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Processing Report : Top Mute, Team 1
School Of. Earth & Environment
University Of Leeds
TOP MUTE
TEAM (1)
Processing Report
(EARS 5165)
Author
Position
Chinedu Amadi
Msc. Structure Geology student
Gboyega Ayeni
Msc. Hydrocarbon Geophyscis student
Kirsteb Macbeath
Dan Sopher
Mai Afifi
17 February, 2016
Signed
Date
2006-05-08
2006-05-08
Msc. Hydrocarbon Geophyscis student
Msc. Hydrocarbon Geophyscis student
2006-05-08
2006-05-08
Msc. Hydrocarbon Geophyscis student
2006-05-08
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Processing Report : Top Mute, Team 1
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TABLE OF CONTENTS
1
EXECUTIVE SUMMARY
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2
INTRODUCTION
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2.1
GEOLOGICAL SETTING AND MOTIVATION
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2.2
OVERVIEW OF ACQUISITION AND DATA CATALOGUE
5
3
6
3.1
PRE-PROCESSING
Bandpass filtering
7
4
MAIN PROCESSING
10
5
POST-STACK PROCESSING
11
6
GEOLOGICAL INTERPRETATION
12
7
REFERENCES
12
8
APPENDICES
12
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Deconvolution: (single – tarace filtering)
1
Theortical Behind:
Offset (Stack)
Mid point (Migration)
Time (Deconvolution)
Let’s start by the convolution model:
Where:
x is the input time function
h is the time domain representation of the filter
f is the output, and
* is used to indicate convolution - a combination of multiplication and addition.
The need for some form of deconvolution early in pre-processing, to try to collapse the wavelet
back to as short as possible – then to produce the recorded trace will show as close to the
reflectivity series as possible.
Different algorthmes to apply deconvloution, the one we used is “PREDICTIVE
DECONVOLUTION”
Predictive Deconvlution based on the least squares definition:

f
 (h  g * f
t
t
t
)2  0,
i=0,1,2,…..,n
i
Eqn(3.1.5.)
Eqn(3.1.5.2)
Fig.(3.1.5.1), shows the formulation of the matrix equation for predictive deconvolution, how the
necessary paramters are chosen from an ACF, and a deconvolution test planel, to summarize
this important process:
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Three parameters have to be chosen:
1- Gap (G)
2- Operator Length (L)
3- Pre-Whitening ratio (%)
f
Fig, 3.1.5.1, ACF for FFID 1100, one channel
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o
Gap determines which part of the ACF will be untouched by the deconvolution – the part
from lag = 0 to lag=G, G msut be < or equal to Land be large enough so that the L points after
lag = G includes all that is to be suppressed in the ACF.
o
L determines how many points are in the filter and what extent of the ACF, from the lag =
G+1 to G+L, will be zeroed by the deconvolution
o
The (%) prewhiting is a samll adjustment to ACF at lag =0 which effectivly ensure
numerical stability.
We applied armeter test for the fifferent Gap values:
Fig, 3.1.5.3, Paramter test for different Gap values
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For the Gap value as shown on fig(3.3.5.3), the best value is G=10 at second zero
crossing
For the Gap value as shown on fig(3.3.5.4), there is no much great change between
different operator lengths, according to theory L >or equal G, we test for difference
operator length as in fig(3.3.5.4),
Fig, 3.1.5.4, Paramter test for different operator length
The final results “Before” and “After”
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Fig, 3.1.5.5, Section Before Deconvolution
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3.3.2. Long path multiple suppression stratigies:( F-K multiplies):
Theory behind:
o Do semblance analysis as usual but pick a stacking velocity function between primary
multiple semblance peaks
o Apply these “intermediate” velocities as NMO correction
o Primaries will be overcorrected and curve upward, multiples undercorrected and remain
curved downward
o Transform NMO – corrected gather gathetr into FK space: primaries will now fall in the
negative wavenumber segment, multiples in the positive segment (with overlap on/around the
k=0 axis because primaries and multiple both have minimal moveout at nearest offset
o Reject (+ve) k half, keeping energy along/near k=0: do inverse transform. FK filtered NMO
corrected gather now has only overcorrected primaries
o Back off “intermediate velocity” NMO correction and apply correct primary NMO: the NMOcorrected gather is now primaries only
o Some near-offset residual multiple energy will remain which ca be suppressed post – stack
Results:
F-K de-multiples suppersses the long path multiples
fig(3.3.2), F-k de-multiple test, shows over correct pramiry event
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Ampitude recovery application:
Theory behind:
All of the energy initially contained in the shot is spread out over a larger and larger area as
time passes. This causes one of the possible losses of energy on a field record, and is
generally referred to as spherical divergence.
Another cause of energy loss is known as inelastic attenuation. This is simply the energy lost
due to the particles of earth through which the wave travels not being perfectly elastic - some
of the energy is absorbed and permanently alters the position of the particles.
Other more complex forms of energy loss (some of which are frequency dependent) include
that caused by the friction of particles moving against each other, and losses at each interface
through which the wave travels and is refracted. (Some of the energy in the original seismic Pwave is converted into an S-wave at each interface and not recorded - more on this later!)
In all cases this generates a total loss of signal that decreases with time, which approximates
to:
where r is the radius of the wavefront and x is an absorption coefficient
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Practical procuders:
After we get the velocity from the velocity analysis, we applied gain recovery on VELOCITY
BASED SCALING,
The results when we applied it doesn’t give the expected results(it doesn’t show up, as we can
see from fig(
)
Amplitude recovery function doesn’t work as expected
fig.( ), Before and after applying amplitude recovery function, on VELOCITY BASED
SCALING ALGORITHM
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