Well-Based Pore Pressure Validation: A Case Study of Akos Field, Coastal Depobelt, Niger Delta Basin

  • Juliet Emudianughe Department of Earth Sciences, Federal University of Petroleum Resources, Effurun
Keywords: Pore Pressure, Eaton’s model, Normal Compaction Trend, Overburden, Depobelt

Abstract

The importance of proper and accurate pore pressure prediction prior to drilling cannot be overemphasized. Such prediction is needed for accurate planning for safe drilling of a well. Inaccurate pre-drill pore pressure prediction may result in avoidable catastrophic incidents during drilling. Till date, the Eaton’s empirical pore pressure prediction model is widely adopted and applied in the industry for pore pressure prediction. The prediction is predicated on the relationship between porosity and formation physical properties such as resistivity, interval transit time and density, with derived overpressure model and normal sediment compaction trend as input. Employing the Eaton’s model, pore pressure was modeled in this study in AKOS field, Niger Delta coastal swamp depobelt, using overburden model and normal sediment compaction trend derived in ADA field, situated about 33 km offset from the AKOS field. The purpose was to determine if overburden model and compaction trend derived in an offset field could be deployed to adequately predict pore pressure in a given field in the Niger Delta. Comparison of the modeled pressures with pore pressures directly measured in the AKOS field shows good agreement in the pore pressure trends, with prediction success rate of only 19%. Since direct pressure measurements give accurate pressure information in the subsurface, the lack of correlation between the modeled and measured pressures indicates that precise pore pressures cannot be predicted for a field by the use of overburden and normal compaction trends derived in another field. In order to use offset data for well-based pore pressure predictions, wide error margins must thus be included in the model to give some level of confidence in the prediction.

Published
2019-06-27