Prediction of porosity in crystalline rocks using artificial neural networks: An example from the Chinese Continental Scientific Drilling Main hole

Abstract

Porosity plays an important part of understanding permeability and fluid flow within the continental, crystalline rocks. Geophysical well logs are presently the most consistent means of providing continuous information for porosity estimation. However, it is difficult to interpret geophysical well logs data in crystalline rocks due to their complex geological features and the difficulty in understanding and using the complex and intensive information content in these data. Motived by the successful prediction abilities of artificial neural networks (ANN) to solve different problems in geophysics, this study explore the applicability of using ANNs to predict porosity in continental, crystalline rocks. This ANN technique is calibrated on Chinese Continental Scientific Drilling Main Hole (CCSD-MH) data, which provides core porosity data combined with four geophysical well logs (density, neutron porosity, sonic and resistivity). The data from CCSD-MH is utilized to train feed-forward backpropagation (FFBP) neural network and radial basis function (RBF) neural network to derive a relationship between geophysical well logs and porosity, and hence predict porosity accurately. The findings demonstrate that ANNs provide better performances with sets of three geophysical well logs (density, sonic and resistivity) than regression technique. Comparison of FFBP to RBF showed that RBF reveals better stability and more accurate performances than FFBP. Based on the success achieved in this study, this intelligence artificial technique can be a very advantageous tool in facilitating the task of geophysicists in the framework of research drillings in continental crust.