KABOUDIA PERMIT – OFFSHORE TUNISIA

Meta-Attributes and Neural network Utility To detect Geological Features and Reservoir Properties of the

Aptian Serdj Dolomitic Reserservoir In Kaboudia Permit

54, Avenue Mohamed V - 1002 Tunis, Tunisie

Tél. : (+216) 71 28 53 00 - Fax : (+216) 71 90 22 82

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OUTLINE

Introduction

Seismic Attributes For Fault Detection

Seismic Attributes For Mapping seismic geomorphology

Conclusion

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Introduction

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Geographical Location

The Kaboudia permit lies in eastern offshore Tunisia and covers an area of about 3104 km2.

bounded to the West by the Monastir-Mahdia shoreline and to the North east by the Halk el Menzel Oil Field

the water depth is generally less than 200 meter.

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The Kaboudia block is located in the Pelagian

platform in the NW-SE “Mahdia-Isis” paleohigh which

separate the Gabes basin from the Hammamet basin.

. It lies in a highly complex structural settings since

Late Triassic to Early Cretaceous period which is

marked by extensive cycle and is relayed by Early

Albian to Early Quaternary compressive cycles which

are mainly related to the subsidence and collision

between African and European plates.

Geological Setting

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•Fault detection

•Predicting Carbonate

Lithofacies

•Mapping seismic

geomorphology

•Well log Data

•Horizon Interpretation

•Seismic Attributes

•PCA

•Neural Network

QC

GENERAL WORKFLOW USED FOR RESERVOIR MAPPING And CHARACTERIZATION

QC

•Seismic Cube

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Seismic Attributes For Fault

Detection

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Inline 1249: Initial Seismic Volume

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Inline 1249: Fault Enhanced Volume

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Inline 1249: Residual= Initial Seismic Volume- Fault Enhanced Volume

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Inline 1249: Residual Energy

Noise spikes and bands of noise are localized mainly around Major faults

The enhanced Seismic will help to better image weaker faults and enhance their Continuity

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Similarity attribute exracted On the Serdj Horizon

Most Negative curvature attribute co-rendredwith Z-Values exracted On the Serdj Horizon

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SIMILARITY(Long-wave)

Most positive curv ature (short wave)

Most negativecurvature (Short wave)

Most Positive Curvature(Long-wave)

Most Negative Curvature (Long-wave)

SIMILARITY(Long wave)

PCA10,029545 -0,00233 -0,00093 -0,00124 -0,00136 0,032908

PCA20,078852 -7,72E-05 0,002204 7,00E-05 4,06E-05 0,089026

PCA30,057931 0,00075 -0,00236 0,000629 0,000766 0,065945

Results of PCA Carried out on the seismic Discontinuity Attributes

Plot of the percent variability explained by each principal component.

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First PCA Extracted On the Serdj Horizon

showing Major E-W Faults patterns

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Second PCA Extracted On the Serdj Horizonshowing the NW-SE Faults patterns

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Fault Probability Cube Extracted On Ain Ghrab HorizonUsing PCA as Input

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Fault Probability Cube Extracted On Serdj HorizonUsing PCA as Input

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Seismic Attributes For Mapping

seismic geomorphology

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Color blended map view of the spectral decomposition

Extracted Near Top Reservoir (Red-15hz, green-30Hz, blue-45hz)

The yellowish colored region is thicker compared the surroundings

This trend is related to the thickness of the Upper Serdj formation

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Waveform segmentation attribute grid of the Serdj Reservoir

It shows trends and facies changes from East to the west

of the Survey

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Waveform segmentation attribute grid of the SerdjReservoir in the Mahdia structure showing

reservoir thickness changes

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SW NE

SW-NE Arbitary Section: Instantaneous Phase

Onlap feature

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SDFFT 15hz SDFFT 30hz SDFFT 45 hz GLCM entropy GLCM homogeniety

Energy

PCA11,49433 1,608712 1,949197 0,218945 1,087503 -0,02814

PCA20,965145 0,951377 1,251847 -0,26147 0,666091 0,045416

PCA3-0,59587 0,1629 -0,53758 1,469537 0,499367 0,012108

Results of PCA Carried out on the seismic Lithologies Attributes

Plot of the percent variability explained by each principal component.

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First PCA Exracted On the Serdj Horizon

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Second PCA Exracted On the Serdj Horizon

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Cross section through wellsP-Impedance

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P-Impedance extracted on the Serdj Horizon Probability volume of Dolomites

extracted on the Serdj Horizon

It Shows NW-SE low Impedance Trend In the

eastern part of the survey (Mahdia Structure

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Predicted Gamma Ray extracted on the Sedj Surface

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Predicted Density extracted on the Sedj Surface

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Predicted Total Porosity extracted on the Sedj Surface

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Final Neural Network Classification Of the Reservoir Upper-Serdj

The Red color shows the extent of the Reservoir where it exhibits the best petrophysical

properties compared the surroundings

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CONCLUSIONS

• To understand the distributions of Serdj dolomitic reservoir and map reservoir patterns and heterogeneities changes across the whole 3D seismic survey area, an unsupervised neural network guided by a pre-stack inversion cubes and relevant combination of seismic attributes is used

• The outputs are volumes and surfaces containing Geological informations which can be used in reservoir characterization workfolws.

54, Avenue Mohamed V - 1002 Tunis, Tunisie

Tél. : (+216) 71 28 53 00 - Fax : (+216) 71 90 22 82

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Thank you for your attention