A Novel Source-Over-Cable Solution To Address The Barents Sea Imaging Challenges

Lie, J.E.; Danielsen, V.; Dhelie, P.E.; Sablon, Ronan; Siliqi, R.; Grubb, Claire; Vinje, Vetle; Nilsen, C.I.; Soubaras, Robert

doi:10.3997/2214-4609.201802101

Cited by 6 publications

(2 citation statements)

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“…The seismic data used in this case example are post stack time migrated (PSTM) TopSeis data from the first-ever marine source-over-cable acquisition (Lie et al, 2018) with a bandwidth of 4-110 Hz. The data cover 1000 inlines and 400 crosslines from 0.7 to 1.6 s, with a bin size of 25 m and a sample rate of 4 ms. We use a window size of 2.5 × 2.5 km and a grid step of 0.5 km.…”

Section: Heavily Faulted Triassic Strata Southwestern Barents Seamentioning

confidence: 99%

A case study on semiautomatic seismic interpretation of unconformities and faults in the southwestern Barents Sea

et al. 2018

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Recently, there has been a growing interest in automatic and semiautomatic seismic interpretation, and we have developed methods for extraction of 3D unconformities and faults from seismic data as alternatives to conventional and time-consuming manual interpretation. Our methods can be used separately or together, and they are time efficient and based on easily available 2D and 3D image-processing algorithms, such as morphological operations and image region property operations. The method for extraction of unconformities defines seismic sequences, based on their stratigraphic stacking patterns and seismic amplitudes, and extracts the boundaries between these sequences. The fault-extraction method extracts connected components from a coherence-based fault-likelihood cube where interfering objects are addressed prior to the extraction. We have used industry-based data acquired in a complex geological area and implemented our methods with a case study on the Polhem Subplatform, located in the southwestern Barents Sea north of Norway. For this case study, our methods result in the extraction of two unconformities and twenty-five faults. The unconformities are assumed to be the Base Pleistocene, which separates preglacial and postglacial Cenozoic sediments, and the Base Cretaceous, which separates the severely faulted Mesozoic strata from prograding Paleocene deposits. The faults are assumed to be mainly Jurassic normal faults, and they follow the trends of the eastern and southwestern boundaries of the Polhem Subplatform; the north–south-trending Jason Fault complex; and the northwest–southeast-trending Ringvassøy-Loppa Fault complex.

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Section: Heavily Faulted Triassic Strata Southwestern Barents Seamentioning

confidence: 99%

A case study on semiautomatic seismic interpretation of unconformities and faults in the southwestern Barents Sea

et al. 2018

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“…Thus limiting the amount of recorded reflected energy, especially in situations with large velocity contrasts in the shallow. Seismic imaging benefits from near offset data in order to record the narrow cone of reflected energy in these velocity-depth settings (Lie et al, 2018). In order to address the shallow-target seismicimaging issue, CGG and Lundin Norway proposed in 2017 a tailored acquisition solution, known as TopSeis (Vinje et al, 2017), yielding improved recording of the the near offsets.…”

Section: Introductionmentioning

confidence: 99%

Cross-streamer wavefield interpolation using deep convolutional neural network

Greiner

Kolbjørnsen

Lie

et al. 2019

SEG Technical Program Expanded Abstracts 2019

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Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broadband synthetic data and TopSeis field data from the Barents Sea.

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Loppa High Composite Tectono-Sedimentary Element, Barents Sea

Brunstad

Ronnevik

2023

Memoirs

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The Loppa High Composite Tectono-Sedimentary Element is a mature exploration area in the south-western Norwegian Barents Sea hosting Skrugard (2012) and the Late Paleozoic Gohta (2013) discoveries. The exploration of the Loppa High started in 1985, and today this is the most studied area of the Barents Shelf which subsurface mapping is based on dense coverage of modern 3D and 2D seismic data and drilling results of over 40 exploration wells. This chapter summarizes the tectono-sedimentary and sub regional development and the petroleum geology of the Loppa High.

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A Novel Source-Over-Cable Solution To Address The Barents Sea Imaging Challenges

Cited by 6 publications

References 0 publications

A case study on semiautomatic seismic interpretation of unconformities and faults in the southwestern Barents Sea

A case study on semiautomatic seismic interpretation of unconformities and faults in the southwestern Barents Sea

Cross-streamer wavefield interpolation using deep convolutional neural network

Loppa High Composite Tectono-Sedimentary Element, Barents Sea

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