Deterministic tools to predict gas assisted gravity drainage recovery factor
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- @Article{HASANZADEH:2024:engeos,
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author = "Maryam Hasanzadeh and Mohammad Madani",
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title = "Deterministic tools to predict gas assisted gravity
drainage recovery factor",
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journal = "Energy Geoscience",
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volume = "5",
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number = "3",
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pages = "100267",
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year = "2024",
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ISSN = "2666-7592",
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DOI = "doi:10.1016/j.engeos.2023.100267",
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URL = "https://www.sciencedirect.com/science/article/pii/S2666759223001130",
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keywords = "genetic algorithms, genetic programming, Gas assisted
gravity drainage, Recovery factor, Deterministic tools,
Statistical evaluation",
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abstract = "Naturally fractured rocks contain most of the world's
petroleum reserves. This significant amount of oil can
be recovered efficiently by gas assisted gravity
drainage (GAGD). Although, GAGD is known as one of the
most effective recovery methods in reservoir
engineering, the lack of available simulation and
mathematical models is considerable in these kinds of
reservoirs. The main goal of this study is to provide
efficient and accurate methods for predicting the GAGD
recovery factor using data driven techniques. The
proposed models are developed to relate GAGD recovery
factor to the various parameters including model
height, matrix porosity and permeability, fracture
porosity and permeability, dip angle, viscosity and
density of wet and non-wet phases, injection rate, and
production time. In this investigation, by considering
the effective parameters on GAGD recovery factor, three
different efficient, smart, and fast models including
artificial neural network (ANN), least square support
vector machine (LSSVM), and multi-gene genetic
programming (MGGP) are developed and compared in both
fractured and homogenous porous media. Buckingham ?
theorem is also used to generate dimensionless numbers
to reduce the number of input and output parameters.
The efficiency of the proposed models is examined
through statistical analysis of R-squared, RMSE, MSE,
ARE, and AARE. Moreover, the performance of the
generated MGGP correlation is compared to the
traditional models. Results demonstrate that the ANN
model predicts the GAGD recovery factor more accurately
than the LSSVM and MGGP models. The maximum R2 of
0.9677 and minimum RMSE of 0.0520 values are obtained
by the ANN model. Although the MGGP model has the
lowest performance among the other used models (the R2
of 0.896 and the RMSE of 0.0846), the proposed MGGP
correlation can predict the GAGD recovery factor in
fractured and homogenous reservoirs with high accuracy
and reliability compared to the traditional models.
Results reveal that the employed models can easily
predict GAGD recovery factor without requiring
complicate governing equations or running complex and
time-consuming simulation models. The approach of this
research work improves our understanding about the most
significant parameters on GAGD recovery and helps to
optimize the stages of the process, and make
appropriate economic decisions",
- }
Genetic Programming entries for
Maryam Hasanzadeh
Mohammad Madani
Citations