3D seismic volume visualization and interpretation: An integrated workflow with case studies

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. W1-W12 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Volume Visualization ◽  
Seismic Facies ◽  
Data Selection ◽  
3D Seismic ◽  
Well Bore ◽  
Stratigraphic Architecture ◽  
Facies Characterization ◽  
3D Seismic Data

One of the major problems in subsurface seismic exploration is the uncertainty (nonuniqueness) in geologic interpretation because of the complexity of subsurface geology and the limited dimension of the data available. Case studies from worldwide exploration projects indicate that an integrated, three-dimensional (3D) seismic volume visualization and interpretation workflow contributes to resolving the problem by mining and exposing critical geologic information from within seismic data volumes. Following 3D seismic data acquisition and processing, the interpretation workflow consists of four integrated phases from data selection and conditioning, to structure and facies characterization, to prospect evaluation and generation, to well-bore planning. In the data selection and conditioning phase, the most favored and frequently used data are the full-angle, limited-angle, and limited-azimuth stack amplitude with significant structure and facies enhancements. Signal-to-noise ratio, color scheme, dynamic range, bit resolution, and visual contrast all affect thevisibility of features of interest. In the structure and facies characterization phase, vertical slicing along arbitrary traverses demonstrates structure styles, stratigraphic architecture, and reservoir geometry in the cross-sectional view. Time/depth slicing defines lateral and vertical variability in the structural trend and areal extent in the map view. Stratal slicing and fault slicing map chronostratigraphic seismic facies and cross-stratal, along-fault seismic signature. Volume flattening and structure restoration aid in unraveling paleostructural framework and stratigraphic architecture and their growth histories. In the prospect evaluation and generation phase, a combination of volume trimming, co-rendering, transparency, attribute analysis, and attribute-body detection is instrumental in delineating volumetric extent and evaluating spatial connectivity of critical seismic features. Finally, in the well-bore planning phase, informed decision-making relies on the integration of all the information and knowledge interrogated from 3D seismic data. Most importantly, interpreters’ geologic insight and play concept are crucial to optimal well-bore planning with high geologic potential and low economic risk.

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

3D seismic data attribute-based characterization of volcanic reservoirs in the BZ34-9 Block, Bohai Bay Basin, eastern China

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. IM1-IM13
Author(s):  
Hongtao Zhu ◽  
Zhiwei Zeng ◽  
Hongliu Zeng ◽  
Changgui Xu
Keyword(s):  
Seismic Data ◽  
Eastern China ◽  
Seismic Facies ◽  
Bohai Bay ◽  
3D Seismic ◽  
Bohai Bay Basin ◽  
Eruption Style ◽  
Volcanic Facies ◽  
3D Seismic Data

Volcanic effusive facies (VEF) and volcanic conduit facies (VCF) are two important facies units that can be found in a volcanic reservoir or edifice. Because VEF and VCF generally exhibit opposing seismic reflection characteristics, few studies have been applied to simultaneous characterization of the two facies in seismic data. We have developed an integrated 3D seismic data attribute-based characterization technique of VEF and VCF in the BZ34-9 Block, Bohai Bay Basin, eastern China. Our method is based mainly on the 3D visualization of a thresholding display so as to separately describe the strong-amplitude reflection of the VEF with its original amplitude attribute and the weak-amplitude chaotic reflection of the VCF with its variance-cube attribute. The detailed workflow comprises four steps, including seismic facies analysis, characterization of the VEF, characterization of the VCF, and merging a display of the two volcanic-facies units. The resulting 3D image of the different volcanic facies described in the BZ34-9 Block should be able to be viewed from any perspective for a better understanding of the related genesis mechanisms of the first and second members of the Shahejie (Es12) and Dongying Formations (Ed). In total, 28 volcanic edifices have been identified on the basis of the proposed method, among which three volcanic edifices exhibited inherited eruptions, in the Es12 and the Ed. Volcanic edifices in the Es12 are distributed locally in the central part of the BZ34-9 Block, showing a central eruption style, whereas those of the Ed are characterized by a widespread distribution in the southern gentle slope of the BZ34-9 Block, revealing a composite, center-fissure eruption style. The approach should be convenient to operate and would be effective in characterizing different volcanic facies simultaneously. This application can serve as a useful reference for other basins or regions with obvious volcanic influence.

scholarly journals Delineation of shallow channel geometry and infill lithology using Spectral decomposition and seismic attributes: A case study from the North Sea Basin, Netherlands

2017 ◽  
Author(s):  
Kenneth Samuel Okiongbo ◽  
Righteous Ombu
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Negative Curvature ◽  
Seismic Facies ◽  
3D Seismic ◽  
Channel Geometry ◽  
The North ◽  
The North Sea ◽  
3D Seismic Data

Abstract. In the Southern North Sea, 3D seismic data had been widely acquired to explore for hydrocarbons, but interpretations of these datasets until now focus mainly on the deep exploration targets of the petroleum companies. Less attention is given to shallow sediments. But these sediments often contain channels that can serve as potential reservoir units. Thus the mapping and identification of these shallow channels and defining their infill lithology is important. In this study, seismic spectral decomposition technique has been used to delineate shallow thin channel geometry in a 3D seismic data acquired in the Dutch sector of the North Sea. The concurrent interpretation of curvature and coherence cubes with seismic facies analysis based on reflection terminations and geometry, amplitude and continuity enables the discrimination between shale versus sand filled channels. The results of the spectral decomposition show two distinct low sinuosity channel features in NNE–SSW direction but becomes diffuse towards the North. The strong negative curvature anomaly along the channels's axes observed in the most negative curvature attribute implies that the sediments within the channels have undergone more compaction. These strong negative curvature anomalies are interpreted to be due to differential compaction of shale filled channels.

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