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DIP workshop– Seismic interpretation – 14th December 2010

A knowledge-based approach of 3D seismic interpretation : a first step to shared earth modeling by means of

cognitive vision

Application to the detection of horizons and faults

Philippe Verney (IFP/ENSMP)Monique Thonnat (INRIA Sophia Antipolis)Jean François Rainaud (IFP)

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2 DIP workshop– Seismic interpretation – 14th December 2010

Agenda

Introduction Common methodology Proposed methodology Horizon detection Fault detection Results Conclusion and

perspectives

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Introduction Area of oil exploration

Aims to compute the amount of hydrocarbon extraction in an oil field.

Goal Where is the oil located? Which objects

(surfaces) do mark the boundaries of this zone? How to answer?

By representing the subsurface for a geological study.

First step The seismic interpretation : to transform a

seismic image into a structural model (horizons, faults and their mutual relationships).

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Input : the seismic cube More or less

coarse representation of the subsurface over several square kilometers.

Store the amplitude reflection of the interfaces.

Vertical succession of lines (black and white on the left image) called “reflectors”

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Another input : the well markers

Intersection between the trajectory of a well and a geological surface identified either by a geophysical method or by sample cores.

Most accurate representation of a geological surface.

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Output

Interpretation of the global structure of the subsurface.

Relationships between those surfaces to build a structural model.

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Summary Initial stages of the reservoir modeling workflow (seismic data

-> structural model) Input : A seismic block and well markers. Output : Necessary information for building an initial structural

model (horizons, faults and their mutual relationships)

Production forecast

Reservoir grid Upscaling

Flow simulationGeological model :facies, porosity, permeability

Structuralmodel

Well and seismicdata

Facies proportions

Stratigraphicmodel

Our work

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Agenda


perspectives

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2 main steps Surface extraction by picking and tracking.

To link those surfaces into one structural model.

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Issues and current solutions The latter operations are not automatic. They

need a user having a very high level of geological and geophysical knowledge for. Choosing where to pick Choosing the tracker parameters Choosing how to link the extracted surfaces

Current help Seismic attributes [Chopra, 2005]

Intend to output a subset of the data that quantifies a specific seismic characteristic interpreters are interested in. For instance, the local continuity or dip of reflectors.

Neural networks [Meldhal, 2000] Intend to highlight objects using several seismic

attributes and “learning” from one similar identification executed by an expert user.

Image processing (after operations Ant Tracking [Randen, 2001] or before operations [Barnes, 2006])

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Lack of the current solutions

Data based and not enough knowledge based. The software doesn’t really propose

interpretations of the seismic block by itself. It just modifies the seismic block in order to provide an easier interpretation for the user.

No information on extracted data The reasons which lead to one surface

extraction are not stored with the data. Most of time, only the geometry of the surfaces are stored.

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Agenda

Introduction Common methodology Proposed methodology Horizon detection Fault detection Results Conclusion and perspectives

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Main idea

Seismic interpretation must first rely on geological knowledge. Need to formalize this knowledge for a use with

computers (Geological Evolution Schema [Perrin, 1998])

Inspirations Cognitive vision for a knowledge based image

interpretation Mainly Orion/Pulsar team (INRIA Sophia Antipolis)

[Hudelot, 2005], [Thonnat, 2002] Semantic web for the formalization and the

exchange of the knowledge Ontologies [Monnin, 2009] and OWL [Smith, 2004]

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Ontology

This is a formal model used in order to describe a particular domain of the real world [Fensel, 2003][Staab, 2004]

In an ontology, the concepts define the basic elements. They are described by means of specific properties (attributes) and can be represented by means of several individuals (instances).The concepts can also be linked together by relationships.

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Architecture Like Marr [Marr, 1982] and Hudelot [Hudelot,

2005], we define 3 abstraction levels : The image processing for extraction and numerical

description of candidates. The characterization of these candidates by means of

relations and attributes representing their visual properties

The decision whether a defined candidate can be associated to some geological object.

