Exploring seismic inversion methodologies for non-stationary geological environments: a benchmark study between deterministic and geostatistical seismic inversion
Authors:
S. Carmo, L. Azevedo and A. Soares
Journal name: Geophysical Prospecting
Issue: Vol 65, No 5, September 2017 pp. 1333 - 1350
DOI: 10.1111/1365-2478.12489
Organisations:
Wiley
Language: English
Info: Article, 
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Summary:
This paper presents a comparison between subsurface impedance models derived from
different deterministic and geostatistical seismic inversion methodologies applied to
a challenging synthetic dataset. Geostatistical seismic inversion methodologies nowadays
are common place in both industry and academia, contrasting with traditional
deterministic seismic inversion methodologies that are becoming less used as part of
the geo-modelling workflow. While the first set of techniques allows the simultaneous
inference of the best-fit inverse model along with the spatial uncertainty of the
subsurface elastic property of interest, the second family of inverse methodology has
proven results in correctly predicting the subsurface elastic properties of interest with
comparatively less computational cost. We present herein the results of a benchmark
study performed over a realistic three-dimensional non-stationary synthetic dataset
in order to assess the performance and convergence of different deterministic and
geostatistical seismic inverse methodologies. We also compare and discuss the impact
of the inversion parameterisation over the exploration of the model parameter space.
The results show that the chosen seismic inversion methodology should always be
dependent on the type and quantity of the available data, both seismic and well-log,
and the complexity of the geological environment versus the assumptions behind
each inversion technique. The assessment of the model parameter space shows that
the initial guess of traditional deterministic seismic inversion methodologies is of high
importance since it will determine the location of the best-fit inverse solution.
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