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.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

Artificial immune-based self-organizing maps for seismic-facies analysis

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. O45-O53 ◽  
Author(s):  
Puneet Saraswat ◽  
Mrinal K. Sen
Keyword(s):  
Immune System ◽  
Data Reduction ◽  
Seismic Data ◽  
Artificial Immune System ◽  
Facies Analysis ◽  
Seismic Facies ◽  
Artificial Immune ◽  
Self Organizing Maps ◽  
Data Set ◽  
Seismic Facies Analysis

Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.

Unsupervised seismic facies analysis via deep convolutional autoencoders

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A39-A43 ◽  
Author(s):  
Feng Qian ◽  
Miao Yin ◽  
Xiao-Yang Liu ◽  
Yao-Jun Wang ◽  
Cai Lu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Seismic Data ◽  
Facies Analysis ◽  
Recognition Task ◽  
Seismic Facies ◽  
Self Organizing Maps ◽  
Convolutional Autoencoder ◽  
Unsupervised Deep Learning ◽  
Rich Information ◽  
Seismic Facies Analysis

One of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate facies maps. Therefore, it is promising to use prestack seismic data for the seismic facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data-driven offset-temporal feature extraction approach using the deep convolutional autoencoder (DCAE). As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic facies analysis can be accomplished through the use of conventional classification or clustering techniques (e.g., K-means or self-organizing maps). Using a physical model and field prestack seismic surveys, we comprehensively determine the effectiveness of our scheme. Our results indicate that DCAE provides a much higher resolution than the conventional methods and offers the potential to significantly highlight stratigraphic and depositional information.

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