Prediction of cementation factor for low-permeability Iranian carbonate reservoirs using particle swarm optimization-artificial neural network model and genetic programming algorithm
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- @Article{MAHMOODPOUR:2021:JPSE,
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author = "Soran Mahmoodpour and Ehsan Kamari and
Mohammad Reza Esfahani and Amir Karimi Mehr",
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title = "Prediction of cementation factor for low-permeability
Iranian carbonate reservoirs using particle swarm
optimization-artificial neural network model and
genetic programming algorithm",
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journal = "Journal of Petroleum Science and Engineering",
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volume = "197",
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pages = "108102",
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year = "2021",
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ISSN = "0920-4105",
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DOI = "doi:10.1016/j.petrol.2020.108102",
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URL = "https://www.sciencedirect.com/science/article/pii/S0920410520311566",
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keywords = "genetic algorithms, genetic programming, The
cementation factor of carbonate reservoirs, RCAL data,
Hybrid PSO-ANN model, Genetic programming algorithm",
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abstract = "cementation factor is a crucial parameter that has a
significant influence on the estimation of reservoir
parameters. Laboratory measurements for cementation
factor are available for occasional cases because
experimental special core analyses for determination of
cementation factor values are expensive and
time-consuming. While this factor plays a significant
role in determining water saturation, there is no
comprehensive and precise relationship for the case of
Iranian carbonate reservoirs. In this article, a unique
model was used based on a powerful combination of
artificial neural network (ANN) and particle swarm
optimization (PSO) algorithm to model the cementation
factor. In the second phase of simulation, a
correlation for the cementation factor was discovered
by genetic programming (GP) algorithm. Both the PSO-ANN
model and GP algorithm are trained by input variables
such as porosity, permeability, and grain density
derived from 175 routine core analysis (RCAL) samples
of 21 carbonated oil fields. To determine the relative
impact of the independent variables on cementation
factor the sensitivity analysis was carried out for
both models. The comparison between the PSO-ANN model
output and the experimental cementation factor data
clearly demonstrated that the built model can predict
the cementation factor with great precision; the mean
square error between the model predictions and the
experimental data was less than 0.07. The root mean
square error of training and testing data sets for the
new developed correlation using GP algorithm were
0.0902 and 0.0727 respectively. Finally, to evaluate
the validity and reliability of the developed models, a
comparison was implemented between these two models and
other empirical models over an external employment data
set (21 data point). This comparison revealed that the
GP algorithm and PSO-ANN model deliver a higher
performance capacity compared to other proposed
correlations for predicting cementation exponent",
- }
Genetic Programming entries for
Soran Mahmoodpour
Ehsan Kamari
Mohammad Reza Esfahani
Amir Karimi Mehr
Citations