Borehole image logs can be used to characterize heterogeneity in carbonate reservoirs. They provide high resolution data on structural features like fractures and faults, depositional features, and reservoir rock types. This allows for identification of permeability barriers and flow paths. Image logs can classify porosity types and estimate permeability in cored and uncored wells. When integrated with core data, they improve reservoir modeling and predictions of fluid flow.

1. Carbonate heterolithic classification/1
Image log & dipmeter analysis course
Characterisation of carbonate reservoir
heterogeneity using borehole image logs

2. Carbonate heterolithic classification/2
1 km
100 m
200 microns
Scales of carbonate reservoir heterogeneity

3. Carbonate heterolithic classification/3
• Borehole imaging tools
• Heterogeneities
– Structural – fractures/faults
– Depositional and diagenetic
– Pore system evaluation
• Reservoir rock typing
• Conclusions
Outline

4. Carbonate heterolithic classification/4
channels
reefs
slide blocks
0.1 mm 100 m 1 km 10 km
1 m 10 m
1 cm 10 cm
1 mm
CORE
(Limited Coverage)
BOREHOLE IMAGES
3D SEISMIC
(Limited Resolution)
fracture width
ripples
dish structures/dewatering
bioclasts
soft sediment deformation
scours/erosion surfaces
grain size/bed thickness trends
bedforms
channel lags
slumps
Scales of investigation

5. Carbonate heterolithic classification/5
• Fractures
– Closed or open
– Natural or drilling induced
– Orientation, spacing and frequency
• Faults
– Determine strike and dip of the fault
– Determine rock displacement along the
fault
– Reservoir compartmentalisation
• In-situ stresses
– Borehole breakout
– Drilling induced fractures
– Potential & artificial fracturing
• Input to fracture modeling
Static Dynamic
36 72
N
Structural heterogeneities

6. Carbonate heterolithic classification/6
Micro-resistivity Acoustic
Fracture
Bedding
Feature classification and characterisation

7. Carbonate heterolithic classification/7
Micro-resistivity images in horizontal well displayed as pseudo-core sticks
Feature classification

8. Carbonate heterolithic classification/8
CBILsm ACOUSTIC EARTH Imagersm
GR HEXDIPsm
5m
Fracture identification in oil based mud systems

9. Carbonate heterolithic classification/9
• For resistivity images:
– Conductive (dark image) =/= Open?
– Resistive (light) =/= Closed?
• For Acoustic Images
– low amplitude (dark) =/= Open?
– high amplitude (light) =/= Closed?
• Core calibration can be used to confirm
– Image logs can only provide an interpretive
insight only
• Only dynamic data provide true insight into
fracture “producibility”
Open versus closed fractures

10. Carbonate heterolithic classification/10
Flowmeter data
Conductive
(open) fractures
Acoustic
Calibration of image log data with core

11. Carbonate heterolithic classification/11
Fault identification

12. Carbonate heterolithic classification/12
BIU 1
BIU 3A
BIU 2
BIU 3B
Formation
Test
Data
(psi)
110.3
26.2
26.2
25.5
10.4
11.8
9.5
12.8
8.9
8.6
7.9
BIU 3B BIU 2 BIU 1
Bedding contact
WSW ENE
MINOR
FAULT B
MINOR
FAULT A
MAJOR
FAULT A
BIU 3A
Fault compartmentalisation

13. Carbonate heterolithic classification/13
• Well and fracture orientation
• Fracture density
• Core density calibration
• Borehole corrected
fracture density
• Fracture spacing
• Fracture length?
• 3D modelling
Schematic path
dow
n boreho
le
(69* / 210*)
Schematic path
down bo
reho
le
(69* / 210*)
Mesozoic carbonate
Oil staining
OBLIQUE-SLIP
Fracture distribution

14. Carbonate heterolithic classification/14
• Increased compartmentalisation
– Permeability barriers
• Increased communication
– Permeability conduits
• Overall objective - producibility
– Fracture model inputs
– Fault seal predictions
– Production enhancement
– Completion strategy
– Selection of perforation intervals
GO
Fracture characterisation

15. Carbonate heterolithic classification/15
• Image facies assigned on basis of image
character, open hole log response
• Image response is related to electrical or
acoustic properties, responds to rock
texture, fluid saturation and mineralogy
• Not possible to distinguish all core
lithofacies
• Calibration with core allows a meaningful
geological interpretation of image logs
Image facies

16. Carbonate heterolithic classification/16
Stylolitic seams
Conductive,
tension gashes
Resistive
cemented
limestone
Dissolution
seam
Stylolites and dissolution seams

17. Carbonate heterolithic classification/17
Cross-bedding

18. Carbonate heterolithic classification/18
STATIC DYNAMC STATIC DYNAMC
STATIC DYNAMC
STATIC DYNAMC
STATIC DYNAMC
STATIC DYNAMC
Nodular limestone image facies

19. Carbonate heterolithic classification/19
0
10
20
30
40
50
60
%
B-Zone
Image facies
0
10
20
30
40
50
60
%
B-Zone
Image facies
0
10
20
30
40
50
60
%
B-Zone
Image facies
0
2
4
6
8
Average
thickness
(ft)
Image facies
0
2
4
6
8
Average
thickness
(ft)
Image facies
0
2
4
6
8
Average
thickness
(ft)
Image facies
0
2
4
6
8
Average
thickness
(ft)
Image facies
Well 4 - uncored
Well 3
Well 2
0
10
20
30
40
50
60
%
B-Zone
Image facies
Well 1
IF1 IF2 IF3 IF4 IF5 IF6 IF7 IF8 IF9 IF10 IF11
Image facies
statistics

