Genetic Facies Analysis Using Seismic Geomorphology and Seismic Attributes in the Continental Shelf of Eastern Mexico

2009 ◽  
Author(s):  
Khaled Fouad ◽  
L. Frank Brown ◽  
William A. Ambrose ◽  
Dallas Dunlap ◽  
Mario Aranda-Garcia ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Facies Analysis ◽  
Seismic Attributes ◽  
Seismic Geomorphology

scholarly journals Application of seismic attribute analysis in fluvial seismic geomorphology

2019 ◽  
Vol 10 (3) ◽  
pp. 1009-1019 ◽  
Author(s):  
Shakhawat Hossain
Keyword(s):  
Spatial Distribution ◽  
Spectral Decomposition ◽  
Seismic Attributes ◽  
Development Planning ◽  
Depositional Environments ◽  
Seismic Attribute ◽  
Field Development ◽  
Seismic Geomorphology ◽  
Sand Bodies ◽  
Attribute Analyses

AbstractSeismic attributes can be important predictors, either qualitative or quantitative, of reservoir geometries when they are correctly used in reservoir characterization studies. This paper discusses seismic attribute analyses and their usefulness in seismic geomorphology study of Moragot field of Pattani Basin, Gulf of Thailand. Early to Middle Miocene fluvial channel and overbank sands are the reservoirs in Pattani Basin. Due to their limited horizontal and vertical distribution, it is not always possible to predict the geometry and distribution of these sands based on the conventional seismic interpretation. This study utilized various seismic attributes, e.g., RMS amplitude analysis, spectral decomposition, semblance and dip-steered similarity, RGB blending to image the geometry and the spatial distribution of sand bodies in horizon and stratal slices at different stratigraphic intervals. Attribute analyses reveal, at shallow stratigraphic levels, RMS and semblance can successfully identify channel-shaped sand bodies and mud-filled channels associated with channel belts. On the other hand in deeper stratigraphic intervals, sand distribution can be imaged more effectively by using spectral decomposition and dip-steered similarity volumes. High-frequency spectral decomposition slices can image thin sands, and low-frequency slices can image thick sands quite effectively in deeper intervals. RGB blending of different frequency slices is particularly useful in delineating channel systems of various dimensions at deeper intervals. These images show the distribution of sands and mud-filled channels at various stratigraphic levels. The width of channel belts varies from 200 m to 3 km. These channel belts are N–S or NW–SE oriented. From the channel pattern and their dimensions, depositional environments can be predicted. Mud-filled channels identified in the horizon slices will act as a connectivity barrier between sand bodies at either side of the channel. They can also act as lateral and up-dip seal to form stratigraphic traps. The seismic attribute analyses clearly show the geometry and spatial distribution of sand bodies. Hence, this method for predicting sand body geometry might help in field development planning as well as in reducing exploration risk.

Reservoir characterization using microseismic facies analysis integrated with surface seismic attributes

2016 ◽  
Vol 4 (2) ◽  
pp. T167-T181 ◽  
Author(s):  
Aamir Rafiq ◽  
David W. Eaton ◽  
Adrienne McDougall ◽  
Per Kent Pedersen
Keyword(s):  
Seismic Data ◽  
Principal Curvature ◽  
Facies Analysis ◽  
Shape Index ◽  
Seismic Attributes ◽  
Seismic Survey ◽  
3D Seismic ◽  
Frequency Distributions ◽  
Scaling Properties ◽  
3D Seismic Data

We have developed the concept of microseismic facies analysis, a method that facilitates partitioning of an unconventional reservoir into distinct facies units on the basis of their microseismic response along with integrated interpretation of microseismic observations with 3D seismic data. It is based upon proposed links between magnitude-frequency distributions and scaling properties of reservoirs, including the effects of mechanical bed thickness and stress heterogeneity. We evaluated the method using data from hydraulic fracture monitoring of a Late Cretaceous tight sand reservoir in central Alberta, in which microseismic facies can be correlated with surface seismic attributes (primarily principal curvature, coherence, and shape index) from a coincident 3D seismic survey. Facies zones are evident on the basis of attribute crossplots, such as maximum moment release rate versus cluster azimuth. The microseismically defined facies correlate well with principal curvature anomalies from 3D seismic data. By combining microseismic facies analysis with regional trends derived from log and core data, we delineate reservoir partitions that appear to reflect structural and depositional trends.

Unsupervised seismic facies analysis using wavelet transform and self-organizing maps

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. P9-P21 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Paulo Léo Osorio ◽  
Paulo Roberto Johann
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Facies Analysis ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Alternative Methods ◽  
Reservoir Properties ◽  
Self Organizing Maps ◽  
Seismic Facies Analysis ◽  
Self Organizing

Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.

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