1. Special Core Analysis Challenges, Pitfalls and Solutions
What’s so “special” about..
Special Core Analysis Challenges, Pitfalls and Solutions
Colin McPhee
SPE London May

2. The geomodel juggernaut!=
Modelling is ‘finished’, but the forecasts do not match observations, imagine the reaction to a request to go back & check core data inputs.
Often happens & each time the team’s protestations are loud.
Very hard to stop the ‘geomodel juggernaut’, usually built on a tight budget that is almost spent & to a deadline that is getting closer

3. Cultural resistance to change – “I know my place”
Cultural issues can prevent the models from being improved.
Reluctance to change model inputs as may have to admit mistakes were made to peers.
Misplaced respect for elders.
Fear of management’s response when told of model rebuild

4. Core data for static and dynamic models
Core tests provide fundamental input to static (in place) and dynamic (recovery factor) reservoir models
Core data experiments are….
The ground truth!
N, , Sw from RCA & SCAL
kro and krw from SCAL

5. The elephant in the room
SCAL data have uncertainties that few end users want to discuss or contemplate (or even want to know about)
Misinterpretation and poor practice impact on static and dynamic modelling

6. The Ground may be shakier than you think
Based on review of > 50,000 SCAL experiments……
70% of SCAL unfit for purpose
core damage
variable data quality
inadequate program planning and inappropriate design
poor reporting standards
method-sensitivity
vendors reluctant to share experience and expertise

7. Core damage During coring During core recovery
Oil-based mud usually alters wettability
Difficult to remove sometimes
Mud invasion and shear failure in weak rock
During core recovery
POOH too fast results in tensile fracturing if pore pressure cannot dissipate
During wellsite/lab handling
Liners flexing/bending
Freezing
Poor stabilisation
Poor preservation

8. Formation evaluation – examples of SCAL
Porosity
Permeability
Porosity
Permeability
Capillary Pressure
Drainage and imbibition
Relative Permeability

9. Porosity T > HOD > E Core porosity - Total or Effective?
Humidity dry for effective porosity?
Often assumed negligible in Carbonates
Often significant in Clastics
Grains
Clay Layers
Clay surfaces & Interlayers
Small Pores
Large Pores
Isolated Pores
Irreducible or Immobile Water
Structural Water
Volume available for storage
Capillary Water
Bound Water
Absolute or Total Porosity Øt
VClay
Matrix
Effective Porosity Øe
Usually assumed negligible in Clastics
May be significant in Carbonates
T > HOD > E

10. Porosity (RCA) Two different methods Two different results! Vg & VbHg
Vp+Vg
Vg & VbHg
Two different methods
Two different results!
Vg+VbHg
Vp & Vg

11. Porosity compaction at stress
Sensitive to “insignificant” artefacts
Two labs – two different results!
Annulus volume between sleeve & plug
Check pre- and post-test results
stress/amb
Net confining stress (psi)
Porosity Change (p.u.)
Pre-test porosity (%)

12. Permeability What is the permeability in your static 3D model?
Air permeability?
Klinkenberg? – measured or from a correlation?
Brine?
Ambient or stressed?
What stress?
How measured – steady or unsteady-state?
How were plugs prepared?
Does it matter?
Kair after HOD (mD)
Kair after harsh drying (mD)
Kair at 400 psi (mD)
Stress (mD)

13. Capillary pressure (drainage)
Principal application in saturation-height modelling
Pc (Height) versus Sw by rock type, rock quality and height
Water Saturation (-)
Height above FWL (ft)
Normalised Sw
J Function
Carbonate J function by R35 bin

14. Capillary pressure (drainage)
Mercury injection capillary pressure
NOT a capillary pressure test (just looks like one)
No Swir: Sw goes to zero at high injection pressure
Lower Sw at high Pc
Core damage at high injection pressures?

