1887

Abstract

Summary

Oil and gas companies are getting increasing capabilities to generate more value-adding data with the ongoing shift to both large in-house and cloud-based computation and storage facilities. It is easy to get lost in the digitization journey and get blinded by all the computational power that unveils with high performance computing. The question then becomes: how do we transform the digital footprint of all this data into something that is easy to relate to for decision makers? In other words, what does sensible, aggregated decision support data look like that adds value and justifies the production and collection of all the digital data that is and will be available in the future? What is noise, what is nice to have, and most importantly, what are the key findings in the data?

The abstract discussed how the information gathered from ensemble-based can be utilized for better decision making by adding value compared to traditional focus by considering uncertainties as an integral part of the modelling process.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202239074
2022-03-23
2024-04-20
Loading full text...

Full text loading...

References

  1. Emerick, A. A. and Reynolds, A. C.
    [2013]. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55, 3–15.
    [Google Scholar]
  2. Evensen, G., Hove, J., Meisingset, H., Reiso, E., Seim, K. S., and Espelid, Ø.
    [2007]. Using the EnKF for assisted history matching of a North Sea reservoir model. In: Proceedings of the SPE reservoir simulation symposium. Society of Petroleum Engineers. SPE-106184-MS.
    [Google Scholar]
  3. Khan, A., Alqallabi, S. A., Al-Jenaibi, F. S., Gacem, M. T., Adli, M., Phade, A. A., Skorstad, A., Mansur, S. and Malla, L.
    Demonstrating flexibility and cost-efficiency of integrated ensemble-based modeling - one approach on three reservoirs. [2021]. In: Proceedings of the Abu Dhabi International Petroleum Conference and Exhibition. SPE-207738-MS.
    [Google Scholar]
  4. Oliver, D. S., and Chen, Y.
    [2011]. Recent progress on reservoir history matching: a review. Computational Geosciences, 15(1), 185–221.
    [Google Scholar]
  5. Sebacher, B., Hanea, R., and Heemink, A.
    [2013]. A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF). Computational Geosciences, 17(5), 813–832.
    [Google Scholar]
  6. Sætrom, J., Phade, A., Vinther, M. L., & Munck, T. F.
    [2016]. Consistently integrating static and dynamic data in the facies model description using an ensemble based approach. In: Proceedings of the International Petroleum Technology Conference. IPTC-18868-MS.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202239074
Loading
/content/papers/10.3997/2214-4609.202239074
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error