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Architecture

The 3 levels are linked to an ontology (called SDVP for Seismic Data Visual Properties) which is implemented in OWL. This ontology formally stores the used knowledge (by means of concepts, relations, attributes and instances) all along the three different levels.

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Agenda


perspectives

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Knowledge representation Sets of reflectorsreflectors (lateral successions of points having

approximately the same amplitude) 1 horizon = several reflectors sharing

the same visual attributes thickness, orientation (dip), colour (amplitude value)

the same time relationships with other reflectors the same distance relationships with a one same well

marker.

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Knowledge representation

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Data ManagementDetection of first order reflectors

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Data Management

+ ++ ++ ++ ++

++ ++ + + ++

++ + - - +

+ - -- -- +

- -- -- -- --

-- - - + -

- - + ++ +

+ + ++ ++ ++

++ ++ ++ + -

- + + - --

-- - - -- -

++ -- -- + ++

A seismic image is a lateral succession of traces.

Each trace is composed by a vertical succession of positive and negative ranges of values.

Detection of first order reflectors

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Data Management

Threshold values are put on amplitudes for reducing noise and decreasing the necessary memory size.

Thinning of the result in order to obtain a surface 1 pixel thick.

Labelization of connected voxels (without Z multi evaluations).

Detection of first order reflectors

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Data ManagementDetection of first order reflectors

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Visual Characterization Affect to the detected reflectors the attributes and

relationships defined in the SDVP ontology visual attributes

thickness, orientation (dip), colour (amplitude value)

time relationships with other reflectors distance relationships with a one same well marker.

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25 DIP workshop– Seismic interpretation – 18 April 2023

Visual Characterization

Mean amplitude computing.

Thickness computing.

2D dip computing.

Visual attributes

4

3

2

2

++

-

+

-

Distance to well marker computing.

11,4

3,2

4

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Vertical distance (in voxels) to the first visible reflectors (upwards and downwards).

Construction of a locallocal chronological relation graph.

Time relationships of one reflector with respect to the others

8 49

2

3 54

A

B

C

D

E

F G

H

I

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H

G

D C

+

Visual relationships

A

B

CD

F

E H

G

G

A

HD

C

H

G

C

D

A

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Geological Correlation Identify the geological horizons corresponding

to the various detected reflectors

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Visual relationsThicknessDipAmplitude

Geological CorrelationHorizon identification

A

B

CD

F

E H

G

H

G

C

D

A

B

E

F

30

10

5

42 39

23 19

BIG

BIGBIG

BIG

LITTLE MEDIUM

MEDIUM

MEDIUM

HIGH

HIGH

MEDIUM

MEDIUM

LOW LOW

LOW LOW

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H

G

C

D

A

B

E

F

42 39

23 19

C

Correlation

Geological CorrelationHorizon identification

A

B

CD

F

E H

G A

B

CD

F

E H

G

H

E+G

D

A

B

F

40.5

23 19

C

E+G

D

A

B

F+H

40.5

22

C

CD

F

H

G

Well markers

H1

H5H5

H1

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Detection of the patches that identify the same horizon

Merging of all the nodes that share the same attributes and relations.

Results on a real block

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Agenda


perspectives

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In seismic data, dip-fault mirrors are observed as successions of horizon vertical disconnections producing reflector gapsreflector gaps.

1 dip fault = several reflector gaps sharing the same visual attributes

dip, heave (through crosslines and inlines) throw

belonging to one same plane University of Wisconsin-Stevens Point

http://www.uwsp.edu/geo/faculty/hefferan/geol320/faults13.jpg

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Data ManagementDetection of reflector gaps

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Data ManagementDetection of reflector gaps

Exploration of each reflector by means of successive crosslines inlines. Inlines

Cro

sslin

es

Grid of Z values attached to each reflector

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Visual Characterization Affect to the detected reflector gaps the attributes and

relationships defined in the SDVP ontology visual attributes

dip, heave (through crosslines and inlines) throw

co-planarity with other reflector gaps

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Visual CharacterizationVisual attributes

Exploration of each reflector by means of successive crosslinescrosslines.

