Making Best Use of Routine Core Analysis Data - Best use of core analysis.pdf · H E L I X E N E R G Y S O L U T I O N S Making Best Use of Routine Core Analysis Data Aim to demonstrate

H E L I X E N E R G Y S O L U T I O N S

Making Best Use of

Routine Core Analysis Data

Craig Lindsay

Senior SCAL Engineer

Helix RDS Limited

28th. March 2007


Making Best Use of Routine Core Analysis Data

Aim to demonstrate how basic core data can be put to effective use

• Core material

• Routine core analysis (RCA) data types

• Example applications of RCA data

- Quantifying Permeability Heterogeneity

- Sampling for Special Core Analysis (SCAL) Studies

• Conclusions


Core

3.8 cm10 cm

Core Plug Core SlabResinatedCore

Whole Core

10 cm


Routine Core Analysis Samples

• RCA Tests

–sidewall (< 2.54 cm.)

–core plugs (2.54 –3.80 cm.)

• “horizontal”and “vertical”

–full diameter ( 15 –30 cm. (L), 10 –15 cm. (D) )

–whole core (up to 60 cm. (L), 10 –15 cm.(D))

Kv

KhScreen


Routine Core Analysis Data


Applying routine core analysis data

• Many possible applications of RCA data

• Examine two example applications of RCA data to obtain best VOI

- Quantifying Permeability Heterogeneity

- Sampling for Special Core Analysis (SCAL) Studies

Issues of upscaling and application in reservoir models beyond the scope

of the presentation


Measuring Core Permeability - Summary

InIn

RegulatorRegulatorRegulatorRegulator

ManometerManometer

Confining PressureConfining Pressure

P1P1 P2P2PaPa

QaQa

CoreholderCoreholder

PlugPlugInIn

RegulatorRegulatorRegulatorRegulator

ManometerManometer

Confining PressureConfining Pressure

P1P1 P2P2PaPa

QaQa

CoreholderCoreholder

PlugPlug

Plugpermeameter

Probepermeameter

L

AQa

Pi Pa

Core Plug Gas in: Qi, Pi

Tip Seal

Rock Surface

Atmospheric Pressure, Pa

Gas flow lines

k

Q P

P PFa

i i i

i a

20002 2

.

kQ P

P PLAa

a a a

i a

20002 2


Permeability –Data Presentation

0.01

0.1

1

10

100

1000

0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19

Helium Porosity, frac.

Air

Per

mea

bili

ty,m

D.

• Tabular data

• Depth plots / x-plots –most useful


Permeability –How well is it Quantified fromcore?

• Core plug measurements at 1 per ft –may not fully characterise the

level of permeability heterogeneity –especially in laminated formations

• How do we know?

• Hurst and Rosvoll 1–proposed method to determine minimum

number of measurements (No) to determine Arithmetic Mean

Permeability +-20%

• Reducing the tolerances –unrealistic number of measurements

• Calculate coefficient of variation,

Cv = Standard Deviation / Arithmetic Average

No = 100 Cv2


Permeability –How well was it Quantified

• Based upon Cv Corbett and Jensen2 proposed heterogeneity classes:

- 0 - < 0.5 Homogeneous

- 0.5 - <1 Heterogeneous

- >1 Very heterogeneous

Three examples of applying these principles:

Rotliegend reservoir SNS

• 150 plugs acquired (1 per ft.)

• Cv = 2.5, No = 625 Very heterogeneous

• Plug alone data did not quantify permeability heterogeneity –probe

permeability data @ 5 measurements per ft = 729 points.


Permeability –How well was it Quantified

Forties Sandstone (marginal) - NNS


• Cv = 1.6, No = 261 Heterogeneous

• Plug data did not quantify permeability heterogeneity –probe

permeability data @ 5 measurements per ft = 590 points.

Forties Sandstone (main channel) - NNS


• Cv = 0.9, No = 81 Heterogeneous

• Plug data did effectively quantify permeability heterogeneity


Selecting samples for SCAL - Background

• Static SCAL –capillary pressure (fluid contacts, Sw distribution), Sw

model parameters - m, n, Qv, m*, n* (water saturation), porosity

compaction, liquid permeability, PV compressibility (primary recovery)

• Dynamic SCAL –wettability, relative permeability, imbibition capillary

pressure (secondary recovery)

• Formation damage studies

• Geomechanical / specialist studies


Selecting samples for SCAL - Caveats

• Exact sampling protocols depend upon the specific objectives…

• SCAL can be only be performed on a sub-set of samples –due to cost

and time constraints

• This example based upon assumption that RCA plugs can be employed

– where RCA sample preparation has been proven to be non-

damaging and fresh state core is not required

• Correct use of RCA during sample selection essential


Selecting samples for SCAL from poro / perm

• Poro perm x-plot –sufficient? No very crude

• Initially check reservoir zonation

0.01

0.1

1

10

100

1000

0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19


Air

Per

mea

bili

ty,m

D.

0.01

0.1

1

10

100

1000

0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19


Air

Per

mea

bili

ty,m

D.

