Application of artificial neural network technique to predict permeability of Miocene reservoirs in Dai Hung field based on core data and wireline logging data
Abstract
Permeability is one of the important parameters for evaluating a reservoir’s flow rate. However, permeability value is usually defined based on core data and well test data, which are limited due to related high costs. Whereas, the calculation of permeability based on wireline logging parameters often meets with difficulties relating to underterminable inputs of calculation models introduced and utilised so far. Conventionally, a linear relationship of porosity and permeability constructed by core data will be applied to wireline logging data to calculate permeability for the whole well. However, this method is not always applicable because of the high heterogeneity of rocks which decreases the R value of the linear porosity - permeability relationship constructed by core data. On that basis, the authors propose to apply the Artificial Neural Network (ANN) to predict permeability based on combined core data and wireline logging, which allows permeability calculation with higher accuracy than conventional methods.
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