20. Carbonate heterolithic classification/20
•Identification, orientation and
analysis of primary stratification
surfaces/heterogeneities
•Identification of key stratal
surfaces
•Borehole image facies analysis
•Depositional modelling
•Sequential analysis
•Pore fabric analysis
•porosity and permeability
distribution
•Palaeotransport/reservoir
geometry analysis
Depositional heterogeneities

21. Carbonate heterolithic classification/21
Thin bed identification
below the resolution of
openhole logs
STAT DYN
MINIPERM
PROFILE
High resolution image log response

22. Carbonate heterolithic classification/22
Resistivity Image Resistivity Image
Acoustic Image Acoustic Image
Resistivity Image Resistivity Image
Acoustic Image Acoustic Image
Reef core Back-reef lagoon
Image log facies analysis – reef facies

23. Carbonate heterolithic classification/23
Oil based mud
Facies stacking patterns and dip analysis

24. Carbonate heterolithic classification/24
Image facies show
an overall
coarsening/ shoaling-
upward trend with
increasing stacking
pattern in upper part
of reservoir
Stacked
fining-upward
grain/pack/
wackestone
units
Mostly trendless
succession of
mudstone/
wackestones
Interpretation
Coarse, vuggy rudist
float to rudstones
Image facies prediction in uncored wells

25. Carbonate heterolithic classification/25
Reservoir architecture

26. Carbonate heterolithic classification/26
Unit A
Unit B
Breccia
Image facies
Lateral facies variations in horizontal wells

27. Carbonate heterolithic classification/27
Porosity classification

28. Carbonate heterolithic classification/28
Porosity classification
Fenestral
Shelter
Growth
framework
Interparticle
Intraparticle
Intercrystalline
Mouldic
Fracture
Channel
Vug
Cavern*
Breccia
Boring
Burrow
Shrinkage
Fabric-selective Non-fabric-selective Fabric-selective or not
* Cavern applies to human sized or larger pores of channel and vug shapes

29. Carbonate heterolithic classification/29
Pore fabric analysis

30. Carbonate heterolithic classification/30
15
20
Ft
Elongate conductive patches
represent secondary mouldic
porosity after bioclasts filled
by conductive drilling mud.
Conductive
(open?) fracture
Micro-resistivity
Mouldic and vuggy porosity

31. Carbonate heterolithic classification/31
Vuggy carbonates seen in both acoustic and resistivity images
Image analysis can be used to threshold the image
and determine the % vuggy macroporosity and
degree of interconnectivity
Acoustic Micro-resistivity
Vuggy porosity

32. Carbonate heterolithic classification/32
_ 3.41
_ 1.64
_ 2.35
_ 0.21
_ 0.75
_ 2.81
_ 1.81
Log K
Permeability map
Thin section
Core
Microporous nodular limestones

33. Carbonate heterolithic classification/33
STATIC
Cemented
tight
limestone
Microporous
high
permeability
limestone
Porosity distribution from images

34. Carbonate heterolithic classification/34
A RRT has a unique reservoir quality but not
necessarily lithofacies.
• Typically established in cored wells on basis of thin
section and SCAL data.
• Extrapolation into uncored wells using openhole logs
and used to predict permeability.
– fine scale permeability heterogeneity below resolution of
standard openhole logs.
– subtle changes in pore system can result in similar
porosities but permeability can vary by several orders of
magnitude.
• High resolution image logs used for Image Rock
Type (IRT) identification and permeability prediction.
Reservoir rock typing

35. Carbonate heterolithic classification/35
Conductive, high K vuggy
skeletal grainstones
at base of units
Resistive, low K
wackestones at top of
units
Stacked small-
scale fining-
upward units,
with high K
grainstones at
base grading up
into low K
wackestones
Image facies

36. Carbonate heterolithic classification/36
Calculate K statistics and
define K rank
Organise core poro-perm data by image
facies (cross-plots)
Group image facies into K classes:
“image rock types”
Assign image image rock types using porosity
from openhole log data as guide to ‘background’ rock type.
Assign K rank and value along-
side image rock types interpretation
QC predicted K against
measured core K
RRT/ permeability prediction workflow

37. Carbonate heterolithic classification/37
0.01
0.1
1
10
100
1000
10000
0 5 10 15 20 25 30 35 40
Helium porosity (%)
Horizontal
permeability
(mD)
Poroperm coded
by image facies
Poroperm calibration
Image facies grouped
into image rock type
0.01
0.1
1
10
100
1000
10000
0 1 2 3 4 5 6 7 8 9
Image Rock Type
Horizontal
permeability
(mD)

38. Carbonate heterolithic classification/38
Results from blind
test showing
predicted
permeability against
permeability from
core analysis data
RRT and permeability prediction

39. Carbonate heterolithic classification/39
Permeability prediction - dipmeter data

40. Carbonate heterolithic classification/40
RRT/permeability variations

41. Carbonate heterolithic classification/41
IRT
IRT
RRT & permeability prediction

42. Carbonate heterolithic classification/42
Conclusions
• Borehole image logs provide high resolution, oriented
data sets
– Provide key information on nature, orientation and scale of
fracture and fault systems, and compartmentalisation
– Provide information on texture, lithofacies and reservoir rock
types.
– Porosity and permeability distribution
– Identification of small-scale heterogeneity below resolution of
standard openhole logs
• Make more effective use of uncored wells to build,
constrain & validate 3D reservoir model realisations