Título: | PROBABILISTIC PORE PRESSURE PREDICTION IN RESERVOIR ROCKS THROUGH COMPRESSIONAL AND SHEAR VELOCITIES | |||||||
Autor: |
BRUNO BROESIGKE HOLZBERG |
|||||||
Colaborador(es): |
SERGIO AUGUSTO BARRETO DA FONTOURA - Orientador |
|||||||
Catalogação: | 24/MAR/2006 | Língua(s): | PORTUGUESE - BRAZIL |
|||||
Tipo: | TEXT | Subtipo: | THESIS | |||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
|||||||
Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=7987&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=7987&idi=2 |
|||||||
DOI: | https://doi.org/10.17771/PUCRio.acad.7987 | |||||||
Resumo: | ||||||||
This work proposes a method for pore pressure prediction
in reservoir rocks
through compressional- and shear-velocity data (seismic
attributes). In the method, the
attributes are considered observations of a physic system,
which behavior depends on a
several not-observable parameters, where the pore pressure
is only one of these
parameters. To estimate the pore pressure, a Bayesian
inversion approach is adopted.
Through the use of a likelihood function, settled through
a calibrated rock physics model,
and through the Bayes theorem, the a priori information
about the not-observable
parameters (fluid and rock parameters and stress state) is
combined with the seismic
attributes, inferring probabilistically the pore pressure.
Due the non-linearity of the
problem, and due the uncertainties analysis demanding, an
algorithm based on Monte
Carlo simulations (a special case of the Metropolis-
Hastings algorithm) is used to solve the
inverse problem. The application of the proposed method is
simulated through some
synthetic examples. It is shown that a successfully pore
pressure prediction in reservoir
rocks depends on a set of factors, as how sensitive are
the rock velocities to pore pressure
changes, the a priori information about rock and fluid
parameters and the uncertainties
associates to the seismic attributes. Since the current
methods for pore pressure prediction
use exclusively the attribute compressional velocity V(p),
the contribution of the attribute
shear velocity V(s) on prediction is evaluated. In a
poorly consolidated rock scenario (or in
sands), the V(s) data, even with great uncertainties
associated, can significantly contribute to
a better pore pressure prediction. In a consolidated rock
scenario, the uncertainties
associated to pore pressure estimates are higher, and the
s V data does not contribute to
pore pressure prediction as it contributes in a poorly
consolidated rock scenario.
|
||||||||