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  • 1
    Publication Date: 2017-05-31
    Description: An integrated multidisciplinary workflow has been implemented for quantitative lithology and fluid predictions from prestack angle gathers and well-log data within the Realgrunnen Subgroup in the Goliat Field, southwestern Barents Sea. We have first performed a qualitative amplitude-variation-with-angle (AVA) attribute analysis to assess the spatial distribution of lithology and fluid anomalies from the seismic data. A simultaneous prestack elastic inversion was then carried out for quantitative estimates of the P-impedance and [Formula: see text] ratio. Probability density functions, a priori lithology, and fluid class proportions extracted from well-log training data are further applied to the inverted P-impedance and [Formula: see text] seismic volumes. The AVA qualitative analysis indicates a class IV response for the top of the reservoir, whereas anomalies from the AVA attribute maps agree largely with the clean sand probabilities predicted from the Bayesian facies classification. The largest misclassification in the lithology classification occurs between shaly sands and shales. A mixed lithology and fluid classification indicates a smaller degree of overlap and allows for the discrimination of hydrocarbon sands. Integration of a qualitative AVA analysis and a quantitative Bayesian probability approach helps in constraining the depositional facies variability within the Realgrunnen Subgroup. Finally, a possible influence of tectonic activity during the deposition of the Realgrunnen reservoir is inferred based on the facies distribution maps.
    Print ISSN: 2324-8858
    Electronic ISSN: 2324-8866
    Topics: Geosciences
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  • 2
    Publication Date: 2017-08-31
    Description: An integrated innovative multidisciplinary approach has been used to estimate effective porosity (PHIE), shale volume ([Formula: see text]), and sand probability from prestack angle gathers and petrophysical well logs within the Lower Triassic Havert Formation in the Goliat field, Southwest Barents Sea. A rock-physics feasibility study revealed the optimum petrofacies discriminating ability of extended elastic impedance (EEI) tuned for PHIE and [Formula: see text]. We then combined model-based prestack inversion outputs from a simultaneous inversion and an EEI inversion into a multilinear attribute regression analysis to estimate absolute [Formula: see text] and PHIE seismic attributes. The quality of the [Formula: see text] and PHIE prediction is shown to increase by integrating the EEI inversion in the workflow. Probability distribution functions and a priori petrofacies proportions extracted from the well data are then applied to the [Formula: see text] and PHIE volumes to obtain clean and shaly sand probabilities. A tectonic-controlled point-source depositional model for the Havert Formation sands is then inferred from the extracted sand bodies and the seismic geomorphological character of the different attributes.
    Print ISSN: 2324-8858
    Electronic ISSN: 2324-8866
    Topics: Geosciences
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  • 3
    Publication Date: 2020-08-17
    Description: Seismic sequences are stratigraphic units of relatively conformable seismic reflections. These units are intervals of similar sedimentation conditions, governed by sediment supply and relative sea level, and they are key elements in understanding the evolution of sedimentary basins. Conventional seismic sequence analyses typically rely on human interpretation; consequently, they are time-consuming. We have developed a new data-driven method to identify first-order stratigraphic units based on the assumption that the seismic units honor a layer-cake earth model, with layers that can be discriminated by the differences in seismic reflection properties, such as amplitude, continuity, and density. To identify stratigraphic units in a seismic volume, we compute feature vectors that describe the distribution of amplitudes, texture, and two-way traveltime for small seismic subvolumes. Here, the seismic texture is described with a novel texture descriptor that quantifies a simplified 3D local binary pattern around each pixel in the seismic volume. The feature vectors are preprocessed and clustered using a hierarchical density-based cluster algorithm in which each cluster is assumed to represent one stratigraphic unit. Field examples from the Barents Sea and the North Sea demonstrate that the proposed data-driven method can identify major 3D stratigraphic units without the need for manual interpretation, labeling, or prior geologic knowledge.
    Print ISSN: 0016-8033
    Electronic ISSN: 1942-2156
    Topics: Geosciences , Physics
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  • 4
    Publication Date: 2019-10-16
    Description: 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.
    Print ISSN: 0016-8033
    Electronic ISSN: 1942-2156
    Topics: Geosciences , Physics
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