Characterisation of carbonate reservoir heterogeneity using borehole image logs

Image log & dipmeter analysis courseCharacterisation of carbonate reservoir heterogeneity using borehole image logs... • Core calibration can be used to confirm – Image logs can only p

Image log & dipmeter analysis course

Characterisation of carbonate reservoir

heterogeneity using borehole image logs

• Borehole imaging tools

• Heterogeneities

– Structural – fractures/faults

– Depositional and diagenetic

– Pore system evaluation

• Reservoir rock typing

• Conclusions

Outline

dish structures/dewatering

bioclasts soft sediment deformation scours/erosion surfaces grain size/bed thickness trends bedforms

channel lags

slumpsScales of investigation

• 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

3 6 7 2

N

Structural heterogeneities

CBIL sm ACOUSTIC EARTH Imagersm

GR HEXDIP sm

5m

Fracture identification in oil based mud systems

• 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

Open versus closed fractures

Flowmeter data

Conductive (open) fractures Acoustic

Calibration of image log data with core

Fault identification

BIU 1

BIU 3A BIU 2

FAULT A MAJOR

FAULT A

BIU 3A

Fault compartmentalisation

• Well and fracture orientation

Schematic path down borehole (69* / 210*)

Mesozoic carbonate Oil staining

OBLIQUE-SLIP

Fracture distribution

• Increased compartmentalisation

– Permeability barriers

• Increased communication

– Permeability conduits

• Overall objective - producibility

– Fracture model inputs

– Fault seal predictions

– Production enhancement

– Completion strategy

GO

Fracture characterisation

• 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

Stylolitic seams

Conductive, tension gashes

Resistive cemented limestone

Dissolution seam

Stylolites and dissolution seams

Cross-bedding

STATIC DYNAMC STATIC DYNAMC STATIC DYNAMC

STATIC DYNAMC STATIC DYNAMC

STATIC DYNAMC

Nodular limestone image facies

0 10 20 30 40 50 60

• Identification, orientation and

analysis of primary stratification

•porosity and permeability

distribution

Depositional heterogeneities

Thin bed identification below the resolution of

openhole logs

STAT DYN

MINIPERM PROFILE

High resolution image log response

Resistivity Image Acoustic Image Resistivity Image Acoustic Image

Resistivity Image Acoustic Image Resistivity Image Acoustic Image

Image log facies analysis – reef facies

Oil based mud

Facies stacking patterns and dip analysis

Image facies show

wackestone units

Reservoir architecture

Porosity classification

Porosity classification

Fenestral

Shelter

Growth framework

Pore fabric analysis

Conductive (open?) fracture

Mouldic and vuggy porosity

Vuggy carbonates seen in both acoustic and resistivity images

Image analysis can be used to threshold the imageand determine the % vuggy macroporosity and

degree of interconnectivity

Vuggy porosity

_ 3.41

_ 1.64 _ 2.35

_ 0.21 _ 0.75

Microporous nodular limestones

Cemented tight limestone

Microporous high permeability limestone

Porosity distribution from images

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.

Reservoir rock typing

Conductive, high K vuggy skeletal grainstones

at base of units

Resistive, low K wackestones at top of

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 side image rock types interpretation

along-RRT/ permeability prediction workflow

Image facies grouped

into image rock type

0.1 1 10 100 1000 10000

Results from blind

test showing

predicted

permeability against

permeability from

core analysis data

RRT and permeability prediction

Permeability prediction - dipmeter data

RRT/permeability variations

IRT

RRT & permeability prediction

• 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