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RECOGNITION OF DEEP-WATER DEPOSITS USING DIGITAL IMAGE EDGEDETECTION TECHNIQUES AND MACHINE LEARNING ROUTINES.
(COLOMBIA-OFFSHORE).
Marcelo Garcia-OcampoEcopetrol S.A. ([email protected]) 2019
Objectives
Description
Assumptions
Constrains imposed data
Define each attribute
Motivation
Benefits
Use
1. Problem
Definition
1
2
3
4
Magdalena fan delta play - 2013
• High impedance contrast“Reservoir distribution”
• Define container boundaries
Channel like
Sheet sands like
2. Prepare
Data
Data structure
Data Distributions
Attribute Histograms
Pairwise scatterplots of attribute
Formatting
Cleaning
Sampling
Scaling
Decomposition
Aggregation
METHOD
Edge detection
K-means clustering
Gaussian mixture models
Pearson correlation coefficient
-1 0 1
-2 0 2
-1 0 1
1 2 1
0 0 0
-2 -1 -1
a) b)
Gx Gy
a) Sobel operator of a pair of 3x3 convolution kernels. b) Same a) but rotated 90°.
2
1
(Gradient based - derivative based)
2. Prepare
Data
Threshold 120 Threshold 130 Threshold 140
500-1000
1000-1500
Sobel edge convolutional Kernel application and uncertainty capture. (geobody)
Equalized threshold
10-5002
3
Data structure
Data Distributions
p10 p50 p90uncertainty
3. Analyze
Data
Euclid
ean
distan
ces from
centro
id to
samp
le
Inten
sity distrib
utio
n (N
et to gro
ss ratio)
Channel complex Offset stacked
Confined channel complex
Sand sheets
-0.61
0.11
similarity d
istribu
tion
0.80
1
Extracted features from unsupervised
algorithms.
2
3
Attribute Histograms
Pairwise scatterplots of attribute
Formatting
Cleaning
Sampling
Scaling
Decomposition
Aggregation
K-means clustering Gaussian mixture models Pearson correlation coefficient
Euclideandistance
Eu dist
Eu dist
#
#
#
0 256
0 256
0 256
0 256
#
#
#
#
confidence ellipse high
low
fair
fair
ARCHITECTURESIZERESERVOIR
Covariance matrix approach
Algorithms Tunning
Ensemble metthods
Interpret and report results
Test Harness and Options
Explore and select algorithms
4. Evaluate
Algorithms
5. Improve Results
Euclidean distances approach
COVARIANCE INDIVIDUALIZATION
FINAL PRODUC TO SEISMIC DATABASE.
Max Euc dist to centroid
GAUSSIAN MIXTURE MODELS
Max Euc dist to samples
confidence ellipse
DISTRIBUTIONS AND LIMITS
5. ResultsChannel types
Lobe types
1500 m
50
0 m
Channel types
Lobe types
0.05
-0.79
0.50.81-0.20
0.9
Clusters' Confidence ellipses
Pccsegments ranked by size
0 256
#
N/G
Representation Architecture of dataset
5. conclusions
ACKNOWLEDGEMENTS:
I would like to thank Ecopetrol S.A. for allowing me to use the data and Ecopetrol Óleo e Gás do Brasil Ltda.
for supporting this work and to attend this meeting.
• The stated objectives were accomplished, detect the main deposits, allowing the ranking based on size, assess its N/G ratio.
• Capture from unsupervised ML techniques features to describe architecture/genetic characteristics.
•The covariance matrix which dictates the maximum similarity per cluster is in agreement with the prograding direction of its belonging channel.
•The Pearson correlation coefficient commutated from each hyperellipsoidally shaped clouds refers as well to the distribution of channels classes moving from 0.4 to -0.4, for complex channels deposits, and out this range for sand sheets deposits (e.i. lobes).
REFERENCES
Benbrahim, M., Daoudi, A., Benjelloun, K., and Ibenbrahim, A. (2005). Discrimination of Seismic Signals Using Artificial Neural Networks, 2nd World Enformatika Conference, Istanbul, Turkey, February, 4-7.
Channel and sheet architectural Styles in offshore Colombia data (blocks RC4 and RC5)., Equion Limited 2011. Interim report
Cronin. Brain Seismic Expression of Deep-water Slope Channel Complex & Frontal Splay Architectural Elements: Calibration with Outcrop & Sea Floor Analogues from Southern and Eastern Turkey & Various Modern Systems., Third EAGE Workshop on Rock Physics., 2015.DOI: 10.3997/2214-4609.201414385
data analysis and intelligence systems Automatic detection of channels in seismic images via deep learning neural networks., 2019.
E. E. Elmahdy and A. W. Aboutahoun, A new Approach for Parameter Estimation of Finite Weibull Mixture Distributions for Reliability Modelling, Applied Math. Modelling37(4) Feb. (2013) 1800–1810.
Ercilla et al., The Magdalena Turbidite System (Caribbean Sea): present-day morphology and architecture model., Marine Geology 185 (303-318), 2002.
Kaur Sabibir, Singh Ishpreet," Comparison between Edge Detection Techniques ", IJICT, Volume No. 15, July 2016.
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Orozco-Alzate et.al. 2006., DISSIMILARITY-BASED CLASSIFICATION OF SEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO,.Earth Sci. Res. J. Vol. 10, No. 2 (December 2006): 57-65
Sobel, I., and G. Feldman, 1968, A 3x3 isotropic gradient operator for image processing: Stanford Artificial Project, 271–272.
Thank you