Título: | EVALUATING THE USE OF RANDOM FOREST REGRESSOR TO RESERVOIR SIMULATION IN MULTI-REGION RESERVOIRS | ||||||||||||
Autor: |
IGOR CAETANO DINIZ |
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Colaborador(es): |
SINESIO PESCO - Orientador THIAGO DE MENEZES DUARTE E SILVA - Coorientador |
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Catalogação: | 22/JUN/2023 | Língua(s): | ENGLISH - UNITED STATES |
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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. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=62992&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=62992&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.62992 | ||||||||||||
Resumo: | |||||||||||||
Oil and gas reservoir simulation is a common demand in petroleum
engineering, and research, which may have a high computational cost, solving
a mathematical numeric problem, or high computational time. Moreover,
several reservoir characterization methods require multiple iterations, resulting
in many simulations to obtain a reasonable characterization. It is also
possible to mention ensemble-based methods, such as the ensemble Kalman
filter, EnKF, and the Ensemble Smoother With Multiple Data Assimilation,
ES-MDA, which demand lots of simulation runs to provide the output
result. As a result, reservoir simulation might be a complex subject to
deal with when working with reservoir characterization. The use of machine
learning has been increasing in the energy industry. It can improve the
accuracy of reservoir predictions, optimize production strategies, and many
other applications. The complexity and uncertainty of reservoir models pose
significant challenges to traditional modeling approaches, making machine
learning an attractive solution. Aiming to reduce reservoir simulation’s
complexities, this work investigates using a machine-learning model as an
alternative to conventional simulators. The Random Forest regressor model
is experimented with to reproduce pressure response solutions for multi-region
radial composite reservoirs. An analytical approach is employed to create
the training dataset in the following procedure: the permeability is sorted
using a specific distribution, and the output is generated using the analytical
solution. Through experimentation and analysis, this work aims to advance
our understanding of using machine learning in reservoir simulation for the
energy industry.
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