Transcript of Special Core Analysis Challenges, Pitfalls and Solutions Colin McPhee SPE London May 26 2015.
- Slide 1
- Special Core Analysis Challenges, Pitfalls and Solutions Colin
McPhee SPE London May 26 2015
- Slide 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 teams 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 2 =
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- 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 managements response when
told of model rebuild 3
- Slide 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. ground truthThe
ground truth! 4 N, , Sw from RCA & SCAL kro and krw from
SCAL
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- 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 5
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- 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 6
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- Core damage During coring 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 7
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- Formation evaluation examples of SCAL Porosity Permeability
Capillary Pressure Drainage and imbibition Relative Permeability 8
Porosity Permeability
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- Porosity Core porosity - Total or Effective? Humidity dry for
effective porosity? 9 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 t VClay
Matrix Effective Porosity e Usually assumed negligible in Clastics
May be significant in Carbonates T > HOD > E
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- Porosity (RCA) Two different methods Two different results! 10
Vp+Vg Vg & Vb Hg Vg+Vb Hg Vp & Vg
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- Porosity compaction at stress Sensitive to insignificant
artefacts Two labs two different results! Annulus volume between
sleeve & plug Check pre- and post-test results 11 stress / amb
Net confining stress (psi) Porosity Change (p.u.) Pre-test porosity
(%)
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- 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? 12 Kair
after HOD (mD) Kair after harsh drying (mD) Kair at 400 psi (mD) Kg
@ Swir @ Stress (mD)
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- Capillary pressure (drainage) Principal application in
saturation-height modelling Pc (Height) versus Sw by rock type,
rock quality and height 13 Carbonate J function by R35 bin Water
Saturation (-) Normalised Sw J Function Height above FWL (ft)
- Slide 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? 14
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- Capillary pressure (drainage) Centrifuge Pc maximum at inlet
face of plug Calculation of inlet face saturation 15 Inlet face Pc
(psi) Water Saturation
- Slide 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 16 MICP PP Pc Centrifuge Scaled Lab Pc
(psi) Water Saturation
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- Capillary pressure (drainage) Porous plate Good but slow
Potential loss of capillary contact Potentially slow drainage 17
Water Saturation Air-Water Capillary Pressure (psi) Water
Saturation Time (days)
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- Imbibition Pc (water-oil) 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 labs
calculated Pc-Sw curves matched our calculations Lab
upper-management were initially unaware of the issues errors later
corrected Plugs found to be fractured 18 Water Saturation Capillary
Pressure (psi) Water Saturation Capillary Pressure (psi)
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- Relative permeability Most relative permeability data are
rubbish the rest are wrong! Jules Reed, LR Senergy, 2013 19 >200
samples 6 usable
- Slide 20
- Why are they rubbish? Plugs unrepresentative or plugged
incorrectly Swir too high and/or non-uniform Wettability
contaminated or unrepresentative 20
- Slide 21
- Why are they wrong? Coreflood testing invalidates analytical
theory Flow is linear and uni-directional Capillary effects are
negligible 21 Water Saturation Nc res x100 Nc res x10 Nc res Sample
Length Water Saturation (-) Length along core (slice)
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- Differential Pressure Sample Length Water Saturation Nc res
x100 Nc res x10 Nc res Nc res x100 Nc res x10 Nc res Saturation is
controlled by capillary number (Nc) Nc = k P x Capillary end
effects
- Slide 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 23
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- 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 24
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- What are the solutions? Relative permeability Ensure
wettability is representative Test design In situ saturation
monitoring Coreflood simulation 25
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- Relative permeability - ISSM Reveals what is going on in the
core plug 26 Sw(NaI) X-ray adsorption 0%0% 100 % Length along core
(slice) Water Saturation
- Slide 27
- Relative permeability - coreflood simulation Recommended
practice for ALL relative permeability tests Several non-unique
solutions are possible so need to sense check 27
- Slide 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
28
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- Test specification example centrifuge Pc 29
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- Plugbook Plug data Base properties 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 30
- Slide 31
- 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 31 Price is what you pay.
Value is what you get - Warren Buffet
- Slide 32
- Questions? 32