Primary funding is provided by

The SPE Foundation through member donations

and a contribution from Offshore Europe

The Society is grateful to those companies that allow their

professionals to serve as lecturers

Additional support provided by AIME

Society of Petroleum Engineers

Distinguished Lecturer Program www.spe.org/dl

From 3D pore scale imaging to Reservoir

inputs: Next generation reservoir

characterization & description

Society of Petroleum Engineers

Distinguished Lecturer Program www.spe.org/dl 2

Mark Knackstedt mark.knackstedt@lithicon.com

Oil and Gas

Overview

• Problem Statement

– The challenge of integrating scale

• Digital rock technology

– Introduction: Pore to plug

– Fast turnaround & Sensitivity Studies

– Enhanced Understanding

• Multiscale Reservoir Characterization

– Upscaling and Uncertainties

– Upscaling in a SYSTEMATIC manner (calibrated at each

scale)

3

Problem Statement Size matters in reservoir characterization

Reservoir model

4

The Multi-scale Reservoir Characterisation

Problem

?

Question:

How do we accurately and effectively model fine-scale

heterogeneities & predict petrophysical response & fluid flow

at larger scales (log/geo/reservoir-scale)

5

Properties are measured at a range of scales

Goal: Assign properties measured at fine scale to the commercial scales.

Spatial Resolution

(m) 10-9 10-6 10-5 10-3 10-2

Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir

model

10-1 1 10 102

Physics & data changes 6

6

Multi-scale Reservoir Characterisation

x 103

x 102

x 104

x 104 1D

0D

Challenge: We need to assign properties measured at fine scale

to the commercial scales

2D

Dynamic Model

Static Model

Well Log

Core Plug

250 m x 250 m

x 4 m

60 m x 60 m

x 0.6 m

0.6 m x 0.6 m

x 0.6 m

0.025 m x 0.025 m

x 0.038m

x 100

x 10 000

x 10 000

Dynamic Model

Static Model

Well Log

Core Plug

250 m x 250 m

x 4 m

60 m x 60 m

x 0.6 m

0.6 m x 0.6 m

x 0.6 m

0.025 m x 0.025 m

x 0.038m

x 100

x 10 000

x 10 000

MicroCT PlugMicroCT Plug

x 1 000

2.5 mm x 2.5 mm

x 3.7 mm

2.5 cm x 2.5 cm

x 3.7 cm

0.6 m x 0.6 m

x 0.64 m

50 m x 50 m

x 0.93 m

250 m x 250 m

x 3.7 m

MicroCT PlugMicroCT Plug

x 1 000

0.0025 m x 0.0025 m

x 0.0037 m

0.025 m x 0.025 m

x 0.037 m

0.6 m x 0.6 m

x 0.64 m

50 m x 50 m

x 0.93 m

250 m x 250 m

x 3.7 m

Dynamic Model

Static Model

Well Log

Core Plug

250 m x 250 m

x 4 m

60 m x 60 m

x 0.6 m

0.6 m x 0.6 m

x 0.6 m

0.025 m x 0.025 m

x 0.038m

x 100

x 10 000

x 10 000

Dynamic Model

Static Model

Well Log

Core Plug

250 m x 250 m

x 4 m

60 m x 60 m

x 0.6 m

0.6 m x 0.6 m

x 0.6 m

0.025 m x 0.025 m

x 0.038m

x 100

x 10 000

x 10 000

MicroCT PlugMicroCT Plug

x 1 000

0.0025 m x 0.0025 m

x 0.0037 m

0.025 m x 0.025 m

x 0.037 m

0.6 m x 0.6 m

x 0.64 m

50 m x 50 m

x 0.93 m

250 m x 250 m

x 3.7 m

Pore

3D

3D

+

+

+

3D

3D

3D

7

Overview • Problem Statement

– The challenge of integrating scale

• Digital rock technology: Pore to Plug

– Technology Development and alternate SCAL data

– Enhanced Understanding

8 Spatial

Resolution (m) 10-9 10-6 10-5 10-3 10-2

Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir

model

10-1 1 10 102

Digital Rock Technology

10

1

10

100

1000

10000

0,0 0,1 0,2 0,3

Porosity

[fraction]

