Pore pressure prediction using probabilistic neural network: case study of South Sumatra Basin

Abstract

Pore pressure prediction in the planning of the drilling well commonly carried out using seismic stacking velocity and Normal Compaction Trend (NCT) analysis with Eaton’s equation. There are other parameters that correlate to pore pressure, i.e. density, P-impedance, S-impedance, and Vp/Vs ratio. The aims of this study are to predict pore pressure distribution from 2D pre and post-stack seismic data of South Sumatera field by applying the Probabilistic Neural Network (PNN). The pre-stack seismic inversion, which resulted in the elastic parameters such as Density (ρ), Vp/Vs ratio, P-impedance (Zp), S-impedance (Zs), is used as input for PNN training. In another hand, the post-stack seismic data, which resulted in the following parameters such as the average frequency, absolute integrated amplitude, apparent polarity, and dominant frequency, is also used to predict the lateral distribution of pore pressure. Our data training using PNN with pre-stack seismic data provided the best correlation up to 98% compared with the post-stack seismic data. Our prediction, in general, provides the pore pressure model and in detail provides over-pressure. The advantage of PNN shows vertical resolution as good as seismic resolution and provides more helpful information for a further drilling operation.