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Special Core Analysis Challenges, Pitfalls and Solutions Colin McPhee SPE London May 26 2015

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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

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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)

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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

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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

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

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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

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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

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

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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

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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

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Questions? 32