Inlines

Cro

sslin

es

Throw = 1

Heave = 2

Dip = 26°

-> NO

Throw = 16

Heave = 4

Dip = 76°

->YES


Constraints :

10 < Throw < 100

1 < Heave < 10

20°< Dip < 80°

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Exploration of each reflector by means of successive inlinesinlines.

Inlines

Cro

sslin

es Throw = 16

Heave = 4

Dip = 76°

->YES


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Labelization of connected voxels having the same visual attributes.

Inlines

Cro

sslin

es


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Results on a real blockDetection and characterization of the reflector gaps

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Geological Correlation Identify the geological dip faults

corresponding to the various detected reflector gaps.

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Dip fault identification on a cross-section Merge all the reflector gaps, which share the same visual

attributes and which are coplanar.

Geological Correlation

3

4

4

2

2

2

40°

35°

35°

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Results on a real blockDip faults identification

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Agenda

Introduction Common methodology Proposed methodology Horizon detection Fault detection Results Conclusions and

perspectives

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Alwyn field Field provided by TOTAL UK. It is located east of

the Shetland islands. The structural style corresponds to tilted and

eroded fault blocks dipping to the west and aligned in a north-south direction corresponding to the main normal fault trend.

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The seismic block

Acquired in 1996. Lateral resolution : 25m x 25m. Vertical resolution : around 15 meters 1163 inlines, 849 crosslines and 1001

timelines (about 4 gigabytes) Study operated only on the most

interesting part of the block (only last 351 timelines) : about 1,4 Gb.

6 wells with around 4 markers each in central part.

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Comparison with validated results.

Our results are compared with the ones that students from ENSMP obtained during theirs training sessions on geophysics. Professors, who well know this block, validated the interpretations.

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Execution time (horizons)

Threshold Reflector number

Merging time (sec)

Horizon number Cumulated time(min)

25000 6 1,35 5 4

10000 85 372 54 19

9000 137 557 90 30

8000 209 1860 120 60

Computer characteristics: VMWare Player 2.5 1,5 Gb RAM 1 CPU Core 2 Duo 1,5 GHz. (only 1 used core cause

of the virtual machine) OS : Debian stable Compiler : g++ 4.1.2.

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A lot more surfaces : global vision of the structure.

Most of surfaces are well identified. One wrong surface cause of a lack of amplitude

at a discordance zone (onlap on erosional surface)

Comparison with manual pickings

ENSPM Pickings Our results

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Execution time (Faults)

ThresholdTime to extract and

characterize (sec)

Fault mirror number

Time to merge fault

mirrors (sec)

Fault number

25000 8,5 40 0,5 0

10000 127 1191 475 44

9000 161,5 1976 1214 101

8000 223,7 4215 5356 287

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Comparison with manual pickings North-South main faults detected. Some parts of the transverse faults

detected.

ENSPM Pickings Our results

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Application on synthetic block Obtained from an artificial model by

extension and compression of various sand layers.

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Store the interpretation Directly into the ontology as individuals/instances of

the concepts. Not only the data itself is stored but also the reasons (by means of (visual attributes and relationships) that led to this interpretation.

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Agenda

Introduction Common methodology Proposed methodology Horizon detection Fault detection Results Conclusions and

perspectives

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Conclusions (1/2) New seismic interpretation platform development :

knowledge based using visual attributes and relationships of geological objects.

Using an optimized data structure (sparse matrix) using less memory and improving computing time.

2 use cases : Horizon interpretation Fault interpretation

Validation of the methodology on a real seismic block.

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Conclusions (2/2) Generic platform able to detect other geological

objects by defining: New concepts associated to visual attributes

and relationships into the SDVP ontology. New specific algorithms to extract candidates to

characterize.

Thanks to the SDVP ontology, we can store the reasons one candidate has been interpreted as a particular geological object.

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Perspectives (1/2)

To introduce new concepts into the SDVP ontology Horizon boundaries for fault detection. Use of stratigraphic columns for fault detection. Chanel characterization.

