Use of windowed seismic attributes in 3D seismic facies analysis and pattern recognition

2018 ◽  
Author(s):  
D.C. Carter
2021 ◽  
Author(s):  
Andrei Tsyhankou ◽  
Alyaksandr Kanyushenka ◽  
Alyaksandr Hrudzinin ◽  
Alyaksei Kudrashou

Abstract The results of the well 10s2-Savichskaya drilling, laboratory core research are set out. Based on the results of integration the latest methods of wire line survey, laboratory core samples research, seismic facies analysis, typical lithotypes of the Savichsko-Bobrovichi area rocks were identified, reservoir features were predicted, the prospects of inter-salt deposits for identifying accumulations of hydrocarbons in unconventional reservoirs were substantiated. A perspective zone was identified and recommendations for drilling a pilot bore were given.


Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. P1-P11 ◽  
Author(s):  
Iván Dimitri Marroquín ◽  
Jean-Jules Brault ◽  
Bruce S. Hart

Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid content of the strata being imaged. Unfortunately, it is not always clear whether the seismic data has a natural clustering structure. Cluster analysis consists of a family of approaches that have significant potential for classifying seismic trace shapes into meaningful clusters. The clustering can be performed using a supervised process (assigning a pattern to a predefined cluster) or an unsupervised process (partitioning a collection of patterns into groups without predefined clusters). We evaluate and compare different unsupervised clustering algorithms (e.g., partition, hierarchical, probabilistic, and soft competitive models) for pattern recognition based entirely on the characteristics of the seismic response. From validation results on simple data sets, we demonstrate that a self-organizing maps algorithm implemented in a visual data-mining approach outperforms all other clustering algorithms for interpreting the cluster structure. We apply this approach to 2D seismic models generated using a discrete, known number of different stratigraphic geometries. The visual strategy recovers the correct number of end-member seismic facies in the model tests, showing that it is suitable for pattern recognition in highly correlated and continuous seismic data.


Geophysics ◽  
2002 ◽  
Vol 67 (5) ◽  
pp. 1372-1381 ◽  
Author(s):  
Frédérique Fournier ◽  
Pierre‐Yves Déquirez ◽  
Costas G. Macrides ◽  
Marty Rademakers

A lithostratigraphic interpretation of seismic data sets covering the Unayzah fluviatile formation in central Saudi Arabia has allowed us to map the sandstone distribution and to characterize the average porosity of the formation. First, sandstone distribution was predicted through seismic facies identification and interpretation with well information. Seismic facies analysis was performed with statistical pattern recognition applied to the portions of traces at the reservoir level, these traces being characterized by a series of seismic attributes. A good convergence of results from unsupervised and supervised pattern recognition was observed. This increases the confidence in the interpretation of sandprone facies. Second, using a statistical relationship between the reservoir average porosity defined at the wells and selected amplitudes of adjacent traces, the porosity was predicted all over the area covered by the seismic information. The model was based on a multivariate linear regression, showing satisfying quality criteria (correlation coefficient, residuals, etc.). The porosity variation predicted from the seismic data complements the sandstone distribution, derived from the seismic facies analysis. In particular, some areas where sandstones are predicted do not appear as porous as could have been suspected from their lithological content, perhaps as the result of diagenetic effects. Last, seismic facies analysis with pattern recognition applied to 2‐D exploratory lines, partly intersecting the 3‐D data set, led to the identification of potential prospects (Unayzah interval with a high sand–shale ratio).


Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. P9-P21 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Paulo Léo Osorio ◽  
Paulo Roberto Johann

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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