Pore Pressure Prediction Using High Resolution Velocity and Acoustic Impedance in a Complex Field

Abstract

Abstract Pore pressure prediction for exploratory deep Paleozoic well to avoid kicks, blow-outs, borehole instability and to design safe mud weight during drilling phase is a major challenge due to unavailability of deep well informations in South Fuwaris field, Kuwait. The problem was resolved integrating acoustic impedance volume derived from innovative seismic inversion on Prestack 3D Q-Land seismic data and geomechanical model with deeper pore pressure trend from drilled well data. The Q-land data with exceptionally clean, high signal to noise ratio, reliable amplitude, stable phase and wide range of frequency with high trace density facilitated reliable inversion for prediction of reservoir properties from seismic. The high resolution is achieved through global optimization to a single non-linear objective function, which provides the optimum positioning of the layers and 3D multi-trace damping of the random-noise. The low frequency model, a prerequisite for seismic inversion was built with densely sampled stacking velocity constraining on seismic horizons and wells. The most popular Bower's method, accounts for Loading (under-compaction) and Unloading (fluid expansion ) was applied to estimate Bower's parameters by cross-plotting velocity against effective vertical stress for drilled well - "A". The velocity reversal at Sargelu formation was confirmed with kicks in this well which was also cross validated with nearby Humma wells. The pore pressure and frac gradient were estimated along proposed exploratory well with the trend developed at known shallower well "A" which was found to be in conformity with regional trends. The lithology predicted along proposed boreholes in order to design mud weight plug and sanctity was reviewed at Top lithological markers using pseudo porosity logs derived with multi-attributes using neural network technique. This paper presents an integrated approach of pore pressure prediction for drilling deep exploratory well to minimize drilling risks.