Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm

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@Article{Kaydani:2014:JPSE,

• author = "Hossein Kaydani and Ali Mohebbi and Mehdi Eftekhari",
• title = "Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm",
• journal = "Journal of Petroleum Science and Engineering",
• volume = "123",
• pages = "201--206",
• year = "2014",
• note = "Neural network applications to reservoirs: Physics-based models and data models",
• ISSN = "0920-4105",
• DOI = "doi:10.1016/j.petrol.2014.07.035",
• URL = "http://www.sciencedirect.com/science/article/pii/S0920410514002344",
• abstract = "Permeability estimation has a significant impact on petroleum fields operation and reservoir management. Different methods were proposed to measure this parameter, which some of them are inaccurate, and some others such as core analysis are cost and time consuming. Intelligent techniques are powerful tools to recognise the possible patterns between input and output spaces, which can be applied to predict reservoir parameters. This study proposed a new approach based on multi-gene genetic programming (MGGP) to predict permeability in one of the heterogeneous oil reservoirs in Iran. The MGGP model with artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and genetic programming (GP) model were used to predict the permeability and obtained results were compared statistically. The comparison of results showed that the MGGP model can be applied effectively in permeability prediction, which gives low computational time. Furthermore, one equation based on the MGGP model using well log and core experimental data was generated to predict permeability in porous media.",
• keywords = "genetic algorithms, genetic programming, rock permeability, porous media, core analysis",

}

Genetic Programming entries for Hossein Kaydani Ali Mohebbi Mehdi Eftekhari

Citations