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,

• author = "Soran Mahmoodpour and Ehsan Kamari and Mohammad Reza Esfahani and Amir Karimi Mehr",
• title = "Prediction of cementation factor for low-permeability Iranian carbonate reservoirs using particle swarm optimization-artificial neural network model and genetic programming algorithm",
• journal = "Journal of Petroleum Science and Engineering",
• volume = "197",
• pages = "108102",
• year = "2021",
• ISSN = "0920-4105",
• DOI = "doi:10.1016/j.petrol.2020.108102",
• URL = "https://www.sciencedirect.com/science/article/pii/S0920410520311566",
• keywords = "genetic algorithms, genetic programming, The cementation factor of carbonate reservoirs, RCAL data, Hybrid PSO-ANN model, Genetic programming algorithm",
• 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