article · International Journal of Environmental Research and Earth Science
Predicting subsurface pore pressure and characterising deepwater reservoirs remains difficult due to geological complexity and sparse well control. To address this challenge, an integrated workflow combining geomechanics, seismic inversion, and trend-kriging was tested in the Bonga Field of the deepwater Niger Delta. The method combined well logs, seismic velocity inversion, and traditional prediction models within a geostatistical framework. Evaluation showed that the Eaton method achieved an error of 0.15 SG in the upper overpressure zone, while the Bowers method delivered higher accuracy, with an error of 0.09 SG, in the deeper unloading zone. Trend-kriging successfully linked well and seismic data, yielding strong spatial predictive performance. The resulting process improves subsurface understanding and enhances drilling safety for complex deepwater reservoir development.
Accurate pore-pressure prediction in deepwater offshore environments is critical for preventing hazardous drilling incidents and optimizing well placement. By effectively integrating seismic data with well measurements, operators can better anticipate high-pressure subsurface zones. This reduces exploration risks, enhances operational safety for drilling crews, and supports more cost-effective management of complex offshore energy resources.
This workflow is targeted at offshore exploration and production companies, geoscientists, and drilling engineers looking to improve deepwater field design and drilling safety. Having been tested on field data from the Bonga Field, the workflow sits at an applied research stage. Further operational deployment could involve integrating larger calibration seismograms, hybrid machine learning algorithms, and 4D geomechanical monitoring to enhance predictive quality.
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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.
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DOI: 10.70382/caijeres.v12i4.079
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