Automatic extraction of dislocated horizons from 3D seismic data using nonlocal trace matching

Abstract

Extracting key horizons from seismic images is an important element of the seismic interpretation workflow. Although numerous computer-assisted horizon extraction methods exist, they are typically sensitive to structural and stratigraphic discontinuities. As a result, these computer-assisted methods have difficulties in extracting noncoherent dislocated horizons. We have developed a new data-driven method to correlate, track, and extract horizons from seismic volumes with complex geologic structures. Our method correlates seismic horizons across discontinuities and does not require user input in the form of seed points or prior identification of faults. Furthermore, the method is robust toward amplitude changes along a seismic horizon and does not jump from peak to trough or vice versa. We use a large sliding window and match full-length seismic traces using nonlocal dynamic time warping to extract grids of correlated points for our target horizons. Through computed accuracy measurements, we discard nonaccurate correlations before interpolating complete seismic horizons. Because our method does not require manually picked seed points or prior structural restoration, it does not rely on interpretive experience or geologic knowledge. The proposed method is applied on different real and complex seismic images, with two case examples from the southwestern Barents Sea, and one on the open source Netherlands F3 seismic data.

Automated geological map deconstruction for 3D model construction using <i>map2loop</i> 1.0 and <i>map2model</i> 1.0

“…The third class of modelling constraints derived by the map2model algorithms provide the spatial and temporal topology of the map layers. Specifically, it creates network diagrams showing the stratigraphic relationships between units in the region of interest (Burns, 1988;Perrin and Rainaud, 2013;Thiele et al, 2016), network diagrams of the relationships between faults, and relationship tables showing whether a particular fault cuts a unit or group.…”

“…The modelling systems we target allow for onlap and erode relationships, and Thiele et al (2016) suggested the topological analysis of units to identify unique relationship characteristics between groups as a possible way forward, but this remains to be tested. Recent attempts at the automatic extraction of stratigraphic information from 3D seismic data (Bugge et al, 2019) could also constrain our systems in basin settings.…”

“…It has also long been recognized that a geological map and its legend provide more than just the distribution of lithological units but is also a compendium of many different types of information (Varnes, 1974;Bonham-Carter and Broome, 1998). Burns (1988) pioneered the analysis of maps in terms of the spatial and temporal relationships stored within, and Harrap (2001) defined a legend language with the aim of consistency checking both during and after map creation, especially when large, complex compilation maps were being created, and to focus on areas where a legend contradicts map relationships. Extracting information from digital geographic information system (GIS) maps was pioneered in the context of mineral prospectivity (Bonham-Carter, 1994) and more recently to validate the maps and analyse specific structures such as stratigraphic contacts and faults and even strati-graphic thicknesses (Fernández, 2005;Rauch et al, 2019;Kelka et al, 2020;Allmendinger, 2020).…”