View article

Permeability is one of the most difficult properties to predict, especially in carbonate reservoirs. Permeability prediction is a challenge to reservoir engineers due to the lack of tools that measure them directly. The most reliable data of permeability, obtained from laboratory measurements on cores, do not provide a continuous profile along the depth of the formation. This paper presents functional networks as a novel approach for forecasting permeability using Well Logs in a Middle Eastern Carbonate reservoir. Unlike the standard artificial neural network, functional network is a problem driven, in these networks there are no weights associated with the links connecting neurons, and it uses unknown neuron functions, that are learned from given families of linearly independent functions during the training process. Appropriate families can be chosen for each specific problem, such as, polynomials, Fourier, exponential …