Sparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm

Li, Chuanhui; Liu, Xuewei

doi:10.1111/1365-2478.13211

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Unlike seismic reflectivity, which occurs at the interfaces of different strata, AI values remain constant within rock layers, simplifying the link with geology and stratigraphy. Inversion is commonly used to extract impedance from seismic reflection data [7] .…”

Section: Introductionmentioning

confidence: 99%

Amplitude Variation with Offset (AVO) Inversion for Reservoir Visualization: A Case Study of Taje Field, Niger Delta, Nigeria

Oborie,

Francis,

Eteh

2024

adv. in geol. and geotech. eng. res.

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Amplitude Variation with Offset (AVO) inversion analysis was performed on pre-stack seismic data and well information gathered from the shallow offshore area of the Niger Delta. This analysis aimed to improve reservoir visualization and employed the Hampson Russell Geoview, AVO, and STRATA software tools. The seismic data were provided in Seg-Y format, covering an in-line range from 4503 to 5569, an x line range from 1434 to 2026, and an angle of incidence range of 0 to 45°. The study centered on the Taje well_026. Within the subsurface, the authors identified five distinct reservoirs, labeled A to E, located at various depths ranging from 3057.50 to 3115.00 m, 3115.00 to 3157.50 m, 3157.50 to 3190.00 m, 3190.00 to 3200.00 m, and 3200.00 to 3239.00 m, respectively. These reservoirs exhibited different fluid compositions. Reservoir A, primarily composed of sandstone, contained brine, whereas Reservoirs B and D, dominated by shale, contained gas. On the other hand, Reservoirs C and E, both comprised of sandstone, held oil. Reservoir C is distinguished by its clean sandstone unit. The inversion results revealed that both Reservoirs C and E consisted of low impedance sand layers surrounded by higher impedance shale layers. The gas migrated from the reservoir and was trapped within the shale units due to deformation of the lithological units, likely induced by stress accumulation. This migration process was facilitated by the shale’s inability to undergo smearing, possibly as a result of faulting mechanisms.

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Section: Introductionmentioning

confidence: 99%

Amplitude Variation with Offset (AVO) Inversion for Reservoir Visualization: A Case Study of Taje Field, Niger Delta, Nigeria

Oborie,

Francis,

Eteh

2024

adv. in geol. and geotech. eng. res.

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An initial model construction method constrained by stratigraphic sequence representation for pre‐stack seismic inversion

Chen,

Zou,

Wang

et al. 2024

Geophysical Prospecting

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The construction of an accurate and high‐resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band‐limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre‐stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three‐dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre‐stack seismic inversion. The main innovation of the proposed method is the three‐dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S‐wave velocity well logging data.

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High dynamic range land wavefield reconstruction from randomized acquisition

Pawelec,

Sava

2024

GEOPHYSICS

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Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in afford- able seismic acquisition which can lead to improvements in the near surface mapping and in noise suppression for land data. However, the near surface traps the majority of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range non-stationary data, the Fourier domain is not optimal for providing a sparse representation - a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation- dependent iterative soft thresholding scheme (IST) for reconstructing high dynamic range wavefields. Our approach requires little parametrization, is easy to implement, and robust to reconstructed wave- field sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces, and compare the data reconstructions to the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric de-emphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding a good phase match while performing gentle denoising.

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Sparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm

Cited by 3 publications

References 30 publications

Amplitude Variation with Offset (AVO) Inversion for Reservoir Visualization: A Case Study of Taje Field, Niger Delta, Nigeria

Amplitude Variation with Offset (AVO) Inversion for Reservoir Visualization: A Case Study of Taje Field, Niger Delta, Nigeria

An initial model construction method constrained by stratigraphic sequence representation for pre‐stack seismic inversion

High dynamic range land wavefield reconstruction from randomized acquisition

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