Pitfalls and implementation of data conditioning, attribute analysis, and self-organizing maps to 2D data: Application to the Exmouth Plateau, North Carnarvon Basin, Australia

Abstract

Recent developments in attribute analysis and machine learning have significantly enhanced interpretation workflows of 3D seismic surveys. Nevertheless, even in 2018, many sedimentary basins are only covered by grids of 2D seismic lines. These 2D surveys are suitable for regional feature mapping and often identify targets in areas not covered by 3D surveys. With continuing pressure to cut costs in the hydrocarbon industry, it is crucial to extract as much information as possible from these 2D surveys. Unfortunately, much if not most modern interpretation software packages are designed to work exclusively with 3D data. To determine if we can apply 3D volumetric interpretation workflows to grids of 2D seismic lines, we have applied data conditioning, attribute analysis, and a machine-learning technique called self-organizing maps to the 2D data acquired over the Exmouth Plateau, North Carnarvon Basin, Australia. We find that these workflows allow us to significantly improve image quality, interpret regional geologic features, identify local anomalies, and perform seismic facies analysis. However, these workflows are not without pitfalls. We need to be careful in choosing the order of filters in the data conditioning workflow and be aware of reflector misties at line intersections. Vector data, such as reflector convergence, need to be extracted and then mapped component-by-component before combining the results. We are also unable to perform attribute extraction along a surface or geobody extraction for 2D data in our commercial interpretation software package. To address this issue, we devise a point-by-point attribute extraction workaround to overcome the incompatibility between 3D interpretation workflow and 2D data.

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

“…Currently, the extraction of seismic attributes represents a fundamental tool for the exploration industry, as it speeds up the seismic interpretation process, reduces interpretation bias (Alcalde et al, 2019), and improves quantitative results (Chopra and Marfurt, 2007;Marfurt, 2018). Nevertheless, attribute analysis of a 2D seismic transect has some limitations, even when the tools are applied proficiently (e.g., "apparent dip"; Ha et al, 2019).…”

“…Steering cubes are typically calculated over 3D seismic volumes, and these require dip values in both the inline and the crossline directions at each seismic sample position. Dip-steering can also be calculated over 2D seismic profiles by storing the dip at every sample position only in the line direction (Ha et al, 2019). Conventionally, at least two steering outputs are generated.…”

Using seismic attributes in seismotectonic research: an application to the Norcia <i>M</i><sub>w</sub> = 6.5 earthquake (30 October 2016) in central Italy

“…Advantages of 3D and pitfalls of 2D are extensively discussed in Torvela et al (2013) and Hutchinson (2016). 2D seismic data are more limited, and 2D post-stack seismic attribute analysis may not provide the same quality of information as when using 3D (Marfurt and Alves, 2015;Ha et al, 2019). However, the main point is that in the past, it was common to sample study areas inland by 2D grids of seismic profiles, with full 3D seismic surveys being rare.…”

Using Seismic Attributes in seismotectonic research: an application to the Norcia's Mw = 6.5 earthquake (30th October 2016) in Central Italy

“…Hence, it is relevant to extract as much information as possible from 81 these 2D surveys in areas not covered by 3D seismic surveys. Whilst in the hydrocarbon industry this process is useful even 82 if mainly driven by a constant necessity to reduce the costs (Ha et al, 2019), in seismotectonic researches it is affected by 83 even worse limitations previously aforementioned. Therefore, also slight improvements obtained on vintage 2D data may 84 bring to new and unprecedent subsurface information in complex and active tectonic environments.…”