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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. (Marcelo.garcia@ecopetrol.com.co) 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.

Machine Learning for Subsurface Characterization1st Edition 2020. El sevier

Nasiriany, Garrett Thomas, William Wang, Alex Yang.,A Comprehensive Guide to Machine Learning SoroushDepartment of Electrical Engineering and Computer Sciences University of California, Berkeley August 13, 2018

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Nam Phuong Pham,.The Thesis Automatic channel detection using deep learning,.THE UNIVERSITY OF TEXAS AT AUSTIN May 2019.

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