INTEGRATED GEOMECHANICS AND SEISMIC INVERSION WORKFLOW FOR PORE-PRESSURE PREDICTION AND RESERVOIR CHARACTERISATION USING TREND-KRIGING IN DEEPWATER BASINS
Geological complexity, limited well control, and the inability to accurately predict pore pressure and characterise reservoirs using traditional stand-alone seismic and/or well-log techniques continue to pose challenges in deep-water basins. In this study, an integrated geomechanics and seismic inversion workflow has been developed for pore-pressure prediction and reservoir characterisation, using the Bonga Field of the deepwater Niger Delta as the study area and incorporating the trend-kriging technique. The methodology integrated well-log analysis, Eaton and Bowers pore-pressure prediction models, seismic velocity inversion and trend-kriging interpolation in a structurally constrained geostatistical framework. The results indicated the mean absolute error of the Eaton method was 0.15 SG in the upper overpressure zone, while the Bowers method yielded better accuracy (0.09 SG) in the deeper unloading-dominated zone. Trend-kriging showed good predictive performance (R = 0.87; MAE = ±65 m/s), providing a very good integration of well and seismic data in terms of spatial prediction. The study shows that the recommended workflow can help to improve subsurface characterisation, increase drilling safety, and aid in optimised reservoir development. It suggests increasing the size of calibration seismograms, using hybrid machine learning algorithms and including 4D geomechanical monitoring to further enhance predictive quality and reservoir management in deep water.