Multicomponent Modelling of the Valhall Field

O’Brien, Mary Jane; Whitmore, D.; Brandsberg‐Dahl, S.; Etgen, John; Murphy, G.E.

doi:10.3997/2214-4609.201407723

Cited by 10 publications

(5 citation statements)

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“…The reservoir has a much stronger PS than PP reflection coefficient; it is even possible to identify a converted event in the raw shot gather at around 6 seconds zerooffset time (Figure 17). Converted wave migrations of the synthetic data were presented by O'Brien et al (1999), and clearly show some of the benefits the PS waves have over the PP waves in this particular dataset when conventional imaging algorithms are used.…”

Section: Cigs For the Ps Converted Wavesmentioning

confidence: 90%

“…As a first test of the inverse GRT algorithm we will apply it to a synthetic dataset from a model with a low velocity lens: the gas-cloud model. This configuration can be viewed as a simplification of the geology at the Valhall field (O'Brien et al, 1999). The model consists of two layers separated by a ramp-like interface that illustrates a target reflector (reservoir)…”

Section: The Gas-cloud Modelmentioning

confidence: 99%

“…In this section we apply the imaging algorithm to the synthetic Valhall dataset (O'Brien et al, 1999). This is a multicomponent, synthetic dataset that was developed to study some of the imaging problems encountered at the Valhall field.…”

Section: The Synthetic Valhall Datamentioning

confidence: 99%

“…The reservoir has a mild dome shape and is composed of fractured chalk with an overburden dominated by shales and silts. Because of gas leakage from the reservoir into the overburden, imaging of the reservoir by conventional P-wave methods is truly difficult (Thomsen et al, 1997;O'Brien et al, 1999). Any of the existing P-wave images generally suffer from sag and lack of continuity below the gas-leakage zone (Thomsen et al, 1997;Hokstad, 1999).…”

Section: The Real Valhall Obs Datamentioning

confidence: 99%

“…We illustrate the performance of our method with two synthetic datasets and a real dataset from the Valhall field. Both the synthetic datasets are generated with isotropic elastic finite difference modeling to mimic the ocean bottom seismics from the Valhall field (O'Brien et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Focusing in dip and AVA compensation on scattering‐angle/azimuth common image gathers

2003

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Common image gathers (CIGs) in the offset and surface azimuth domain are used extensively in migration velocity analysis and amplitude versus offset (AVO) studies. If the geology is complex and the ray field becomes multi-pathed, the quality of the CIGs deteriorates. To overcome these problems, the CIGs are generated as a function of scattering-angle and azimuth at the image point. The CIGs are generated using an algorithm based on the inverse generalized Radon transform (GRT), but stacking only over migration dip angles. Including only dips in the vicinity of the geological dip, or focusing in dip, results in improved signal-to-noise ratio on the CIGs. Migration velocity analysis can be done using differential semblance. This is carried out by minimizing a function of the derivative of the CIGs with respect to horizontal coordinates (offset/azimuth or scattering-angle/azimuth), but AVO/AVA (amplitude versus angle) effects will degrade the performance of the velocity analysis. We overcome this problem by computing an incomplete inverse GRT compensating for AVA effects. The resulting CIGs can be used for velocity analysis based on differential semblance, and they can be stacked to produce improved images. The algorithms are developed for inhomogeneous anisotropic elastic media, but they have so far only been tested on imaging-inversion of PP and PS reflected waves in an isotropic elastic medium. This was done on two synthetic datasets generated by finite-difference modeling and ocean-bottom seismic (OBS) data from the Valhall field. We show that by performing the imaging of the real OBS data in the angle domain, it is possible to construct a well focused PP image of the Valhall reservoir directly beneath the "gas cloud" in the overburden. Brandsberg-Dahl et al.

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Section: Cigs For the Ps Converted Wavesmentioning

confidence: 90%

Section: The Gas-cloud Modelmentioning

confidence: 99%

Section: The Synthetic Valhall Datamentioning

confidence: 99%

Section: The Real Valhall Obs Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Focusing in dip and AVA compensation on scattering‐angle/azimuth common image gathers

2003

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Multi-attribute and Artificial Neural Network Analysis of Seismic Inferred Chimney-like Features in Marine Sediments: A Study from KG Basin, India

Ramu

Sain

2021

Journal of the Geological Society of India

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In marine seismic sections, features like “chimneys”, characterized by low similarity chaotic zones or vertical disordered characteristics arise due to migration of gas. Numerous interpretation tools have been developed for the identification of chimney features from seismic data. Here, a study over a 2D seismic reflection profile, acquired by CSIR-National Geophysical Research Institute (NGRI), Hyderabad in the Krishna-Godavari (KG) basin in 2010 was carried out. The data is optimally conditioned or post-processed before extracting the attributes such as the similarity, dip variance, energy, and frequency washout, to capture the chimneys on seismic data. Finally, the attributes are combined and trained over interpreter’s acquaintances through a non-linear multi-layer perceptron (MLP) neural network to compute a hybrid attribute, defined as the chimney meta-attribute. The neural training results into an overall normalized root mean squared (RMS) error of 0.6 to 0.7 between the train and test data sets. Three chimneys of ~1.5, ~0.9, ~1.3 km heights are observed over the seismic line. As chimneys represent gas migration paths, the computation of chimney attribute helps in understanding the genesis of gas-hydrates not only in the KG basin, studied here, but also in any other prospective basin.

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Some Facts, Data Analysis, and Examples

Rajput¹,

Thakur²

2016

Geological Controls for Gas Hydrate Formations and Unconventionals

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Multicomponent Modelling of the Valhall Field

Cited by 10 publications

References 0 publications

Focusing in dip and AVA compensation on scattering‐angle/azimuth common image gathers

Focusing in dip and AVA compensation on scattering‐angle/azimuth common image gathers

Multi-attribute and Artificial Neural Network Analysis of Seismic Inferred Chimney-like Features in Marine Sediments: A Study from KG Basin, India

Some Facts, Data Analysis, and Examples

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