Using discovery process and accumulation volumetric models to improve petroleum resource assessment in Sverdrup Basin, Canadian Arctic Archipelago
Zhuoheng Chen, Kirk G. Osadetz, 2011. "Using discovery process and accumulation volumetric models to improve petroleum resource assessment in Sverdrup Basin, Canadian Arctic Archipelago", Arctic Petroleum Geology, Anthony M. Spencer, Ashton F. Embry, Donald L. Gautier, Antonina V. Stoupakova, Kai Sørensen
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Sverdrup Basin hosts a structural petroleum play in Mesozoic clastic reservoirs. Twenty-one discovered fields (eight crude oil and 25 natural gas pools) have 294.1×106 m3 crude oil, and 500.3×109 m3 natural gas, original in-place contingent resources. We discuss and compare discovery process and volumetric assessment methods that, respectively, predict a 673.1×106 m3 or 698.7×106 m3 median crude oil resource and a 1187.4×109 m3 or 1202.8×109 m3 median natural gas resource. Both methods predict that the largest crude oil and third-largest natural gas pools are undiscovered, a result inferred to be consistent with available data and the exploration history. Volumetric assessments can precede any discoveries and they use common geoscience data inputs; however, they can be affected by data interdependencies and biases from exploratory sampling and subjective parameter estimates, particularly those affecting the number of accumulations. Discovery process methods solve for the accumulation numbers and size distribution simultaneously, accounting for sampling bias and free of data interdependencies, but only once sufficient discoveries exist. The Sverdrup Basin dataset and exploration history permit us to cross-validate volumetric and discovery process assessments and validate their predictions, for example undiscovered pool sizes, against a regional geoscience dataset. The discovery process results agree well with geoscience constraints, but the initial volumetric assessment must be restricted to predict undiscovered pool sizes consistent with the geoscience dataset. Our analysis illustrates advantages and potential pitfalls for volumetric and discovery process assessments and shows that cross-validation between methods and against available data constrains resource potentials and improves confidence in result.