Download - Geological Fracture detection -Geomage
MultiFocusing Diffraction Imaging (MFDI)
• Introduction and theory
• Numerical model
• Case studies– Case 1 – Mediterranean – Case 2 – integrated study 1– Case 3 – integrated study 2
Outline
Theory
Conventional NMO
Before NMO correction After NMO correction
offset offset
tim
e
V11
V22
X – source R – receiver
CRE radius & CEE radius and emergence angle
2D MultiFocusing – 3 parameters
wavefront
obstacle
Diffraction - definition
• strong need for small-scale natural fracture and fault detection
• seismic wavefront contains certain fracture information, difficult to extract
• diffraction, key to higher resolution
Why diffraction?
• MF imaging stacks large number of traces and increases S/N ratio
• weak seismic events are enhanced• diffraction energy contains important information
but is weak and sensitive to noise• MF diffraction imaging methodology increases
diffraction S/N ratio
MultiFocusing diffraction imaging (MFDI)
Diffraction moveout coincides with MultiFocusing moveout when the reflection interface shrinks to a point, i.e., when RCRE = RCEE.
Diffraction stacking – a special case of MultiFocusing stacking
• Introduction and theory• Numerical model• Case studies
– Case 1 – Mediterranean – Case 2 – integrated study 1– Case 3 – integrated study 2
Outline
fractures
20 km
Numerical diffraction model
Size of fracture: 1 x 0.3 meter
Fracture density increases along model length
fractures
dep
th (
m)
distance (m)
2D-model MultiFocusing stack
2D-model MultiFocusing post-stack migration
2D-model MultiFocusing diffraction stack
2D-model MultiFocusing diffraction post-stack migration
• Introduction and theory• Numerical model• Case studies
– Case 1 – Mediterranean – Case 2 – integrated study 1– Case 3 – integrated study 2
Outline
Case study 1: geology
Geomage MultiFocusing – structure stack
Geomage MultiFocusing – diffraction stack
MultiFocusing – migrated diffraction stack
diffraction values in color on migrated MF stack
evaporites
Geomage MultiFocusing – diffraction stack
MultiFocusing diffraction velocities
smoothed velocity from MF processing
corrected velocity from MF diffractions
m/s
• Introduction and theory• Numerical model• Case studies
– Case 1 – Mediterranean – Case 2 – integrated study 1– Case 3 – integrated study 2
Outline
Integrated diffraction case study 1
Goal – predict fractured zones within unconventional reservoir onshore Europe
well data:– log data of six wells with old
and poor logs – incomplete well tests– key information on two wells
kept back as blind test
formation: oil-shaleaverage thickness: 40 m
Integrated diffraction case study 1
formation: oil-shaleaverage thickness: 40 m
seismic data– diffraction– amplitudes– attributes
– seismic structural interpretation, seismic attribute analysis
– MultiFocusing diffraction imaging– petrophysical analysis and well log
interpretation – clusters and statistical analysis– geological review and conclusion
Project scope
Seismic PSTM amplitudes
Well A Well B Well C
time map depth map
?
?
?
?
Horizon maps
70
75
80
85
90
95
100
105
110
115
120
125
130
135
139
3
Well A
Well B
Well C
Temperature map at reservoir
tem
pe
ratu
re (
de
gre
es
C)
130
100
70
180
200
220
240
260
280
300
320
340
360
380
400
420
440
460
3
Well A
Well B
Well C
Pressure map at reservoir
460
340
180
pre
ssu
re (
PS
I)
Well-to-seismic ties
Max.Corr = 0.79Max.Corr = 0.82
reservoir
reservoir
PSTM in background, diffraction image in color
Well A
increasing evidence of fracturing and facies change in areas of uplift and compression
Well B
PSTM in background, diffraction image in color
increasing evidence of fracturing and facies change in areas of uplift and compression
Petrophysical results and diffraction trace along well paths
US-14
US-6
US-7
Q 2.1н=1м/сут3
Q 16н=м/сут3
Сухо
Кровля абалак.свиты
Кровля баж.свиты
Parametr of fractures and additional permeability
Trace of Diffraction imageК _p nk
К _p nk
Kp_nk
VSH
VSH
VSH
Trace of Diffraction image
Trace of Diffraction image
Parametr of fractures and additional permeabilityParametr of fractures
and additional permeability
Lithology index