V2 (more optimized data structures and a GUI)

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Perspectives (2/2)

Better processing of the output in order to be better accepted by next softwares of the earth modeling workflow (smoothing, hole filling, extrapolation…) .

Store the SDVP ontology data into an ontology database (Mastella, 2009) for sharing on the semantic web and optimizing data management.

Management of the succession of the various steps of the interpretation using a workflow model (Belaid, 2009)

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Publications

70th EAGE Conference & Exhibition An Approach of Seismic Interpretation Based on Cognitive

Vision, P. Verney, M. Perrin, M. Thonnat, JF. Rainaud, B024, Juin 2008

An approach of seismic interpretation based on cognitive vision, P. Verney, M. Perrin, M. Thonnat, JF. Rainaud, Student poster, Juin 2008.

SEG Las Vegas 2008 Annual Meeting A knowledge-based approach of seismic interpretation :

horizon and dip-fault detection by means of cognitive vision, P. Verney, JF. Rainaud, M. Perrin, M. Thonnat, pp 874-875, Novembre 2008.

Patent PROCEDE POUR CONSTRUIRE UN MODELE STRUCTURAL AU

MOYEN DE TECHNIQUES DE VISION COGNITIVE, P. Verney, JF. Rainaud, M. Perrin, M. Thonnat, numéro 08/03.178, 9 Juin 2008.

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Références (1/2) [Barnes, 2006] Barnes A. E., A filter to improve seismic discontinuity

data for fault interpretation, Geophysics, vol. 71, issue 3, 2006, p. P1

[Chopra, 2005] Chopra S., Marfurt K. J., Seismic attributes — A historical perspective, Geophysics, Vol. 70, No. 5, Septembre-Octobre 2005, p. 3SO–28SO

[Fensel, 2003] Fensel D., Hendeler J., Lieberman H., Wahlster W., Spinning the semantic web. The MIT Press, Cambridge, Massachusetts, 2003

[Ganter, 1999] Ganter B., Wille R., Formal Concept Analysis, Springer, Berlin, 1999.

[Hudelot, 2005] Hudelot C., Towards a Cognitive Vision Platform for Semantic Image Interpretation; Application to the Recognition of Biological Organisms, Thèse d'informatique. Université de Nice – Sophia Antipolis, France, 2005

[Marr, 1982] Marr. D., Vision, W.H. Freeman and Company, New York , 1982

[Meldhal, 2000] Meldhal P., Heggland R., De Groot P., Bril B., Semi-automated detection of seismic objects by directive attributes and neutral networks, method and applications, EAGE 62nd Conference and Technical Exhibition, A-04, Glasgow, Scotland, 2000

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Références (2/2) [Monnin, 2009] Essai de comparaison des ontologies informatiques

et philosophiques. Entre être et artefacts. XVIèmes Rencontres interdisciplinaires sur les systèmes complexes naturels et artificiels de Rochebrune, Ontologie et dynamique des systèmes complexes, perspectives interdisciplinaires., Megève, France, 2009.

[Perrin, 1998] Perrin M., Geological consistency : an opportunity for safe surface assembly and quick model exploration, 3D Modeling of Natural Objects, A Challenge for the 2000’s, 3(4-5), Juin 1998.

[Randen, 2001] Randen T., Pedersen S. I., Sonneland L. Automatic Extraction of Fault Surfaces from Three-Dimensional Seismic Data, Annual International Meeting, Society Exploration Geophycist, Expanded abstract, 2001.

[Smith, 2004] Smith M. K., Welty C., McGuinness D. L., OWL Web Ontology Language Guide, ed. W3C Recommendation, 10 February 2004, available at http://www.w3.org/TR/owl-guide/ accessed July 15th, 2009.

[Staab, 2004] Staab S., Studer R., ed. Handbook on Ontologies. Springer, Berlin, 2004.

[Thonnat, 2002] Thonnat M., Knowledge-based techniques for image processing and for image understanding, J. Phys. IV France EDP Science, Les Ulis, 12:189{236, 2002.

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