Fast Integrated Reservoir Modelling on the Gjøa Field Offshore Norway
- Jon Sætrom (Resoptima AS) | Eirk Morell (Resoptima AS) | Reza Rostami Ravari (Lise Schiøtz) | Claire Le Maitre (Lise Schiøtz) | Mailin Seldal (Engie E&P Norge AS)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 13-16 November , Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 3.3 Well & Reservoir Surveillance and Monitoring, 3 Production and Well Operations, 5.5 Reservoir Simulation, 7.6.6 Artificial Intelligence, 3.3.6 Integrated Modeling, 5 Reservoir Desciption & Dynamics, 5.1.5 Geologic Modeling
- Uncertainty Quantification, Reservoir Modelling, Data Conditioning, Prescriptive Analytics, Machine Learning
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- 345 since 2007
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In this paper, we combine reservoir data; reservoir physics; machine learning algorithms; and the "know-how" of the subsurface team in one efficient workflow to quickly build reservoir models that lead to an improved understanding of the Gjøa field offshore Norway. Key elements of the solution include:
Consistent integration of static and dynamic data – when they arrive – in one automated and repeatable modelling process.
Ability to address uncertainty in all parts of the modelling chain – from G&G data interpretation to flow simulation and dynamic data conditioning.
Single platform for multidisciplinary collaboration between the subsurface disciplines in the modelling and reservoir management efforts.
Scalability and robustness – the workflow is equally applicable for small and large reservoirs.
Additionally, we implement a prescriptive analytics solution to quickly identify and rank robust infill-well targets using data conditioned reservoir models as input.
The amount of data we collect from a reservoir is rapidly increasing. Hence, it is more important than ever to utilize tools that address the shortcomings of the traditional reservoir modelling approaches. Having the ability to capture the information found in the collected static and dynamic data in a consistent manner when they arrive, while embracing the inherent uncertainty of the reservoir modelling process, will lead to improved reservoir management decisions, and ultimately an increase in the recoverable volume.
|File Size||1 MB||Number of Pages||14|
Abadpour, A.,Adejare, M.,Chugunova, T., & Deboaisne, R. (2017). Characterization and Uncertainty Reduction on Facies Distribution and Probability Cubes with Ensemble Kalman Filter History Matching. SPE Reservoir Characterisation and Simulation Conference and Exhibition, 8–10 May. Abu Dhabi, UAE: SPE.
Halset, G.,Arrigoni, V.,Panfili, P.,Tveit, J.-A., & Sætrom, J. (2017). Integrated Software Tool Brings Speed, Reliability to Reservoir Modeling on Barents Sea Project. Retrieved from Journal of Petroleum Technology: https://www.spe.org/en/jpt/jpt-article-detail/?art=2909
Marcus, G. (2012). Is "Deep Learning" a Revolution in Artificial Intelligence? Retrieved from The New Yorker: https://www.newyorker.com/news/news-desk/is-deep-learning-a-revolution-in-artificial-intelligence
Maucec, M.,De Matos Ravanelli, F. M.,Lyngra, S.,Zhang, S. J.,Alramadhan, A. A.,Abdelhamid, O. A., & Al-Garni, S. A. (2016). Ensemble-Based Assisted History Matching With Rigorous Uncertainty Quantification Applied to a Naturally Fractured Carbonate Reservoir. SPE Annual Technical Conference and Exhibition, 26-28 September. Dubai, UAE: SPE.