Integrated Basin Subsidence Analysis and the Importance of Whole-Lithosphere Thermal Modeling to Petroleum Systems Analysis
It has long been recognized that basin subsidence is driven by crustal and mantle thinning and, once thinning has ended, by the cooling of the lithosphere. The stratigraphic architecture of a basin can thus, in principle, be used as a proxy for heat‐flow into the overlying sediments. However, it is also the case that observed heat‐flow and subsidence are often different to expectations derived from simple lithosphere thinning models, and that other processes may be in play, and that there may be no traditional calibration data (well temperatures and maturity) in the areas about which we are asked to make useful predictions. We present two case studies, illustrating the role of two processes that cannot be ignored: thermal blanketing by sediment, and base‐crust or intra‐crust magmatic underplating). Full‐lithosphere modeling using base lithosphere temperature boundary conditions rather than base‐sediment heat‐flow is required to account for these processes, but also opens the way to using non‐traditional data to calibrate our thermal models. Example 1, The Exmouth Plateau, Northwest Australia: Recently acquired seismic reflection and refraction data reveals up to 18 km of sediment overlying mostly normal thickness oceanic crust. The observed Permian(?) to Late Jurassic tectonic subsidence appears, at first, to fit with classical ocean crust subsidence models, superficially suggesting that sediment thermal blanketing is not a major issue. However, we cannot match well data if apply a typical oceanic heat‐flow to the base of the sediment pile, nor can numerical models which use a typical Mesozoic ocean crust analogue reproduce observed subsidence. Numerical models which resolve the apparent mismatch between heat‐flow, subsidence and lithosphere thinning and thickening show that the lithosphere is 25‐30 km more than typical Mesozoic oceanic crust, consistent with observed upper mantle shear wave velocity in this area. Sediment thermal blanketing can reduce basement heat‐flow to less than half the expected value, while the sediment pile can contribute at least as much again to surface heat‐flow. Inferred elevated heat‐flow during a second Late Jurassic event is largely the result of uplift and erosion driven by the isostatic response to mantle thinning and not a direct result of a deep‐seated heat spike. Example 2, The Vøring margin, Norway: Prior to drilling the Dalsnuten well on the Gjallar Ridge, we prepared basin models which revealed the importance of magmatic underplating and its role in determining the maturity of deeper strata and tested the sensitivity of the proposed Upper Jurassic petroleum system to the presence or absence of underplate and the relative thickness of underplate and residual continental crust. Models with continental crust only run cool and oil‐mature, but with even moderate underplate from 20% of the total crust run gas‐mature. Peak maturity and present‐day temperature at prognosed source rock level was more sensitive to the presence or absence of magmatic underplating than the absolute thickness of underplate. Gravity modeling also revealed an upper mantle thermal anomaly associated with intense Paleocene lithosphere thinning and underplating, supporting the more magmatic interpretations and highlighting a risk, subsequently proven by a dry hole. Key learnings: An approach to model building and calibration driven by both data and geodynamic process can help resolve problems which are not apparent in models which use basal heat‐flow thermal boundary conditions. These models can be somewhat calibrated with subsidence data and gravity models, and implicitly include the effects of sediment loading and blanketing, and the results used to de‐risk opportunities in sparsely or undrilled areas, where we cannot use conventional empirical approaches to model calibration. Sediment blanketing can result in very different heat‐flow at basement and mudline to the predictions of over‐simple geodynamic models. Critically, appropriate sensitivity analysis means it is not always necessary to identify the “correct” model to make a reasonable business decision. Doing this is a timely‐manner remains a challenge. We need to evolve new ways of working with enormous amounts of data, using statistically robust approaches to know when our models are “good enough” and to consistently identify the types of data that might make them better.
AAPG Datapages/Search and Discovery Article #90349 © 2019 AAPG Hedberg Conference, The Evolution of Petroleum Systems Analysis: Changing of the Guard from Late Mature Experts to Peak Generating Staff, Houston, Texas, March 4-6, 2019