1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
  5. Conference paper

Abstract

Summary

History matching using 4D seismic time-lapse data provide a method to manage uncertainties of multi-models, but it remains a great challenge without reaching any clear statements or practical methodology or good practise to follow. What makes the SHM so difficult is mainly the nature of the seismic data. Indeed, acquisition, interpretation, processing, make this data embedded with uncertainties, due to the physics and measurement issues. One of the challenge is to be able to extract and quantify the uncertainties carried over by the seismic data and use it to guide decisions.

The way we insert seismic data and its inherent uncertainty into the workflow is the key to further enforce progress in 4D seismic history matching. A comprehensive workflow is implemented which allows shape to estimate uncertainties using weighted factor. In the proposed history matching workflow, 4D seismic attributes are converted to binary images which are representative of fluid saturations, then binary maps are compared using a pattern-matching objective function which can capture the main feature of the seismic data. Novelty is that weighted binary maps are generated on different estimation of the uncertainty within seismic attribute, to explore and screen performance on the seismic history matching procedure. Weighted maps are associated with error/uncertainty quantification of 4D seismic signature, which allow us to identify even specific governing region of seismic reflecting fluid properties, also it shows greatest general-usage potential. An adaptive derivative free optimisation has been applied for the history matching process. Global and local properties are parameterised in the history matching loop, which includes permeability, porosity, fault transmissibility and net-to-gross.

Numerical experiments with a UKCS field shows this methodology is quite flexible and efficient, which circumvent large uncertainty in seismic data and use of uncertain petro-elastic model. This study also implies that the seismic history matching achieves a reasonable production matching while constrains saturation changes derived from time-lapse data using weighted binary seismic history matching method.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201802282
2018-09-03
2024-04-20

  • From This Site

    /content/papers/10.3997/2214-4609.201802282
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aranha, C., Tanabe, R., Chassagne, R. & Fukunaga, A.
    (2015) Optimization of oil reservoir models using tuned evolutionary algorithms and adaptive differential evolution.2015 IEEE Congress on Evolutionary Computation (CEC). [Online]. pp.877–884. Available from: doi:10.1109/CEC.2015.7256983.
     https://doi.org/10.1109/CEC.2015.7256983 [Google Scholar]
  2. Amini, H. & Alvarez, R.
    (2014) Calibration of the Petro-elastic Model ( PEM ) for 4D Seismic Studies in Multimineral Rocks. In: EAGE/FESM Joint Regional Conference Petrophysics Meets Geoscience.2014 pp. 174–178.
     [Google Scholar]
  3. Blei, D.M. & Michael I.Jordan
    (2004) Variational inference for Dirichlet process mixture models Mean field variational inference.Bayesian Analysis. [Online] (1), 1–9. Available from: doi:10.1214/06‑BA104.
     https://doi.org/10.1214/06-BA104 [Google Scholar]
  4. Chassagne, R., Obidegwu, D., Dambrine, J.
    , (2016) Binary 4D seismic history matching, a metric study.Computers & Geosciences Journal.
     [Google Scholar]
  5. Govan, A., Primmer, T., Douglas, C., Moodie, N., et al.
    (2006) Reservoir Management in a Deepwater Subsea Field--The Schiehallion Experience.SPE Reservoir Evaluation & Engineering. [Online] 9 (4), 6–9. Available from: doi:10.2118/96610‑PA.
     https://doi.org/10.2118/96610-PA [Google Scholar]
  6. MacBeth, C., Floricich, M. & Soldo, J.
    (2006) Going quantitative with 4D seismic analysis.Geophysical Prospecting.
     [Google Scholar]
  7. Obidegwu, D.
    (2015) Seismic History Matching Using Binary Images
     [Google Scholar]
  8. Obidegwu, D, Chassagne, R., MacBeth, C.
    , (2017) Seismic assistes history matching using binary maps, Journal of Natural Gas Science and Engineering
     [Google Scholar]
  9. Oliver, D.S. & Chen, Y.
    (2011) Recent progress on reservoir history matching: a review.Computational Geosciences. [Online] 15 (1), 185–221. Available from: doi:10.1007/s10596‑010‑9194‑2.
     https://doi.org/10.1007/s10596-010-9194-2 [Google Scholar]
  10. Pele, O. & Werman, M.
    (2008) Robust real-time pattern matching using bayesian sequential hypothesis testing.IEEE Trans Pattern Anal Mach Intell. [Online] 30 (8), 1427–1443. Available from: doi:10.1109/TPAMI.2007.70794.
     https://doi.org/10.1109/TPAMI.2007.70794 [Google Scholar]
  11. Taha, A.A. & Hanbury, A.
    (2015) An Efficient Algorithm for Calculating the Exact Hausdorff Distance.IEEE Trans. Pattern Anal. Mach. Intell. [Online] 37 (11), 2153–2163. Available from: doi:10.1109/TPAMI.2015.2408351.
     https://doi.org/10.1109/TPAMI.2015.2408351 [Google Scholar]
  12. Tanabe, R. & Fukunaga, A.S.
    (2014) Improving the search performance of SHADE using linear population size reduction.Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. [Online] 1658–1665. Available from: doi:10.1109/CEC.2014.6900380.
     https://doi.org/10.1109/CEC.2014.6900380 [Google Scholar]
  13. Tarantola, A.
    (1986) A Strategy For Nonlinear Elastic Inversion of Seismic Reflection Data.1986 SEG Annual Meeting.
     [Google Scholar]
  14. Teshome, M., Zerubabel, L. & Yoon, K.D.
    (2011) A SIMPLE BINARY IMAGE SIMILARITY MATCHING METHOD 2 . Matching Method.Computer Engineering.212–15.
     [Google Scholar]
  15. Tillier, E., Da Veiga, S. & Derfoul, R.
    (2013) Appropriate formulation of the objective function for the history matching of seismic attributes.Computers and Geosciences. [Online] 5164–73. Available from: doi:10.1016/j.cageo.2012.07.031.
     https://doi.org/10.1016/j.cageo.2012.07.031 [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201802282
Loading
Seismic History Matching Uncertainty With Weighted Objective Functions
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802282
/content/papers/10.3997/2214-4609.201802282
/content/papers/10.3997/2214-4609.201802282
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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