Per

mea

bil

ity

[mD

]

Elastic moduli

FRF

Porosity

NMR relaxation times

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Sw

Rel P

erm

krw_exp1kro_exp1krw_exp2kro_exp2krw_exp3

kro_exp3krw simkro simkrw avgkro avg

Rel

ati

ve p

erm

eab

ilit

y

1.0

0.8

0.6

0.4

0.2

0.0 1.0 0.8 0.6 0.4 0.2 0.0

Sw [fraction]

Relative permeability

Capillary pressure

Resistivity index

PETROPHYSICAL FLUID FLOW

DIGITAL ROCK PROPERTIES

Absolute permeability

3D DIGITAL ROCK

Cementation exponent

Acoustic velocities

Saturation exponent

Mercury injection

Sw sensitivity

Wettability analysis

IFT sensitivity

Rate sensitivity

EOR/IOR

Oil in Place

Formation damage

4D IMAGING

Fluid sensitivity

Geochem. Reactivity

Unconventional Play

QUALITATIVE

Wettability mapping

Digital Workflow

11

Workflow – Conventional

Normalised Data vs Pore Throat Size

0.0

0.2

0.4

0.6

0.8

1.0

0.001 0.01 0.1 1 10 100 1000Pore Throat Radius (Microns)

Dis

trib

uti

on

Fu

ncti

on

s

Lab MICP

DRP MICP

0.0

0.2

0.4

0.6

0.8

1.0

0.1 1 10 100 1000 10000

T2 [ms]

No

rma

lis

ed

Am

plitu

de

Sample 6 Exp

Sample 6 DRP

BB 274 88 RT3

-120

-80

-40

0

40

80

120

0.00 0.20 0.40 0.60 0.80 1.00

Water saturation, Sw (frac.)

Cap

illa

ry p

ress

ure

, P

c (p

si) Pc Exp

Hg curvePc SimPc Exp SIPc Exp FIPc Sim

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

0.0 0.2 0.4 0.6 0.8 1.0Sw

Re

lative

pe

rme

ab

ility

DRP - CC.RRT6

Lab - CC.RRT6

HM - Lab - CC.RRT6

1) SCAL: Fast turnaround, Difficult cases

Derive (primarily) kr and Pc data for reservoir modelling with

fast turnaround. Undertake otherwise impossible SCAL:

Cuttings, Sidewall, damaged core.

2) Enhanced Understanding

Fluid distributions, EOR/IOR recovery mechanisms, wettability,

heterogeneity, uncertainties

3) Upscaling and Uncertainties

Upscaling in a SYSTEMATIC manner (calibrating at each scale).

Analyse heterogeneous/complex rocks (e.g, thin beds,

carbonates)

Main Application Areas for Digital Rock

Technology

13

1) SCAL: Fast turnaround, Difficult cases

Derive (primarily) kr and Pc data for reservoir modelling with

fast turnaround

• 2-3 month turnaround

• Multiple sensitivity studies: Incorporate ranges of

behaviour– perform 100’s of experiments on same

core plug to understand options and uncertainties

• Next generation reservoir description: Orders of

magnitude more data (SPE Forum 2011)

• Applicable on sidewall and cuttings. Restoration of

data for old core material

Main Application Areas for Digital Rock Analysis

14

Project Example: ADCO Carbonate Reservoir Study

• Within <1 year, 95 core plugs were analysed:

• A data set of porosity, permeability, FRF, m, n

• Samples represent 16 rock types spanning 4 orders of magnitude in permeability and 20 porosity units

• Pc curves for primary drainage and water flooding

• kr curves for primary drainage, water flooding and gas/oil @ Swi

• Resistivity index curves for primary drainage and water flooding

• Two Papers at 2012 Society of Core Analysts: SCA2012-03 & 13

15

Predicted vs. Experimental

Porosity [frac.]