Lithology index
Lithology index
Truth Formation resistivity
Resistivity from tool with big and short
radius of investigation
Resistivity from tool with big and short
radius of investigationResistivity from tool with big and short
radius of investigation
Flush zone resistivity
Flush zone resistivity
Truth Formation resistivity
Truth Formation resistivity
Flush zone resistivity
GR
NGK60GR
NGK60
GR
NGK60
APS
APS
APS
PZ
BK
PZ
BKPZ
BK
Diffraction amplitudes and well results
diffraction image horizon map production rate vs diffraction amplitude
B
C
A
diffraction amplitude
pro
du
ctio
n r
ate
correlation coefficient = 0.7
in situ porosity vs diffraction amplitude
y = 32.477ln(x) + 30.932R² = 0.8434
y = 33.229ln(x) + 26.969R² = 0.8827
y = 22.734ln(x) + 21.653R² = 0.8434
0
10
20
30
40
50
60
70
80
0 1 2 3 4
Lin
ear
volu
me
Diffraction image, у.е
Qsr
Q2
Q3
B
A
C
B
C
A
diffraction image horizon map
in s
itu
po
rosi
ty (
%)
diffraction amplitude
average correlation coefficient: 0.85
Diffraction amplitudes and well results
Attribute horizon maps around various well locations
composite map: first derivative of envelope, diffraction amplitude, temperature, pressure
B
A
C
B
C
A
calculated in situ porosity map
B
C
A
• Introduction and theory• Numerical model• Case studies
– Case 1 – Mediterranean – Case 2 – integrated study 1– Case 3 – integrated study 2
Outline
Integrated diffraction case study 2
Goal – predict fractured zones within unconventional reservoir, onshore Americas
well data:– six wells were within the
survey limits – only five could be used– old wells with
incomplete information
formation: oil shaleaverage thickness: 11 m
100041300222W400
MD
,м
Resistivity andD of flush zone
5 10
Dzona2
0 2000
RES_DEP
0 2000
RES_MED
0 2000
RES_SLW
Properties
2300 2900
DEN
150 300
DT
0 500
GR
2300 2900
den_correct
Porosities
0 0.2
KPnk_gk
Paramether of fractures
0 0.001
KPfrac2
0 0.001
KPfrac1
Paramether of fractures
0 0.014
DIFR2DDIV100
0 0.001
KPfrac4
0 0.0001
KPfrac6
-8000 8000
SEISMIC
Paramether of fractures
0 0.001
KPfrac9
0 0.001
KPfrac11
0 0.001
KPfrac10
0 10
n
1 5
LIT
7000 21000
imp
0 10
PE
0 500
GR
2300 2900
DEN
2480
2500
2520
2540
2560
2580
2600
2620
2640
D flash zone
seismic data– amplitudes– attributes– diffraction amplitudes– diffraction velocities
formation: oil shaleaverage thickness: 11 m
Integrated diffraction case study 2
Goal – predict fractured zones within unconventional reservoir, onshore Americas
– diffraction imaging– seismic attribute analysis– petrophysical analysis and well log
interpretation– clusters and statistical analysis– geological review and conclusion
Project scope
Seismic arbitrary PSTM amplitude section
Well A Well B Well C
Time map at main target interval
Seismic-to-well tieWell D
PSTM amplitudes around target horizon
main horizon minus 30 ms
main horizon minus 12 ms
B
C
A
F
C
A
F
B
100052700124W400
MD
,м
Resistivity and D of flush zone
5 16
Dzona2
0 1950
RES_DEP
0 1950
RES_MED
Porosity, Litology and clayness
0 1
VSH
0 0.1
KPnk_gk
Paramether of fractures
0 1E-5
KPfrac
0 0.01
DIFR
Traces of seismic attributes, Impedance, Vrelationship (logs)
-9000 9000
SEISMIC
10000 25000
imp
0.9 1
dV
0 1
dVs_p
2688
2700
2712
2724
2736
2748
2760
2772
2784
D flash zone
Petrophysical analysis
calculated fracturing
de
pth
(m
)
Cross-plot, calculated in situ porosity and diffraction
in s
itu
po
rosi
ty (
%)
diffraction amplitude
correlation coefficient: 0.9
Diffraction interpretation
diffraction amplitude
main horizon -12 ms secondary target
Integrating digital elevation model (DEM)anomaly not caused by surface condition
DEM
diffraction amplitude
anomaly possibly caused by surface condition.
Integrating digital elevation model (DEM)
DEM
diffraction amplitude
Summary
• three case studies
• petrophysical analysis – natural fracturing, in situ porosity
• MF diffraction amplitude - calibrate to wells
• calculate seismic attributes
• all data types integrated
Conclusions
• correlation between diffraction amplitudes and natural fractures
• prediction of fracture swarms in tight shales
• integrating other data types enhances fracture-prediction accuracy