1887

Abstract

Summary

Facies modeling of sedimentary basins and hydrocarbon reservoirs is a well-established field, but most methods focus on siliciclastic facies, with well-defined shapes. Ultra-deep carbonate sedimentary basins, on the other hand, began to be explored recently and there is a great need for technological development for their modeling. In addition to the challenge of integrating spatial data from different scales for modeling thin layers, there is a distinctive feature in carbonates, which is the asymmetric repetition of facies. Therefore, the quantification of geological uncertainty can be modeled through techniques that use Markov chains. Secondary information from seismic surveys can help generate auxiliary probabilistic fields and trend model. However, the challenge lies in calculating the lateral facies transition probabilities. We propose an innovative methodology to quantify uncertainty of ultra-deep carbonate facies. The methodology uses Markov chains random fields with trend simulation with Bayesian approach to generate facies realizations through three input variables: transiogram model, probability field model, and trend model. The results were compared to sequential indicator co-simulation. The method presented satisfactory results, with significant gains in reproducing the spatial characteristics of ultra-deep carbonates facies.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202335059
2023-11-27
2026-02-12

  • From This Site

    /content/papers/10.3997/2214-4609.202335059
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Le Blévec, T., Dubrule, O., John, C.M. et al. (2018). Geostatistical Modelling of Cyclic and Rhythmic Facies Architectures.Mathematical Geoscience50, 609–637.
     [Google Scholar]
  2. Li, W. (2007). Markov chain random fields for estimation of categorical variables.Mathematical Geology, 39, 321–335.
     [Google Scholar]
  3. Purkis, S., Vlaswinkel, B., & Gracias, N. (2012). Vertical-to-lateral transitions among Cretaceous carbonate facies—a means to 3-D framework construction via Markov analysis.Journal of Sedimentary Research, 82(4), 232–243.
     [Google Scholar]
  4. Scheidt, C., Li, L., & Caers, J. (Eds.). (2018). Quantifying uncertainty in subsurface systems (Vol. 236). John Wiley & Sons.
     [Google Scholar]
  5. Journel, A. G. (2002). Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses.Mathematical geology, 34, 573–596.
     [Google Scholar]
  6. Krishnan, S. (2008). The Tau model for data redundancy and information combination in earth sciences: theory and application.Mathematical Geosciences, 40(6), 705–727.
     [Google Scholar]
  7. Dubuisson, M. P., & Jain, A. K. (1994, October). A modified Hausdorff distance for object matching. In Proceedings of 12th international conference on pattern recognition (Vol. 1, pp. 566–568). IEEE.
     [Google Scholar]
  8. Yin, Z., Amaru, M., Wang, Y., Li, L., & Caers, J. (2022). Quantifying uncertainty in downscaling of seismic data to high-resolution 3-D lithological models.IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202335059
Loading
Geostatistical Modeling of Carbonate Facies with Markov Chains Random Fields with Trend Using Bayesian Approach
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335059
/content/papers/10.3997/2214-4609.202335059
/content/papers/10.3997/2214-4609.202335059
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