0.00

0.10

0.20

0.30

0.40

0.50

0.00 0.10 0.20 0.30 0.40 0.50experimental

DR

P

Fm1-Field1

Fm2-Field1

Fm2-Field2

Fm2-Field3

1:1

range 4 por units

Permeability in mD

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04experimental

DR

P

Fm1-Field1

Fm2-Field1

Fm2-Field2

Fm2-Field3

1:1

Factor 2 range

Example: NMR - Pc - MICP

0.0

0.2

0.4

0.6

0.8

1.0

0.1 1 10 100 1000 10000

T2 [ms]

No

rma

lis

ed

Am

plitu

de

Sample 6 Exp

Sample 6 DRP

Normalised Data vs Pore Throat Size

0.0

0.2

0.4

0.6

0.8

1.0

0.001 0.01 0.1 1 10 100 1000Pore Throat Radius (Microns)

Dis

trib

uti

on

Fu

ncti

on

s

Lab MICP

DRP MICP

Saturation vs Pore Throat Size

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.001 0.01 0.1 1 10 100Pore Throat Radius (Microns)

Mercu

ry

Sa

tura

tio

n (

Fra

c)

Lab MICP

DRP MICP

0.0

0.2

0.4

0.6

0.8

1.0

0.1 1 10 100 1000 10000

T2 [ms]

No

rma

lis

ed

Am

plitu

de

Sample 6 Exp

Sample 6 DRP

-120

-80

-40

0

40

80

0.00 0.20 0.40 0.60 0.80 1.00

Water saturation, Sw (frac.)

Cap

illa

ry p

ress

ure

, P

c (

psi

)

PcPD_Exp

Pc_DY_18_48_Sim

BB 274 88 RT3

-120

-80

-40

0

40

80

120

0.00 0.20 0.40 0.60 0.80 1.00

Water saturation, Sw (frac.)

Cap

illa

ry p

ress

ure

, P

c (

psi

) Pc ExpHg curvePc SimPc Exp SIPc Exp FIPc Sim

R

R

T

6 0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0Sw

Re

lative

pe

rme

ab

ility

DRP - CC.RRT6

Lab - CC.RRT6

HM - Lab - CC.RRT6

Samlple ID RRT6 BU_570_36 BU_570_37 BU_570_106 DRP - CC.RRT6

k(mD) 17.5 13.3 25.8 9.31 23.1

Porosity (frac.) 0.275 0.26 0.301 0.264 0.269

Swi 0.134 0.1 0.06 0.11 0.09

Sorw 0.19 0.13 0.09 0.13 0.20

krw(Sorw) 0.62 0.88 0.86 0.82 0.70

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

0.0 0.2 0.4 0.6 0.8 1.0Sw

Re

lative

pe

rme

ab

ility

DRP - CC.RRT6

Lab - CC.RRT6

HM - Lab - CC.RRT6

Predicted vs. Experimental – SS Relative

Permeability

18

Pore to Plug Bonus:

Understanding of processes

– Enhanced Understanding

Fluid distributions, EOR/IOR recovery

mechanisms, wettability,

heterogeneity, uncertainties

Enhanced Oil Recovery

Oil in Place

Formation damage

4D IMAGING

Fluid sensitivity

Geochemical reactivity

Unconventional reservoirs

QUALITATIVE

Wettability

Pore scale wettability mapping

1

4

2

5

3

A1

A2

S w

Pc

0

+

-

10 S 1

S 3

S 5 S 2

S 4

Amott-Harvey Index

13

12

SS

SSIw

53

43

SS

SSI o

owAH III

USBM Index

2

1log

A

AIUSBM

Montaron, SPE 105041

20

Wettability Analysis

• Wettability characterization at

molecular and pore scales

• Couple with direct imaging of fluid

distributions

Fogden, SCA 2011 21

ENABLES FLUID MAPPING IN 3D

Dodd et al., IPTC 17696, 2014 22

Multistage Experimental Flooding Workflow

• Sample preparation:

17mm

• Dry image: Axial pressure: 1600psi, confining pressure:

