1887

Abstract

Summary

Predicting and estimating real world conditions presents one of the major ways in which uncertainty is introduced to reservoir modelling, with errors often occurring due to the scarcity of subsurface geological information. The inherently random nature of physical phenomena is another source of uncertainty. While uncertainty due to physical phenomena cannot be reduced - because it is a state of nature - developing more accurate models and collecting additional data can help decrease estimation uncertainty. These uncertainties affect the output of reservoir models, but to what extent? This study provides a systematic way to investigate the propagation of hard data uncertainty through the estimate of original oil in place (OOIP) and recoverable oil in place (ROIP) for a given reservoir model. The reservoir is modelled using a multiple-point statistics (MPS) methodology. The results show that the projected OOIP and ROIP values are very sensitive to hard data uncertainty.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201702644
2017-12-11
2024-04-18
Loading full text...

Full text loading...

References

  1. Ang, A.H.S., and Tang, W.H.
    1984. Probability Concepts in Engineering Planning and Design, Vol. 2. Decision, Risk, and Reliability. John Wiley and Sons, New York.
    [Google Scholar]
  2. Caers, J., Zhang, T.
    2004. Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models. In: Integration of outcrop and modern analog data in reservoir models. Am. Assoc. Petrol. Geol. Memoir.80: 383–394.
    [Google Scholar]
  3. Century Geophysical Corporation. 2011. http://www.century-geo.info/dnn
    [Google Scholar]
  4. CMG
    CMG, Computer Modelling Group Ltd.2007. Calgary, Alberta, Canada.
    [Google Scholar]
  5. Duzgun, H.S.B.
    2004. Reliability-Based Design of Rock Slopes. PhD thesis, Middle East Technical University, Turkey.
    [Google Scholar]
  6. Remy, N., Boucher, A., Wu, J.
    2009Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, Cambridge, New York.
    [Google Scholar]
  7. Srinivasan, S.
    2000. Integration of production data into reservoir models: A forward modeling perspective. PhD thesis, Stanford University, USA.
    [Google Scholar]
  8. Strebelle, S.
    2000. Sequential simulation drawing structures from training images. PhD thesis, Stanford University, USA.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201702644
Loading
/content/papers/10.3997/2214-4609.201702644
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