PORE PRESSURE PREDICTION BASED ON SEISMIC ATTRIBUTES RESPONSE TO OVERPRESSURE

Abstract

A seismic attribute-based quantitative methodology for remote prediction of effective stresses is proposed in this study. The algorithm uses neural networks as a nonlinear interpolator between a set of seismic attributes to predict changes in differential pressure. This method is based on the relationships between specific seismic attributes and differential pressures, which were established through a series of laboratory tests. A number of ultrasonic elastic measurements simulating normal compaction, fluid expansion and tectonic mechanisms of overpressure were performed on reservoir sandstone and shale core samples. The effects of different stress paths on seismic attributes derived from experimentally recorded wavelets were investigated. Positive relationships were established between differential pressure and several seismic attributes for the first time in these tests. It has been found that instantaneous frequency, weighted mean frequency, instantaneous phase, instantaneous pseudo-Q factor, instantaneous bandwidth, instantaneous dominant frequency and autocorrelation function are sensitive to differential pressure changes. The relationships between those attributes and differential pressure follow Eberhart- Phillips’ stress-velocity empirical relationship. This implies that seismic attributes could be used for a quantitative prediction of overpressure. Moreover, our experimental results show that frequency-based seismic attributes exhibit a greater sensitivity to changes in differential pressure than does seismic velocity. The proposed methodology has been tested on a 3D seismic dataset from the North West Shelf of Australia. Predicted pore pressure values are in good agreement with available borehole data.