Suriname Offshore Bid Round

Staatsolie Hydrocarbon Institute, a wholly owned subsidiary of Staatsolie Maatschappij Suriname, recently announced a round of competitive bidding for eleven new blocks offshore Suriname. The blocks are in the nearshore to shallow offshore areas, in water depths up to 150 meters, located south of the deepwater discoveries and north of the onshore producing oilfields.

Getech’s industry leading Multi-Sat2020 gravity data provide complete coverage over all the offshore parts of Suriname as a self-consistent 1km grid. As well as benefiting from increased satellite track coverage which enable a higher resolution and more accurately constrained solution, the data set includes data from the latest SARAL/AltiKa missions. These make use of a higher frequency altimeter meaning valid data can be obtained closer to the shoreline before land interference kicks-in meaning more coherent data in shallow marine environments, the focus for this latest Staatsolie bid round.

Offshore Suriname has undergone a complex evolutionary history including orogenic, extensional and strike-slip/transform tectonics that begun during the Paleoproterozoic and continue at Present-day. Getech’s Multi-Sat2020 data allow detailed insight into the pervasive Paleo and Neoproterozoic structural trends and their influence on the subsequent development of Jurassic–Cretaceous extensional systems and resultant crustal architecture.

Getech’s regional structural and crustal architecture interpretations for Guyana-Suriname form part of a global coverage that unravels the connections between the Central Atlantic, Equatorial Atlantic and Proto-/Caribbean Sea with Guyana-Suriname at the centre. These interpretations provide the foundations for a geologically robust, global dynamic plate model that satisfies the tectonics of neighbouring regions (including the Central Atlantic, Proto-/Caribbean, Gulf of Mexico and Equatorial Atlantic) and allows the development of paleogeographic/GDE maps and circulation models which can be used to refine predictive models at finer scales.

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