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Lithospheric Evolution Along a Slowly Rifted Passive Margin: Otway Basin, Southeast Australia — A Pragmatic Approach for Petroleum Systems Modeling

Abstract

The passive margin along the Otway coast of SE Australia has been subject to many thermal and heat flow studies in the past. Together with calibration data from hydrocarbon exploration wells we could estimate the present day lithospheric mantle thickness using isostatic principles, including the effects of flexural strengths. The thickness evolution of the lithospheric mantle and crust is a major uncertainty when analyzing the thermal history of a sedimentary basin. Traditional workflows invert the tectonic subsidence to stretching factors and thickness variations of lithospheric layers through geological time. This approach is widely used, but poorly calibrated, reducing the quality of the generated thermal model for petroleum systems modeling and predictive workflows. In this paper we present a method of calculating the base of the lithosphere from seismic interpretation and how we can quantify related uncertainties. The 2-step estimative workflow delivers workable scenarios of the present-day lithospheric layer thicknesses. Isostatic principles are used, and an uncertainty quantification is included using multiple inputs (gravity, paleo water-depth analysis and seismic interpretation. The present-day solutions based on our 2-step workflow can then be used as calibration data for the thickness evolution of the lithosphere during syn- and post-rift phases in the basin (stretching and cooling). The included uncertainty analysis can define minimum and maximum paleo temperature scenarios that are geologically reasonable and consistent with geological and geophysical observations. The quality and the confidence in the results of predictive workflows is highly increased, especially as the models are calibrated against geological observations, geological reasoning and measured temperature data. Calibration against present day temperatures alone would not be enough to reduce uncertainties to a manageable level.