Abstract
This study aims to design a back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field in the USA. In order to estimate the porosity, a neural network approach is applied, which uses as input sonic, density and resistivity log data, which are known to affect the porosity. This network easily sets up a relationship between the input data and the output parameters without having prior knowledge of petrophysical properties, such as porefluid type or matrix material type. The results obtained from the empirical relationship are compared with those from the neural network and a good correlation is observed. Thus, the ANN technique could be used to predict the porosity from other well log data.
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Singh, S., Kanli, A.I. & Sevgen, S. A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field. Stud Geophys Geod 60, 130–140 (2016). https://doi.org/10.1007/s11200-015-0820-2
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DOI: https://doi.org/10.1007/s11200-015-0820-2