Comparison of Machine Learning methods for estimating Permeability and Porosity of Geothermal reservoirs.

Transport and storage characterizations including porosity and permeability are crucial in any reservoir evaluation plans. They regulate the directions of the reservoir fluids and flow through porous media. Accurate estimation of these properties, particularly permeability, is vital for improvement of CO2 sequestration, selection of cost-effective production schemes, optimization of well placement, developing geothermal energy schemes, a secure design of noxious and management of water supplies. 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 can be employed to have a comprehensive comparison.

Comparison between the machine learning models can be used to show that hybridized method could predict the petro-physical properties of the reservoir with a good accuracy. 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. The capability of such methods in different areas might be varied based on the location, reservoir type, depth, heterogeneity, and other reservoir parameters.