Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs

This paper deals with the comparison of models for predicting porosity and permeability of oil reservoirs by coupling a machine learning concept and petrophysical logs. Different machine learning methods including conventional artificial neural network, genetic algorithm, fuzzy decision tree, the imperialist competitive algorithm (ICA), particle swarm optimization (PSO), and a hybrid of those ones are employed to have a comprehensive comparison. The machine learning approach was constructed and tested via data samples recorded from northern Persian Gulf oil reservoirs. The results gained from the machine learning models used in this paper are compared to the relevant real petrophysical data and the outputs achieved by other methods employed in our previous studies. The average relative absolute deviation between the approach estimations and the relevant actual data is found to be less than 1% for the hybridized approaches. The results reported in this paper indicate that implication of hybridized machine learning methods in porosity and permeability estimations can lead to the construction of more reliable static reservoir models in simulation plans.

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