Porosity prediction using fuzzy clustering and joint inversion of wireline logs: A case study of the Nam Con Son basin, offshore Vietnam

Petrophysical properties such as porosity, permeability and fluid saturations are important parameters for reservoir characterisation, which can be determined by experimental constitutive equations between rock parameters and well logging data. Thus, the same rock properties might demonstrate different patterns, depending on the input and equations used. In this work, we used the cross-properties (a common set of rock properties) that influence different measurements to reduce the ambiguity of the petrophysical property definition. We present an approach of using fuzzy c-means clustering to classify the well logs and core data in clusters and then running inversion for each cluster. The obtained results allowed us to establish suitable parameters in constitutive equations, which usually vary with rock units that may relate to clusters. Testing data applied to the Nam Con Son basin show a square correlation coefficient of 0.66 between the predicted and core measurement, suggesting a reasonable matching of the testing data set.