Seismic Lithofacies Prediction and Reservoir Characterisation in Deep-Offshore Niger Delta, Nigeria
The deepwater Niger Delta is associated with local mud diapirism and complex sand distribution. Hence, sequence stratigraphic prediction of reservoir sands and seal, as well as geostatistical reservoir characterisation, have not been effective in the deepwater environments due to the isolated nature of the reservoir sand bodies dispersed in shale-prone environments. Consequently, the exploration risk of drilling dry holes and the challenges of deepwater field development are very high. Hence, this study is aimed at predicting and characterising deepwater reservoir systems, as a means of reducing geologic uncertainties in the study area. About 600 km2 of three dimensional seismic data, log suite from seven wells, and 60 m sedimentological core footage were utilized in the study. The methodology combined qualitative log analysis, petrophysics, rock physics, seismic attribute analysis, sedimentology, and geostatistics. Sedimentological evidence from core including sand injectite, floating mudclast, faint normal grading, parallel lamination, lenticular beddings, normal and inverse grading, has shown that the sedimentation mechanisms in the deepwater Niger Delta comprises of sediment slumping, sliding, debris flow, turbidity current flow, pelagic and hemipelagic settling, with no diagnostic support for hyperpycnal flow. Also, the study has shown the depositional model to be a hybrid of turbidite fan, debrite lobes and channel sands dispersed in background shale. The predicted reservoir facies include channels sands and fan lobes having good to excellent reservoir qualities, with porosity ranging between 0.21 and 0.36. The reservoir sands are easily distinguished from the background shale based on diagnostic elastic properties which measure stiffness, rigidity, and incompressibility. The reservoir sands are characterised by low lambda-rho, low Poisson's ratio, low primary versus shear wave velocity ratio, low closure stress scalar; and high mu-rho. This study has also shown the diagnostic post-stack seismic attributes for reservoir characterization in the study area to include: sweetness, envelope, reflection intensity, and root mean square amplitude. These diagnostic seismic attributes have correlation of between 0.66 and 0.8 with elastic rock properties in predicting lithofacies. Also, the use of seismic attributes as training image for geostatistical modeling of channel sands and fan lobes reconstruct reservoir geometries much better for field development than variogram and object-based geostatistical techniques. Finally, this study has proved that the integration of rock physics, post-stack seismic attributes and sedimentology is very effective in addressing the inherent challenges of predicting and characterizing geometrically complex reservoir facies in the shale-prone deepwater setting of the offshore Niger Delta.