Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality

Abstract

Despite exploration and production success in Niger Delta, several failed wells have been encountered due to overpressures. Hence, it is very essential to understand the spatial distribution of pore pressure and the generating mechanisms in order to mitigate the pitfalls that might arise during drilling. This research provides estimates of pore pressure along three offshore wells using the Eaton's transit time method, multi-layer perceptron artificial neural network (MLP-ANN) and random forest regression (RFR) algorithms. Our results show that there are three pressure magnitude regimes: normal pressure zone (hydrostatic pressure), transition pressure zone (slightly above hydrostatic pressure), and over pressured zone (significantly above hydrostatic pressure). The top of the geopressured zone (2873 mbRT or 9425.853 ft) averagely marks the onset of overpressurization with the excess pore pressure above hydrostatic pressure (P∗) varying averagely along the three wells between 1.06−24.75 MPa. The results from the three methods are self-consistent with strong correlation between the Eaton's method and the two machine learning models. The models have high accuracy of about > 97%, low mean absolute percentage error (MAPE 0.98). Our results have also shown that the principal generating mechanisms responsible for high pore pressure in the offshore Niger Delta are disequilibrium compaction, unloading (fluid expansion) and shale diagenesis.

Keywords

• Niger Delta
• Pore pressure
• Reservoir
• Fracturing pressure
• Artificial neural network
• Machine learning algorithm