The main object of this study is to detect faults and fractures patterns to reveal physical features and structural geometry of the Kaboudia dolomitic Aptian reservoir. To accurately map faulted areas near top reservoir, many attributes derived from the pre-conditioned seismic data have been proposed as fault identifiers such as dip, curvature, coherency and similarity then a supervised neural network was constructed to select and combine the most sensitive fault attributes and trained at identified fault and non-fault locations to generate a fault probability cube. The outcome of this study is an enhanced subsurface display of the geology in order to identify reservoir properties and geometries and assess it significance for hydrocarbon exploration.

1. win-win partnership
STRUCTURAL INTERPRETATION AND FAULTS MAPPING
USING SEISMIC ATTRIBUTES AND NEURAL NETWORK OF
A 3D POST-STACK SEISMIC SURVEY IN KABOUDIA PERMIT
General setting Fault Enhancement Workflow
Applying The
Filters & QC
The Kaboudia permit lies in eastern offshore Tunisia and covers an area of about 3104 km².
It is 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.
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. The compressive cycle is recognized by four major tectonic unconformities related
to Early Albian-Austrian phase (top Serj unconformity), to Late Cretaceous-Eocene
Pyrenean phase (top Souar-base Fortuna unconformity), to the Middle-Late Miocene
Atlassic phase (top Fortuna-base Ain Grab unconformity) and to the Early Quaternary phase
(base Pliocene-base RafRaf unconformity). These structural activities were associated with
significant movements of pre-existing faults and folds leading to the generation of many
local unconformities, structural inversion, erosion and lateral variation of facies of different
stratigraphic series.
We set out to develop a seismic-fault detection method which recombines multiple
attributes into a new attribute that gives the optimal view of the targeted object .The seismic
volume has been subject to several stages of pre-conditionning to enhance discontinuities
and calculate similarity and curvature attributes These final attributes show detailed
geometry of the major fault system and numerous subtle lineaments in the study area.
METHODOLOGY:
Seismic attributes can provide essential details
from seismic data, but noise present in the data-
set often distort the outcome. To optimally
remove noise, data conditioning was carried out
in two steps, in the first step, a dip volume was
created using Fourier transform based algorithm
in inline and cross-line directions. This dip
volume was then used as a reference, and a
median filter was applied on the seismic data
guided by prepared dip volume. It reduces the
structurally oriented random and coherent noise
and increases the continuity and visibility of
faults and fractures by preserving and at the
same time sharpening the edges.
Definitions:
Similarity is a coherence attribute that expresses
how similar two or more traces are to each other
either in the crossline or inline directions Trace
discontinuities of the seismic data may be the
result of the faults or stratigraphic features.
In the other hand, curvature attributes are being
increasingly used to characterize faults and
fractures, based on the fact that certain areas with
curved surfaces are related to discontinuity
zones, which can be represented by faults and
fractures. The curvature attribute emphasizes the
positive (antiforms) and negative (sinforms)
curvatures, whereas on a flat surface or in
inflection zones the curvature is zero.
Inline 1249: Fault Enhanced Volume
Small Scaled Fault
Interpretation
Inline 1249: Residual= Initial Seismic Volume- Fault Enhanced Volume
Noise spikes and bands of noise around low amplitude levels are removed
Noise is localized mainly around Major faults
 The enhanced Seismic will help to better image weaker faults and enhance their Continuity
Inline 1249: Initial Seismic Volume
Fault Probability Cube
Seismic
volume
Calculate
Dip Volume
Fault Enhanced
Volume
TWT SURFACES
Curvature
Mapping Seismic
Geomorphology
TVSW
Spectral Bluing
Seismic Attribtes Grids
Detailed Small scale faults
Interpretation
Similarity
Seismic Attribute
Grids
METHODOLOGY:
In order to improve imaging of small scaled faults
two techniques that shape the seismic spectrum
to optimize the vertical resolution were used. The
first is TIME VARIANT SPECTRAL WHITENING
(TVSW). This method involves passing the input
data through a number of narrow band-pass
filters and determining the decay rates for each
frequency band. The inverse of these decay
functions for each frequency band is applied and
the results are summed. The second is SEISMIC
SPECTRAL BLUEING where an operator was
designed by matching the spectrum of seismic
data with well data and then convolved with input
data to create enhanced volume.
3D View of Similarity Cube, Full Stack Seismic and faults Interpretation
Serdj Horizon: Similarity attribute co-rendered with crossline
Direction Energy gradient to better Image fault Continuity
Serdj Horizon: Maximum curvature attribute co-rendred with Energy
 it shows minor E-W Faults
E-W Minor Faults
Fault Probability Cube
computed Using Neural Network
Extracted On Ain Ghrab Horizon
Fault Probability Cube
computed Using Neural Network
Extracted On Serdj Horizon
Blue: Intial Volume
Red : Fault Enhanced
Green: TVSW
Cyan: SSB
Inline 1252: initial Seismic Volume With Fault interpretation (black) Inline 1252: TVSW Seismic Volume With Minor Fault interpretation (white)
the high-frequency noise is usually amplified (Using TVSW OR SSB)
and so a band-pass filter must be applied to the resulting data
Since conventional seismic data is band limited, it provides limited subsurface geological information. Moreover, higher frequencies within the
band are more attenuated. In order to improve the characterization of faults In this study, we used two different filters: the dip-steered median
filter to remove random noise and increase the lateral continuity of reflections, and the fault-enhancement filter used to enhance the
discontinuities of the reflections. After filtering, similarity and curvature attributes were applied in order to identify the distribution of faults along
the data.