3D Mad Dog Pressure and Stress Prediction Coupling Seismic Velocities, Pressure, and Stress Measurements

Abstract

We predict pressure and stress in the 3D Mad Dog field using the Full Effective Stress (FES) pressure-prediction workflow. The FES workflow incorporates the full stress tensor (e.g., lateral stress and deviatoric stresses) into pressure prediction: it uses a geomechanical model to predict mean total and shear stresses in the 3D field and a relationship between velocity and equivalent effective stress (instead of vertical effective stress) to account for both mean- and shear-induced pore pressure generation. In complex geologic settings, such as salt basins or thrust belts, compaction depends on non-vertical and differential stresses; in such settings, the FES method offers a significant improvement over the traditional approach, that is based on the vertical effective stress. We focus our study on the anticline below the Mad Dog salt at the original platform area. We quantify the mean and shear-induced overpressures and show that shear-induced pressures account for 80% of the total overpressure in front of the salt. We also show that shear-induced pressures are the source of more than 1.5ppg overpressure in the anticline below salt, where the mean-stress approach alone predicts underpressures (less than hydrostatic). Higher pressures and the decrease in lateral stress in the anticline area lead to a 1ppg drilling window (defined in this paper as the difference between the pore pressure and minimum principal stress at any given depth). This drilling window is shifted to higher overpressures by 0.4ppg compared to the VES prediction. We find that the stress ratio in the mudrocks decreases to ~55% of its uniaxial value. Furthermore, we show that the velocity-informed geomechanical model is able to predict the pore pressure regression observed at Mad Dog and the regional hydraulic connectivity in the area. The three-dimansional (3D) geomechanical model is built in Horizon (Elfen). The known pressure regression in the sands is modeled; mudrock pore pressures are initialized using the VES estimate. Modified Cam Clay is used to quantify mean- and shear-induced compaction. Overall, we demonstrate that incorporating the full stress tensor is important for pressure and stress prediction at Mad Dog, and that the FES method, by providing both pressure and stress, can help improve drilling-window estimates.