1887

Abstract

Summary

“Seismic inversion results are non-unique and can suffer from various pitfalls related to prior model assumptions or seismic data quality issues. Combining results from two entirely different inversion algorithms can substantially improve the understanding of these results. In this study, we use an inversion for probabilities of litho-facies, referred to as Pcube+, together with a classical elastic inversion. Pcube+ is a Bayesian method of updating prior probability models of lithology-fluid classes defined in the elastic parameter space. Outputs are posterior probability volumes for each pre-defined lithology-fluid class. A standard elastic inversion yields elastic parameter volumes, which can subsequently be transformed into probabilities of litho-facies using classification.

Comparing results from the two methods in areas away from well control has proven valuable in this case study. The main data source for pre-defining the lithology-fluid classes in PCube+ are well logs. However, wells tend to be biased and may not represent the full elastic parameter space that is required to explain the seismic data away from wells. In these situations, a cross-check with an elastic inversion gives crucial insight into which elastic parameter distributions are required to match the seismic AVO response, so that prior models for PCube+ can be fine-tuned.”

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201800729
2018-06-11
2024-03-29
Loading full text...

Full text loading...

References

  1. Kolbjørnsen, O., Buland, A., Hauge, R., Røe, P., Jullum, M., Metcalfe, R.W., Skjæveland, Ø.
    , [2016] Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model. The Leading Edge, 35(5), 431–436.
    [Google Scholar]
  2. Fatti, J.L., Smith, G.C., Vail, P.J., Strauss, P.J., and Levitt, P.R.
    [1994] Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique. Geophysics, 59, 1362–1376.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800729
Loading
/content/papers/10.3997/2214-4609.201800729
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