Seismic modelling of metre-scale normal faults at a reservoir-cap rock interface in Central Spitsbergen, Svalbard: implications for CO2 storage

Lubrano-Lavadera, Paul; Senger, Kim; Lecomte, Isabelle; Mulrooney, Mark Joseph; Kühn, Daniela

doi:10.17850/njg003

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Although ray-based PSF convolution modelling has been applied in several geological case studies (e.g. Botter et al, 2014;Lecomte et al, 2015;Botter et al, 2017;Kjoberg et al, 2017;Eide et al, 2018;Rabbel et al, 2018;Grippa et al, 2019;Lubrano-Lavadera et al, 2019;Jensen et al, 2021), comparative studies between waveform-based modelled/migrated data and simulated seismic images obtained via ray-based and wave-based PSF convolution modelling are limited. Lecomte et al (2003) illustrate how PSF-convolved images obtained from ray tracing match well with Kirchhoff PSDM images obtained from a model of the Gullfaks field located on the Norwegian Continental Shelf.…”

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confidence: 99%

Point‐spread function convolution to simulate prestack depth migrated images: A validation study

Jensen

Lecomte

Gelius

et al. 2021

Geophysical Prospecting

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Seismic migration commonly yields an incomplete reconstruction of the Earth model due to restricted survey aperture, band-limited frequency content and propagation effects. This affects both illumination and resolution of the structures of interest. Through the application of spatial convolution operators commonly referred to as point-spread functions, simulated prestack depth-migrated images incorporating these effects may be obtained. Such simulated images are tailored for analysing distortion effects and enhance our understanding of seismic imaging and subsequent interpretation. Target-oriented point-spread functions may be obtained through a variety of waveform and ray-based approaches. Waveform approaches are generally more robust, but the computational cost involved may be prohibitive. Ray-based approaches, on the other hand, allow for efficient and flexible sensitivity studies at a low computational cost, but inherent limitations may lead to less accuracy. To yield more insight into the similarities and differences between point-spread functions obtained via these two approaches, we first derive analytical expressions of both wave-and ray-based point-spread functions in homogeneous media. By considering single-point scatterers embedded in a uniform velocity field, we demonstrate the conditions under which the derived equations diverge. The accuracy of wave-based and ray-based point-spread functions is further assessed and validated at selected targets in a subsection of the complex BP Statics Benchmark model. We also compare our simulated prestack depth migrated images with the output obtained from an actual prestack depth migration (reverse time migration). Our results reveal that both the wave-and ray-based approaches accurately model illumination, resolution and amplitude effects observed in the reverse time-migrated image. Furthermore, although some minor deviations between the wave-based and ray-based approaches are observed, the overall results indicate that both approaches can be used also for complex models.

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confidence: 99%

Point‐spread function convolution to simulate prestack depth migrated images: A validation study

Jensen

Lecomte

Gelius

et al. 2021

Geophysical Prospecting

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“…The strata exposed in the Festningen section is near vertically tilted due to its location at the eastern limb of the West Spitsber-making outcrop-based geomodels (Larssen et al 2020). These can be used for instance in flow simulations (Cabello et al 2018), or to generate synthetic seismic images of the outcrops (Lubrano-Lavadera et al 2019).…”

Section: The Festningen Profile -A Geological Track Recordmentioning

confidence: 99%

Digitising Svalbard’s Geology: the Festningen Digital Outcrop Model

et al. 2022

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hand samples to 'seismic-scale' outcrops at a fraction of the cost of terrestrial or helicopter-based lidar scanning that was routinely used previously (Buckley et al. 2008).Digital outcrop models irrespective of acquisition methods provide a high-resolution geo-referenced representation of the outcrops (Marques Jr et al. 2020). They are widely accessible to geoscientists and provide access to the outcrops at a fraction of the environmental and economic cost of traditional fieldwork, especially in remote areas like Svalbard. Furthermore, DOMs provide safe access to otherwise inaccessible steep cliffs or hazardous areas. Digitization of outcrops is also important for their preservation, for instance due to infrastructure construction covering the outcrop or coastal or other erosion. Finally, digital outcrops can be spatially integrated with other complementary data sets, for instance ground-penetrating radar or electrical resistivity tomography.The acquisition and processing of digital outcrop models by both lidar scanners and using structure-from-motion is well covered in the literature. Similarly, many uses of extraction of quantitative data from digital outcrops are available, including deciphering the sedimentological and structural evolution of an area. As such, digital outcrops represent ideal input data for parable to the advent of 3D seismic for the subsurface (Marques Jr et al. 2020) -the exponential generation of digital outcrop models using affordable consumer-grade hardware (especially drones, and in recent years also smartphone-integrated light detection and ranging, i.e. lidar, scanners; Luetzenburg et al. 2021). Coupled with user-friendly structure-from-motion software it is now possible to generate georeferenced digital outcrop models from

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“…The slimhole operations did not, however, allow full wireline characterisation and, for instance, only qualitative density measurements were taken [35]. Existing bulk density data derived through geotechnical characterisation (e.g., [34]) provide insufficient coverage to calibrate the entire log suite, as required for instance for seismic modelling [36]. The acquisition of additional bulk density data is therefore needed to quantify a continuous density log, which is vital to the on-going characterisation of the cap rock sequences.…”

Section: The Longyearbyen Co 2 Lab Data Sets and Sample Collectionmentioning

confidence: 99%

Digital Drill Core Models: Structure-from-Motion as a Tool for the Characterisation, Orientation, and Digital Archiving of Drill Core Samples

et al. 2020

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Structure-from-motion (SfM) photogrammetry enables the cost-effective digital characterisation of seismic- to sub-decimetre-scale geoscientific samples. The technique is commonly used for the characterisation of outcrops, fracture mapping, and increasingly so for the quantification of deformation during geotechnical stress tests. We here apply SfM photogrammetry using off-the-shelf components and software, to generate 25 digital drill core models of drill cores. The selected samples originate from the Longyearbyen CO2 Lab project’s borehole DH4, covering the lowermost cap rock and uppermost reservoir sequences proposed for CO2 sequestration onshore Svalbard. We have come up with a procedure that enables the determination of bulk volumes and densities with precisions and accuracies similar to those of such conventional methods as the immersion in fluid method. We use 3D printed replicas to qualitatively assure the volumes, and show that, with a mean deviation (based on eight samples) of 0.059% compared to proven geotechnical methods, the photogrammetric output is found to be equivalent. We furthermore splice together broken and fragmented core pieces to reconstruct larger core intervals. We unwrap these to generate and characterise 2D orthographic projections of the core edge using analytical workflows developed for the structure-sedimentological characterisation of virtual outcrop models. Drill core orthoprojections can be treated as directly correlatable to optical borehole-wall imagery data, enabling a direct and cost-effective elucidation of in situ drill core orientation and depth, as long as any form of borehole imagery is available. Digital drill core models are thus complementary to existing physical and photographic sample archives, and we foresee that the presented workflow can be adopted for the digitisation and digital storage of other types of geological samples, including degradable and dangerous ice and sediment cores and samples.

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Seismic modelling of metre-scale normal faults at a reservoir-cap rock interface in Central Spitsbergen, Svalbard: implications for CO2 storage

Cited by 4 publications

References 43 publications

Point‐spread function convolution to simulate prestack depth migrated images: A validation study

Point‐spread function convolution to simulate prestack depth migrated images: A validation study

Digitising Svalbard’s Geology: the Festningen Digital Outcrop Model

Digital Drill Core Models: Structure-from-Motion as a Tool for the Characterisation, Orientation, and Digital Archiving of Drill Core Samples

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