Application of Intelligent Models in Determining the Pore Pressure of Hydrocarbon Reservoirs

Abstract

In seismic methods, pore pressure is estimated by converting seismic velocity into pore pressure and calibrating it with pressure results during the well-testing program. This study has been carried out using post-stack seismic data and sonic and density log data of 6 wells in one of the fields in SW Iran. While an optimum number of attributes is selected, the General regression (GRNN) provides higher accuracy than Back Propagation (BPNN) at the initial prediction stages. Suitable attributes for estimating compressional velocity (Vp) and density from seismic data are extracted by the Emerge module of HRS.8 software. Acoustic impedance (AI) is the most applicable seismic attribute used as root and reverses AI for estimating P-wave and density. Using a set of attributes can train the system to estimate the property. The correlation coefficient of actual and predicted P-wave using an AI seismic attribute has been calculated as 0.74 and the multi-attribute technique as 0.79. Also, density and three attributes reach from 0.57 to 0.60, which shows a better relationship between seismic attributes and density. After determining optimum layers with the principal components analysis (PCA), formation pressure was modeled with the feed forward-backpropagation (FFBP-ANN) method. Correlation between 0.2 and 0.3 is suitable for generating a neural network layer, and values below 0.2 have a low correlation. Five information layers, including gamma, Vp, AI, density, and overburden pressure, have the most linear convergence with the initial pressure model and are used to modify the ANN model of effective pressure with Petrel 2016 software.