Abstract
Determination of a petroleum reservoir structure and rock
bulk properties relies extensively on inference from reflection
seismology. However, classic deterministic methods to
invert seismic data for reservoir properties suffer from some
limitations, among which are the difficulty of handling complex,
possibly nonlinear forward models, and the lack of robust
uncertainty estimations. To overcome these limitations,
we studied a methodology to invert seismic reflection data in
the framework of the probabilistic approach to inverse problems,
using a Markov chain Monte Carlo (McMC) algorithm
with the goal to directly infer the rock facies and porosity of
a target reservoir zone. We thus combined a rock-physics
model with seismic data in a single inversion algorithm. For
large data sets, the McMC method may become computationally
impractical, so we relied on multiple-point-based a priori
information to quantify geologically plausible models. We
tested this methodology on a synthetic reservoir model. The
solution of the inverse problem was then represented by a
collection of facies and porosity reservoir models, which were
samples of the posterior distribution. The final product included
probability maps of the reservoir properties in obtained
by performing statistical analysis on the collection of
solutions.
bulk properties relies extensively on inference from reflection
seismology. However, classic deterministic methods to
invert seismic data for reservoir properties suffer from some
limitations, among which are the difficulty of handling complex,
possibly nonlinear forward models, and the lack of robust
uncertainty estimations. To overcome these limitations,
we studied a methodology to invert seismic reflection data in
the framework of the probabilistic approach to inverse problems,
using a Markov chain Monte Carlo (McMC) algorithm
with the goal to directly infer the rock facies and porosity of
a target reservoir zone. We thus combined a rock-physics
model with seismic data in a single inversion algorithm. For
large data sets, the McMC method may become computationally
impractical, so we relied on multiple-point-based a priori
information to quantify geologically plausible models. We
tested this methodology on a synthetic reservoir model. The
solution of the inverse problem was then represented by a
collection of facies and porosity reservoir models, which were
samples of the posterior distribution. The final product included
probability maps of the reservoir properties in obtained
by performing statistical analysis on the collection of
solutions.
| Original language | English |
|---|---|
| Journal | Geophysics |
| Volume | 80 |
| Issue number | 1 |
| Pages (from-to) | R31-R41 |
| Number of pages | 11 |
| ISSN | 0016-8033 |
| DOIs | |
| Publication status | Published - 15 Dec 2014 |
| Externally published | Yes |