to minimize water cut and overall carbon emissions, while optimizing production levels. Results were promising and highlighted the potential significant reductions in carbon emissions for the studied synthetic reservoir. Moreover, the deployment of deep electromagnetic surveys was found particularly beneficial as a deep formation evaluation method for tracking the injected waterfront inside the reservoir and optimizing the sweep efficiency. Accordingly, such integrated AI approach has a twofold benefit: maximizing the hydrocarbon productivity, while minimizing the water consumption and associated carbon emissions.

Future Outlook:

Such framework represents a paradigm shift in reservoir management and improved oil recovery operations under uncertainty. It proposes an innovative technique to reduce the carbon footprint and attain at real-time an efficient circular injection development plan.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202133073
2021-04-19
2024-04-26

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References

  1. Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z.
    (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling.Journal of Petroleum Science and Engineering, 174, 776–789.
     [Google Scholar]
  2. Bender, S., & Akin, S.
    (2017). Flue gas injection for EOR and sequestration: Case study.Journal of Petroleum Science and Engineering, 1033–1045.
     [Google Scholar]
  3. Bennaceur, K.
    (2019). How the Oil and Gas Industry Is Contributing to Sustainability.Journal of Petroleum Technology, 71(3), 38–39.
     [Google Scholar]
  4. Dai, Z., Middleton, R., Viswanathan, H., Fessenden-Rahn, J., Bauman, J., Pawar, R., … McPherson, B.
    (2014). An integrated framework for optimizing CO2 sequestration and enhanced oil recovery.Environmental Science & Technology Letters, 49–54.
     [Google Scholar]
  5. Hassan, A., Elkatatny, S., & Abdulraheem, A.
    (2019). Intelligent prediction of minimum miscibility pressure (MMP) during CO2 flooding using artificial intelligence techniques.Sustainability, 11(24), 7020.
     [Google Scholar]
  6. Heucke, U.
    (2015). Nitrogen Injection as IOR/EOR Solution For North African Oil Fields.SPE North Africa Technical Conference and Exhibition, 14–16 September, Cairo, Egypt. Cairo: Society of Petroleum Engineers.
     [Google Scholar]
  7. Katterbauer, K., & Marsala, A.
    (2020). A novel 4IR framework for interwell saturation mapping.82nd EAGE Annual Conference & Exhibition.
     [Google Scholar]
  8. (2020). A Novel 4IR Framework for Interwell Saturation Mapping.82nd EAGE Annual Conference & Exhibition (pp. 1–5). Amsterdam: EAGE.
     [Google Scholar]
  9. (2020). A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs.Arabian Journal for Science and Engineering, 1–7.
     [Google Scholar]
  10. Katterbauer, K., Al-Yousif, A., & Marsala, A.
    (2020). Intelligent Reconciliation of Well Logs-A Pathway Towards 4IR Assisted Log Interpretation.Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers.
     [Google Scholar]
  11. Katterbauer, K., Hoteit, I., & Sun, S.
    (2015). History Matching of Electromagnetically Heated Reservoirs Incorporating Full-Wavefield Seismic and Electromagnetic Imaging.SPE Journal, 20(5), 923–941.
     [Google Scholar]
  12. Katterbauer, K., Marsala, A., Maucec, M., Zhang, Y., & Hoteit, I.
    (2020). History Matching of Time-Lapse Deep Electromagnetic Tomography with A Feature Oriented Ensemble-Based Approach.ECMORXVII. Edingburgh.
     [Google Scholar]
  13. Katterbauer, K., Marsala, A., Schoepf, V., & Donzier, E.
    (2021). A novel artificial intelligence automatic detection framework to increase reliability of PLT gas bubble sensing.Journal of Petroleum Exploration and Production, 1–11.
     [Google Scholar]
  14. Kumar, J., Agrawal, P., & Draoui, E.
    (2017). A Case Study on Miscible and Immiscible Gas-Injection Pilots in a Middle East Carbonate Reservoir in an Offshore Environment.SPE Reservoir Evaluation & Engineering, 11.
     [Google Scholar]
  15. Lawrence, J., Maer, N., Stern, D., Corwin, L., & Idol, W.
    (2002). Jay Nitrogen Tertiary Recovery Study: Managing a Mature Field.Abu Dhabi International Petroleum Exhibition and Conference. Abu Dhabi: Society of Petroleum Engineers.
     [Google Scholar]
  16. Luis, F., Al Hammadi, K., & Tanakov, M.
    (2016). Case Study of C02 Injection to Enhance Oil Recovery into the Transition Zone of a Tight Carbonate Reservoir.Abu Dhabi International Petroleum Exhibition & Conference, 7–10 November, Abu Dhabi, UAE. Abu Dhabi: Society of Petroleum Engineers.
     [Google Scholar]
  17. Nengkoda, A., Mirza, M., Ghozali, F., Susandi, I., & Al Wardi, A.
    (2011). Trending of H2S from Reservoir After Acidizing from Well Contained SRB Bacteria: Souring Case Study in X Field Southern Oman.SPE Project and Facilities Challenges Conference at METS, 13–16 February. Doha: Society of Petroleum Engineers.
     [Google Scholar]
  18. Şükrü, M.
    (2019). Analysis of the effect of experimental adsorption uncertainty on CH4 production and CO2 sequestration in Dadas shale gas reservoir by numerical simulations.Journal of Petroleum Science and Engineering, 1051–1066.
     [Google Scholar]
  19. Wang, S., & Chen, S.
    (2019). Insights to fracture stimulation design in unconventional reservoirs based on machine learning modeling.Journal of Petroleum Science and Engineering, 682–695.
     [Google Scholar]
  20. You, J., Ampomah, W., & Sun, Q.
    (2020). Development and application of a machine learning based multi-objective optimization workflow for CO2-EORprojects.Fuel, 264, 116758.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202133073
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Carbon footprint forecasting of IOR activities via an intelligent NARX framework for promoting greener reservoir management
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202133073
/content/papers/10.3997/2214-4609.202133073
/content/papers/10.3997/2214-4609.202133073
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