Zone X

Zone Y


Selecting samples for SCAL –FZI and HFU

• Determine FZI (Amaefule et al3) from RCA poro perm data

• FZI - a unique parameter that incorporates the geological attributes

of texture and mineralogy in the discrimination of distinct reservoir

hydraulic or flow units (HFU)

e

kRQI

0314.0

e

ez

1z

RQIFZI

• HFU - a mappable portion of the total reservoir within which

geological and petrophysical properties that affect the flow of fluids

are consistent and predictably different from the properties of other

reservoir rock volumes


Selecting samples for SCAL - HFU

• HFU -

0.0

0.2

0.4

0.6

0.8

1.0

-0.70 -0.50 -0.30 -0.10 0.10 0.30 0.50

Log FZI

Fre

quen

cyHU 1 HU 2 HU 3 HU 4 HU 5

0.01

0.1

1

10

100

1000

0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19


Air

Per

mea

bili

ty,m

D.

HU 5HU 4HU 3HU 2HU 1

0.01

0.1

1

10

100

1000

0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19


Air

Per

mea

bili

ty,m

D.


Selecting reservoir representative samples forSCAL

• HFU –number and FZI range defined but..

• Need to determine:

- How representative is the cored interval of the reservoir unit

(use log / analogue data to determine)?

- Are entire / part zones represented?

- What proportion of each interval does each HFU represent?

Need to consider:

- Static SCAL –whole range of rock qualities represented

- Dynamic (flow) SCAL –bias sample selection towards best rock

quality


Selecting samples for SCAL –other issues

• Other considerations:

- Economic –how many tests can be performed for the available

budget?

- Minimum number of data points to define a property, e.g. 4 –5

to define cementation exponent (m).?

1

10

100

0.1 1Porosity at 3685 psig (-)

Fo

rmat

ion

Fac

tor

at36

85p

sig

(-)

m=1.97


Selecting samples for SCAL –Loranz diagram

• Define Storage Hydraulic Units (SHU) and Flow Hydraulic Units

(FHU) –use of Lorenz diagram (Jensen et al.4)- a graphical

presentation of the Lorenz coefficient (Lc)

Lorenz Plot

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Fraction of total storage capacity (phi*h), ci

Fra

ctio

no

fto

talf

low

cap

acit

y(k

*h),

fi

Transmissivity HU

Storativity HU

0.01

0.1

1

10

100

1000

10000

0 0.1 0.2 0.3 0.4

Helium Porosity, fraction

Air

Per

mea

bili

ty,m

D

Transmissive HU

Storage HU


Selecting samples for SCAL –SHU and FHU

• Zonal distribution of SHU & FHU

8800

8820

8840

8860

8880

8900

8920

8940

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Porosity, frac.

Dep

th,f

t.T

VD

SS

THUSHU

8800

8820

8840

8860

8880

8900

8920

8940

0.01 0.1 1 10 100 1000

Air Permeability, mD.

Dep

th,f

t.T

VD

SS

THUSHU


Selecting samples for SCAL - Example

• The example SCAL programme:

- Confined to Static properties (m, n and drainage Pc), therefore

all HU to be represented

- The cored interval represented 1 entire reservoir zone, 2 part

zones

- The part represented zones were considered representative of

the whole zone

- Budgetary constraints on programme costs

- Time constraints –FF/RI pairs rather than same samples

- Minimum number of sample for m, n etc. would be 4 per zone


Selecting samples for SCAL - Summary

• The SCAL programme:

- 5 HU, 3 zones

- 4 FF / RI pairs per zone

- Zonal HU’s represented appropriately

- Met budgetary and time constraint criteriaHU Zone X HU Actual SCAL Zone Y HU Actual SCAL Zone Z HU Actual SCALHU1 16 22% 25% 1 2% -HU2 16 73% 75% 53 72% 75% 11 20% 25%HU3 5 23% 25% 5 7% - 8 15% 25%HU4 1 5% - 16 30% 25%HU5 18 33% 25%


Conclusions

• Presented are some examples of how basic RCA data can be

employed to derive considerable VOI

• Tools employed are straightforward documented procedures

• Important to benefit from use of published and current research

• Methodologies employed were fit for purpose –other techniques

available

• Issues of application and upscaling beyond the scope of this

presentation

• Don’t overlook the value of appropriately derived RCA data


References

1Hurst, A. and Rosvoll, K. 1991. Permeability variations in sandstones and their relationship to sedimentary

structures, Reservoir Characterisation II, Academic Press, San Diego, p. 166-196

2Corbett, P.W.M., and Jensen, J.L., 1992. Estimating the mean permeability: How many measurements do you

need? First Break, 10, p89-94.

3Amaefule et al. Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units

and Predict Permeability in Uncored Intervals / Wells, SPE 26436, 1993

4Jensen, J.L., Lake, L.W., Corbett, P.W.M., and Goggin, D.J., 1997, Statistics for Petroleum Engineers and

Geoscientists, Prentice-Hall, New Jersey


Acknowledgements

AFES

Thank You for your attention!

Documents

Making Best Use of Routine Core Analysis Data - Best use of core analysis.pdf · H E L I X E N E R G Y S O L U T I O N S Making Best Use of Routine Core Analysis Data Aim to demonstrate