Método paralelo de superiorização para problemas de ajuste de histórico usando priors sísmicos a suavidade por partes
History Matching is a very important process used in managing oil and gas production since it aims to adjust a reservoir model until it closely reproduces the past behavior of a actual reservoir, so it can be used to predict future production. This work proposes the use of an iterative method cal...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/52424 |
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| Resumo: | History Matching is a very important process used in managing oil and gas
production since it aims to adjust a reservoir model until it closely reproduces
the past behavior of a actual reservoir, so it can be used to predict future
production. This work proposes the use of an iterative method called superiorization for the constrained optimization of a reservoir production model. The
superiorization method is a bi-objective optimization approach, where the first
objective is the production result and the second is a measure of the smoothness
by parts of the reservoir. The second criterion seeks to optimize its functional
without negatively affecting the optimization of the first criterion. The superiorization method is used in conjunction with some iterative algorithm. Our
work used the tabu search algorithm in conjunction with superiorization. As a
comparative approach for these algorithms, a non-superiorized genetic algorithm was developed, given that this technique is widely used in the literature
to solve history matching. Tests were also performed with the non-superiorized
tabu search to see how the superiorized algorithm differs. Both techniques are iterative and use population approaches. As the problem addressed is an inverse
problem that is usually severely underdetermined, several possible solutions
may exist for its resolution. Due to this, we also propose the use of seismic
data from the reservoirs to use these data to verify the faults present in the
reservoir. We can use piecewise smooth functions to reduce the number of
possible results through regularization using the second optimization criterion
of the superiorized version of the tabu search algorithm. Another critical factor
in the history matching process is the simulation time, which is generally high.
Thus, we also propose to investigate the use of parallelism of the solution using
the CPU. The experiments were carried out in a 3D reservoir model to find
correspondence for gas, oil, and water production values. The results obtained
during the research show that the parallel approach decreases the execution
time by up to 70%. The genetic approach obtained better mean values using
statistical tests regarding the precision of the result. However, the tabu search
and the superiorization method showed similar results but were more stable
since the results of this algorithm have less variation. |
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