Assessing the calibration of the Sydney Basin thermal structure model - are shallow groundwater bores a good substitute for deeper measurements
Cara Danis, Craig O'Neill and Steve Quenette
ASEG Extended Abstracts
2013(1) 1 - 4
Published: 12 August 2013
Abstract
Inference on subsurface temperatures in geothermal studies based on sparse datasets and modelling, contains many sources of significant uncertainty but formal Bayesian approaches to deal with such uncertainties are very time consuming. A more practical method utilising measured data means we can now understand the robustness and sensitivity of modelled geothermal anomalies, optimise unknown parameters and produce a representative estimate of subsurface temperature. In the Sydney Basin previous estimates of subsurface temperatures at 5km have ranged from less than 100C to over 250C, largely due to uncertainties and assumptions during the modelling process. Our model optimisation process identified the basement and Permian Coal measures, for both thermal conductivity and heat production, and the basal temperature condition contribute the largest sources of uncertainty in conjunction with unconstrained heterogeneities in the basement and coal bearing formations. Our optimised model estimates the temperature at 5km depth in the Sydney Basin ranges from 150?C to 200?C with higher temperatures associated with areas of thick sediment and multiple coal measures. A comparison of model thermal conductivity profiles with measured data from four wells in the Sydney Basin indicates, on a regional scale, our parameters are reasonable when the average thermal conductivity is considered but highlights the degree of heterogeneity within the sedimentary profile. The full implications of this on estimated temperatures at depth are currently not well understood. Reliable observables are essential and this practical approach of developing ensembles constrained by these observables allows a better understanding of the variance expected in the responses in subsurface temperature. In addition parameters which are unknown can be estimated with a greater degree of certainty.https://doi.org/10.1071/ASEG2013ab022
© ASEG 2013