scholarly journals Energy potential of the Millungera Basin: a newly discovered basin in north Queensland

2011 ◽  
Vol 51 (1) ◽  
pp. 295 ◽  
Author(s):  
Russell Korsch ◽  
Heike Struckmeyer ◽  
Alison Kirkby ◽  
Laurie Hutton ◽  
Lidena Carr ◽  
...  

Deep seismic reflection surveys in north Queensland that were collected in 2006 and 2007 discovered a previously unknown sedimentary basin, now named the Millungera Basin, which is completely covered by a thin succession of sediments of the Jurassic–Cretaceous, Eromanga-Carpentaria Basin. Interpretation of regional aeromagnetic data suggests that the basin could have areal dimensions of up to 280 km by 95 km. Apart from regional geophysical data, virtually no confirmed geological information exists on the basin. To complement the seismic data, new magnetotelluric data have been acquired on several lines across the basin. An angular unconformity between the Eromanga and Millungera basins indicates that the upper part of the Millungera Basin was eroded prior to deposition of the Eromanga-Carpentaria Basin. Both the western and eastern margins of the Millungera Basin are truncated by thrust faults, with well-developed hangingwall anticlines occurring above the thrusts at the eastern margin. The basin thickens slightly to the east, to a maximum preserved subsurface depth of ˜3,370 m. Using sequence stratigraphic principles, three discrete sequences have been mapped. The geometry of the stratigraphic sequences, the post-depositional thrust margins, and the erosional unconformity at the top of the succession all indicate that the original succession across much of the basin was thicker–by up to at least 1,500 m–than preserved today. The age of the Millungera Basin is unknown, but petroleum systems modelling has been carried out using two scenarios, that is, that the sediment fill is equivalent in age to (1) the Neoproterozoic-Devonian Georgina Basin, or (2) the Permian–Triassic Lovelle Depression of the Galilee Basin. Using the Georgina Basin analogue, potential Cambrian source rocks are likely to be mature over most of the Millungera Basin, with significant generation and expulsion of hydrocarbons occurring in two phases, in response to Ordovician and Cretaceous sediment loading. For the Galilee Basin analogue, potential Permian source rocks are likely to be oil mature in the central Millungera Basin, but immature on the basin margins. Significant oil generation and expulsion probably occurred during the Triassic, in response to late Permian to Early Triassic sediment loading. Based on the seismic and potential field data, several granites are interpreted to occur immediately below the Millungera Basin, raising the possibility of hot rock geothermal plays. Depending on its composition, the Millungera Basin could provide a thermal blanket to trap any heat which is generated. 3D inversion of potential field data suggests that the inferred granites range from being magnetic to nonmagnetic, and felsic (less dense) to more mafic. They may be part of the Williams Supersuite, which is enriched in uranium, thorium and potassium, and exposed just to the west, in the Mount Isa Province. 3D gravity modelling suggests that the inferred granites have a possible maximum thickness of up to 5.5 km. Therefore, if granites with the composition of the Williams Supersuite occur beneath the Millungera Basin, in the volumes indicated by gravity inversions, then, based on the forward temperature modelling, there is a good probability that the basin is prospective for geothermal energy.

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luan Thanh Pham ◽  
Ozkan Kafadar ◽  
Erdinc Oksum ◽  
Ahmed M. Eldosouky

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.


2010 ◽  
Author(s):  
M. Shyeh Sahibul Karamah ◽  
M. N. Khairul Arifin ◽  
Mohd N. Nawawi ◽  
A. K. Yahya ◽  
Shah Alam

2014 ◽  
Vol 644-650 ◽  
pp. 2670-2673
Author(s):  
Jun Wang ◽  
Xiao Hong Meng ◽  
Fang Li ◽  
Jun Jie Zhou

With the continuing growth in influence of near surface geophysics, the research of the subsurface structure is of great significance. Geophysical imaging is one of the efficient computer tools that can be applied. This paper utilize the inversion of potential field data to do the subsurface imaging. Here, gravity data and magnetic data are inverted together with structural coupled inversion algorithm. The subspace (model space) is divided into a set of rectangular cells by an orthogonal 2D mesh and assume a constant property (density and magnetic susceptibility) value within each cell. The inversion matrix equation is solved as an unconstrained optimization problem with conjugate gradient method (CG). This imaging method is applied to synthetic data for typical models of gravity and magnetic anomalies and is tested on field data.


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