Bayesian Theorem Application to Model Reservoir Facies Distribution on Deltaic Depositional Environment, Case Study of Browse Basin

Successful Hydrocarbon exploration and reservoir characterization always related with good understanding of geology and geophysics aspect. One of the fundamental issues is to get reliable parameter distribution and quantify the confidence level of the parameter model in 3D. The case study in this paper will be applied in Browse field that and the result will be a distribution model in 3D of facies and hydrocarbon fluid. The workflow that will be introduced in this paper is the combination between rock physics analysis, simultaneous seismic inversion and Bayesian estimation theorem. Rock physics analysis includes well log conditioning and correlation analysis between reservoir parameter (porosity, saturation, Vshale, etc) with seismic parameter (acoustic impedance, vp/vs, shear impedance, etc) to obtain facies classification in well log scale. The simultaneous seismic inversion method is used to obtain seismic parameter cube to be correlated with rock physics result to drive the facies distribution. Bayesian estimation theorem assembles initial knowledge about a model before observing the inversion attributes. The probability density function later will be used to drive the facies distribution combined with well log data and seismic data; and also estimate the confidence level distribution in 3D. The result can be used to identify new play in the Browse field.