Facies probability application for gas reservoir characterization

Abstract

Reservoir characterization through seismic inversion is the principle method in hydrocarbon exploration. Facies probability is an advance method of reservoir characterization which integrates model-based seismic inversion and facies classification to construct a better reservoir modelling. The reservoir modelling is produced from four main steps. First is crossplot analysis to define the relation between acoustic parameter and petrophysics parameter. Second, model-based seismic inversion to produce acoustic impedance volume from seismic data. Third, facies classification which is defined from porosity effective, shale volume and water saturation. Final step is Bayesian Inference Framework to model probability density function (pdf) of each facies. The result of crossplot analysis shows that there is a linear relationship between acoustic impedance and porosity hence the porosity volume can be derived from the model-based inversion result. Facies classification is divided into two categorical zone, pay and non-pay. Pay zone is categorized as a high porosity layer filled with gas. Its parameters fit with Vshale > 0.6, Sw < 0.7 and porosity effective > 0.22. Then estimation is run using Bayesian probability into acoustic impedance volume and porosity volume resulting probability volume of each facies.