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Connecting the Dots: Correlating Oil Geochemistries (Points) Along and Across the South Atlantic Margins

Abstract

In an increasingly 3D seismic world of regular spatial sampling, we use scattered point data such as oils and piston cores. Our data may locally appear like a pointillist painting, resolving to individual points only in closeup. Our efforts often resemble a child's exercise in connect-the-dots but lacking the ordinal numbers to direct the drawing. We overcome this lack with repeated statistical analysis and incorporation of complementary external constraints. With enough dots, we can draw useful pictures such as the distribution of pre-salt oils that are likely sag-derived. With access to about 1500 oils and ∼4500 piston cores for the South Atlantic Margin basins of Africa and Brazil, we have about 6 samples for each 10 linear km of margin. Our points cluster in areas of inferred prospectivity (piston cores) or discovery (oils) leaving vast areas unsampled. Their geochemical complexity is sufficient to yield correlations relating to age and depositional environment of source layer and maturation and migration history of the expelled hydrocarbons. This may tell us, for example, to connect dots from Brazil's Campos to Angola's Kwanza basins but how? We use Atlantic Region coverages of potential field data to reveal regional to sub-basin architecture. The data, also inverted to basement depth and sediment thickness, provide definition of structure and burial that control geochemical parameters. This paper considers the distribution of, and inferred controls on, about a hundred pre-salt oils, mostly in the Brazilian basins of Santos, Campos and Espírito Santo plus the African conjugates of Gabon to Angola. Because we rarely see pure end-member (single source type) oils, we also review inferences from post-salt (mostly marine) sources to unravel the evidence of mixing of oils and/or sources. We conclude by mapping a set of pre-salt sag-type sourced oils which likely derive from a mixed assemblage of paleo-environments and associated biomarkers.