~1500psi, temperature: ~50

• Brine saturation: ~150 pore volume of brine passes

through the core (1.5mol KCl+1mol NaI), @0.01cc/min pore

pressure:~1300psi

• CO2 injection: ~50 pore volume of scCO2 @0.01cc/min is

flushed through the sample & pressure:~1300psi

• Brine injection: ~150 pore volume of brine @0.01cc/min is

flushe through the sample, pressure: ~1300psi

Imaging of flooding at Reservoir T & P

X-Slices

Dry Brine 1 scCO2 Brine 2

( ) scCO2

( ) Brine

Legend

Residual scCO2

saturation: 25%

24

Imaging and modelling of flooding

at Reservoir T & P

Comparison of Simulation with Time-Series Imaging

Sim

ula

tion

Tim

e S

eries

Imagin

g

Pore Network Drainage (to Sw=0.5 )

Imbibition

(Sco2 =29%) Segmented File

Time Series Imaging

Grain

Clay

Pore

scCO2/Air

Brine

Simulation

Grain

Clay

Pore

scCO2 Invasion

Trapped Brine

Trapped scCO2

Brine Imbibition

Brine 1 scCO2 Brine 2: SCO2=31% Segmented File

• Upscaling & Uncertainties

– Upscaling in a SYSTEMATIC manner (calibrated at each

scale)

26

26 Spatial

Resolution (m) 10-9 10-6 10-5 10-3 10-2

Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir

model

10-1 1 10 102

Moving beyond plug scale… new territory

Basic Methodology

Ringrose, Svalbard Workshop: Modelling and risk assessment of geological

storage of CO2, 2009

Pore to Whole core (Log scale) upscaling

via digital rock technology

• Imaging and registration at whole core down to pore scale enables one to to classify

(cluster) the rock at each scale into rock types

• Rock types can be treated as distinct units in the upscaling process

• Enables pore to plug, plug to core and core to log workflows that integrate and

upscale data in a consistent manner for improved reservoir characterisation

• Aim to deliver dynamic data anchored to log responses 28

Images acquired and integrated at various scales

29

PU8

Section or whole

core image

oriented to show

registered plug

location

Core Scan

Plug Scan Mini-Plug Scan BSEM or FIBSEM

Whole Core CT Original Image resolution = 200x200x2000 µm

30

Porosity permeability trends by rock type

0.1

1

10

100

1000

0.05 0.1 0.15 0.2 0.25 0.3 0.35

Porosity

Pe

rm (

mD

)

Rock 1

Rock2

Rock3

From plug images derive….

…properties derived on individual “cm-inch” scales (on individual rock types)

32

Methodology that

honours

Properties and

Uncertainties at

EVERY Scale

Can Derive Effective

Upscaled Properties of

Complex (Laminated,

Carbonate) Intervals!

Drainage Waterflood

0

0,2

0,4

0,6

0,8

1

0 0,2 0,4 0,6 0,8 1

Sw

Re

l P

erm

0

1

2

3

4

5

0 0,2 0,4 0,6 0,8 1

Sw

Pc

(b

ar)

-5

-4

-3

-2

-1

0

1

2

0 0,2 0,4 0,6 0,8 1

Sw

Pc

(b

ar)

0

0,2

0,4

0,6

0,8

1

0 0,2 0,4 0,6 0,8 1

Sw

Re

l P

erm

Static Properties to lithofacies scale

Vary geometry & properties (e.g., rock type

distribution, lamina thickness, permeability,

porosity

Propagate the variability identified at the

pore scale directly to lithofacies curves

Enumerate the most probable flow

properties at the lithofacies scale including

variability Rustad, SPE11305, 2008

Have identified rock types at pore scale

Dynamic Properties to lithofacies scale

Enumerate the most probable MP flow properties at

the lithofacies scale including variability

Account for the direction of flow and the balance of

gravitational, viscous and capillary forces at various

scale: Need CL, VL & VE, tensorial based

Applied to full field gave significantly improved history

match

Klov, SPE84549, 2005

Future: Multistage Process

Pore to core to log/facies to…. 36

Conclusion: The Multi-scale Reservoir

Characterisation Problem

Question: How do we accurately and effectively model fine-scale heterogeneities that will

impact on petrophysical response & fluid flow in a reservoir-scale model?

? ?

Option: Follow a detailed, sequential, multi-scale, pore-to-plug-to-log- (to-

reservoir) characterisation workflow which incorporates 3D multiscale imaging,

robust classification and upscaling procedures 37

Society of Petroleum Engineers

Distinguished Lecturer Program www.spe.org/dl 38

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