Porosity estimation by neural networks for CO2 storage in Otway site

Abstract

Dynamic simulation of CO2 migration requires a variety of modeling parameters fed by geomechanical models. The confidence of these parameters of material groups such as porosity and permeability is crucial in achieving successful simulations. Based on the geomechanical and geophysical parameters, we estimated porosity distributions on the Paaratte Formation in the Otway site, one of the CO2 storage project in Australia. Considering the nonlinear relations between porosity logs and seismic data, we applied the neural network scheme that addresses the porosity value across a whole domain. With only one monitoring well and two injection wells at the site, seismic data are used to restore the spatial absence in porosity. The technique of the neural network was conducted based on the integration of the well logs to the seismic volume and the inversion of acoustic impedance. The results indicated that a correlation value of the well and the seismic tie is 75% and the value between the recorded and the estimated porosity is 87% on average. Further, the time slice maps of porosity at a depth of the injection interval demonstrated a CO2 plume developed in the Paaratte formation of the Otway site.