Integrated Assessment of Seismic Anomalies in Niger Delta Deepwater
Krzysztof M. Wojcik and Neil R. Braunsdorf
Shell International Exploration and Production, Inc, Houston, TX
Recent drilling in the outboard areas of the Niger Delta tested turbidite reservoirs frequently characterized by robust amplitude and AvO anomalies. Drilling results include both successes and failures indicating that the presence of seismic anomalies does not guarantee a success and improved methodologies are needed to discriminate between true hydrocarbon indicators and false positives. The wealth of data coming from the new wells has advanced our knowledge of the rock properties system and its geologic controls, while new volume visualization and interpretation technologies enable 3D integration of conceptual models and seismic observations resulting in an enhanced predictive framework and methodologies.
A broad, multidisciplinary and integrated approach is required for a thorough quantitative assessment of prospective areas. Seismic anomalies are detected and categorized through advanced multi-attribute volume visualization techniques. A set of representative subsurface scenarios is generated based on the multi-scale geological context of specific objectives. Scenario elements include lithology and stratigraphic characteristics of reservoir sandstones and bounding mudrocks and 3D representations of environmental variables and appropriate rock and fluid properties models. Synthetic seismic attributes are then calculated for each scenario covering a 3D region including the target anomaly and its lateral extensions. Calibration and comparison of the modeled and observed seismic response is carried out using volume interpretation techniques leading to the quantification of the signature match and scenario probability.
Several case studies based on the recent Niger Delta deepwater drilling campaign demonstrate how the combination of fundamental rock properties knowledge, geologically constrained scenarios, and the ability to carry out quantitative predictions in a volume interpretation environment improves our ability to predict lithology and pore-fill and mitigate subsurface risks.