Improved Imaging of the Subsurface Geology in the Mowla Terrace, Canning Basin using Gravity Gradiometry Data
Irena Kivior, Stephen Markham, Fasil Hagos, Mark Baigent, Tony Rudge and Mark Devereux
ASEG Extended Abstracts
2018(1) 1 - 10
Published: 2018
Abstract
A study was undertaken to test whether it is possible to map basement configuration and sedimentary horizons from the gravity gradiometry (AGG) data. This was within the EP431 Buru Energy permit on the Mowla Terrace in the onshore Canning Basin. By applying the Horizon Mapping method, using Energy Spectral Analysis Multi-Window-Test as described in the Methodology section (ESA-MWT), to AGG data, we conducted a test study on a narrow 8km long swath along 2D seismic traverse HCG-300, and at three wells: Pictor -1, Pictor-2 and Pictor East-1, with three additional wells located nearby. ESA-MWT was applied to gridded Bouguer and tensor gravity data. The ESA-MWT procedure was conducted at stations 1km apart. At each station, multiple spectra were computed over incrementally increasing windows. For each spectrum, the depth was interpreted and plotted versus window size, and from these graphs, multiple Depth-Plateaus were detected at each station. These Depth-Plateaus correspond to density contrasts within the sediments and the underlying basement. These were then laterally merged with those from adjacent stations to form density interfaces. The results were validated with seismic and the litho-stratigraphy from well data which showed a good correlation with the tops of several sedimentary formations and intra-formational lithological boundaries. Ten density interfaces were mapped: Top Precambrian Basement, Top Nambeet Formation, Intra-Willara Interface, Top Acacia Sandstone, Top Willara Formation, Intra-Goldwyer Interface, Top Goldwyer Formation, Top Nita Formation, Intra-Tandalgoo Group Interface and Intra-Tandalgoo Group Interface. The geological model built along the Test Profile from interpretation of the AGG data shows good correlation with the wells and seismic data.https://doi.org/10.1071/ASEG2018abP017
© ASEG 2018