Seismic attributes in reservoir characterization: an overview

Sanda, Oumarou; Djeddi, Mabrouk; Tabod, Tabod Charles; Margat, Jean; Ngos, Simon; Essi, Jean Marcel Abaté; Kamguia, Joseph

doi:10.1007/s12517-021-06626-1

Cited by 15 publications

(6 citation statements)

References 65 publications

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“…This approach is termed "independent" inversion because it initiates by extracting separate estimates of zero-offset P and S reflectivities from the seismic gathers. This is done using the Fatti equation which is given as: (1) where The third term from the above Fatti equation which consists of c 3 R D is dropped when considering how to extract R P and R S from an N-trace gather.…”

Section: Methodsmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

“…In exploration seismology, seismic reflection is employed to locate potential hydrocarbon traps [1] .…”

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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“…The seismic attributes are powerful tools for monitoring and predicting hydrocarbon reservoirs [31]. Seismic attribute measures the characteristics of interest in the exploration and exploitation of hydrocarbon resources [32].…”

Section: Seismic Attributesmentioning

confidence: 99%

“…Fig. (10): Applied frequency attribute on in-line and cross-lineThe application of seismic attributes in reservoir depiction involves deciphering reservoir properties such as porosity, thickness, lithology, direct hydrocarbon indicators (such as bright spot, flat spot, and dim spot), and traps (faults and anticlines)[31].…”

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confidence: 99%

Structural and seismic attribute analysis of the Paleocene carbonate reservoir from the Balkassar Field, Potwar Plateau, Pakistan

Khan,

Rehman

et al. 2024

Journal of Petroleum Research and Studies

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The structural and seismic properties of the Paleocene carbonate reservoir are re-evaluated, using 3D seismic data multi-attributes, to improve reservoir prediction accuracy rarely documented earlier. This study highlights a number of relevant post-stack seismic attributes for understanding the structural setup and seismic properties of a carbonate reservoir. The structural analysis was carried out using seismic interpretation, combined with multi-attribute analysis for prospect actualization and generation of a 3D structural model to determine the structural aspect. The structural interpretation reveals the presence of faults and an asymmetrical anticlinal structure demonstrating a four-way closure, confirming compressional tectonics. The high amplitude anomaly (-6 to 12) combined with the RMS attribute (23 to 32) suggests bright spots in the Paleocene carbonate reservoir and gas saturated zone, the variance attribute (0.05 to 0.03) highlights the presence of two major faults, and the lower instantaneous frequency demonstrates the presence of hydrocarbons. The attribute results indicate that the Lockhart Limestone possesses good reservoir potential as revealed by DHI (bright spot), gas saturated zone and a trapping mechanism supported by four-way structure closure in the region and can help to understand analogous carbonate reservoirs in similar geological settings around the world.

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“…Hence, Over the last several decades, geoscientists have devoted significant effort to computer-aided fault interpretation by inventing novel methods, algorithms and significant characteristics to assist in detecting, depicting, and extracting the faults of interpretational importance from the surrounding unfaulted features [3,4]. Numerous seismic volume attributes have been developed and implemented for detecting lateral discontinuity by improving visual appearance of faults and fractures through seismic images [1, 7,8]. Seismic properties are also important in getting a deeper understanding into the fault and fracture systems and their linkages [9].…”

Section: Introductionmentioning

confidence: 99%

An Improved Seismic Fault Interpretation in a Structurally Complex Geologic Setting Using a Pretrained CNN Model and Seismic Attributes: An Example from the Browse Basin, Australia

Islam¹,

Elsaadany²,

Babikir³

et al. 2023

Preprint

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Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.

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Seismic attributes in reservoir characterization: an overview

Cited by 15 publications

References 65 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

Structural and seismic attribute analysis of the Paleocene carbonate reservoir from the Balkassar Field, Potwar Plateau, Pakistan

An Improved Seismic Fault Interpretation in a Structurally Complex Geologic Setting Using a Pretrained CNN Model and Seismic Attributes: An Example from the Browse Basin, Australia

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