MD2 (Multi-Dimensional, Multi-Disciplinary) Interpretation: Geochemical and Heat Flow Integration with 2.5-D Seismic Interpretation Examples from Gabon, the Congo Fan, Kwanza Basin and Niger Delta
William G. Dickson1, Craig F. Schiefelbein2, Mark E. Odegard3, and James M. Brooks4
1 Dickson International Geosciences (DIGs), Houston, TX
2 Geochemical Solutions International, The Woodlands, TX
3 Grizzly Geosciences, Sugar Land, TX
4 TDI-Brooks Int'l Inc, College Station, TX
Reconnaissance surface geochemical and heat flow studies offshore Nigeria to Angola in 1997 and 2000 helped define the likely origin and areal distribution of oil, condensate and gas seepage on the continental margin. More recent work in Brazil demonstrated the value of multi-disciplinary efforts in understanding a lacustrine-sourced petroleum system and a related exploration play. Widespread coverages of raster data (gravity, bathymetry/topography) and point samples (oils analyses, piston cores, heat flow probes) augmented with published material can extend the interpretation of sparse 2D seismic lines to evaluate leads and prospects in such plays.
One example in Gabon shows, at the intersection of two main structural trends, a pronounced seismic high which ought to be a focus for hydrocarbon migration. Unfortunately, potential fields data suggest a volcanic origin and geochemical confirmation is lacking. Nearby features in the area are linear, up to 40 - 60 km in length but without geochemical sampling or direct hydrocarbon indicators. Angola Block 34 demonstrates direct hydrocarbon indicators (mapped BSR's after Cunningham & Lindholm 1998), and scattered piston core anomalies but highly variable surface heat flow increases the exploration risk. The same data sets in the Niger Delta show strong correlations to each other, the main oceanic fracture zones and the toe thrust belts, forming exploration "compartments".
Continuing efforts in this project should provide greater insight into the complex petroleum systems of the Gulf of Guinea, so extending the value of each individual data set for prospect evaluation and ranking.