15. Capillary pressure (drainage)
Centrifuge
Pc maximum at inlet face of plug
Calculation of inlet face saturation
Inlet face Pc (psi)
Water Saturation

16. Capillary pressure (drainage)
Centrifuge vs MICP vs porous plate (PP)
MICP
no wetting phase – no Swir – Sw always lower at higher Pc
Centrifuge
No entry pressure (compared to MICP & PP) - Abrupt transition to Swir
MICP
PP Pc
Centrifuge
Scaled Lab Pc (psi)
Water Saturation

17. Capillary pressure (drainage)
Porous plate
Good but slow
Potential loss of capillary contact
Potentially slow drainage
Water Saturation
Time (days)
Water Saturation
Air-Water Capillary Pressure (psi)

18. Imbibition Pc (water-oil)
Water Saturation
Capillary Pressure (psi)
Example results oil-brine imbibition Pc
Lab average Sw does not agree with Dean-Stark
If average Sw wrong then end face Sw and Pc-Sw wrong
Did lab not think Sro = 40%-50% strange?
3 iterations (and about 3 months) before lab’s calculated Pc-Sw curves matched our calculations
Lab upper-management were initially unaware of the issues
errors later corrected
Plugs found to be fractured
Water Saturation
Capillary Pressure (psi)

19. Relative permeability
“Most relative permeability data are rubbish – the rest are wrong!” Jules Reed, LR Senergy, 2013
>200 samples – 6 usable

20. Why are they rubbish? Plugs unrepresentative or plugged incorrectly
Swir too high and/or non-uniform
Wettability contaminated or unrepresentative

21. Why are they wrong? Coreflood testing invalidates analytical theory
Flow is linear and uni-directional
Capillary effects are negligible
Water Saturation (-)
Length along core (slice)
Water Saturation
Ncres x100
Ncres x10
Ncres
Sample Length

22. Saturation is controlled by capillary number (Nc)
Capillary end effects
Ncres x100
Ncres x10
Ncres
Differential Pressure
Sample Length
Water Saturation
Ncres x100
Ncres x10
Ncres
Saturation is controlled by capillary number (Nc)
Nc = k DP
s Dx
Sample Length

23. What are the solutions? Carefully review legacy data
Identify uncertainties and impact on:
In place calculations
Recovery factor
What is the value of information?
Is it worth doing the experiments at all?
Or is it because we have a table to fill in in Eclipse
New core data
learn from legacy data review
integrated program design
focal point
improved test and reporting documentation

24. What are the solutions? Lab audit
Assess resources, equipment, experience and expertise of management and technicians
Check plugs
Test data set interpretation
Design programme with stakeholders and lab
Do not “cut and paste” from previous jobs
Do not pick from a “menu”
Draw up flowchart
Look where value added at little incremental cost
Iterate, iterate, iterate

25. What are the solutions? Relative permeability
Ensure wettability is representative
Test design
In situ saturation monitoring
Coreflood simulation

26. Relative permeability - ISSM
Sw(NaI)
X-ray adsorption 0%
100%
Reveals what is going on in the core plug
Length along core (slice)
Water Saturation

27. Relative permeability - coreflood simulation
Recommended practice for ALL relative permeability tests
Several non-unique solutions are possible so need to sense check

28. Test specifications/data reporting
Detailed test and reporting specifications
define test procedures and methods
Define what, when and how reported
experimental data essential
use to verify and check lab calculations
allows alternative interpretation
most labs retain experimental data only for short time
Tedious and time consuming … but
essential in data audit trail
invaluable in unitisation
can save money as you may not have to repeat tests

29. Test specification example – centrifuge Pc

30. Plugbook Plug data Base properties History SCAL test history
porosity and permeability
History
when/how cut, cleaned & dried
SCAL test history
Plug CT scans
Heterogeneity
Damage?
Plug photographs
pre-and post-test
Can be easily customised

31. Price is what you pay. Value is what you get - Warren Buffet
Summary
Lab test pitfalls have a huge impact on core analysis modelling data input
But....
uncertainties are recognisable and manageable
best practice, real-time QC, and robust workflows ensure that core data are fit for purpose prior to petrophysical analysis.
a forensic data quality assessment can minimise data redundancy and reduce uncertainty in reservoir models
Price is what you pay. Value is what you get - Warren Buffet

32. Questions?