Whats so special about Special Core Analysis Challenges
What’s so “special” about. . Special Core Analysis Challenges, Pitfalls and Solutions Colin Mc. Phee SPE London May 26 2015
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 2
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 3
Core data for static and dynamic models • Core tests provide fundamental input to static (in place) and dynamic (recovery factor) reservoir models N, , Sw from RCA & SCAL kro and krw from SCAL • Core data experiments are…. • The ground truth! truth 4
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
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
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
Formation evaluation – examples of SCAL • Porosity • Permeability • Capillary Pressure Porosity Permeability • Drainage and imbibition • Relative Permeability 8
Porosity • Core porosity - Total or Effective? • Humidity dry for effective porosity? T > HOD > E Absolute or Total Porosity Øt Matrix Effective Porosity Øe VClay Grains Clay Layers Clay surfaces & Interlayers Small Pores Bound Water Capillary Water Structural Water Large Pores Isolated Pores Volume available for storage Irreducible or Immobile Water Usually assumed negligible in Clastics Often assumed negligible in Carbonates Often significant in Clastics May be significant in Carbonates 9
• Two different methods Vg & Vb. Hg Porosity (RCA) • Two different results! Vp & Vg Vp+Vg Vg+Vb. Hg 10
Porosity compaction at stress • Sensitive to “insignificant” artefacts • Two labs – two different results! • Check pre- and post-test results stress/ amb • Annulus volume between sleeve & plug Porosity Change (p. u. ) Net confining stress (psi) Pre-test porosity (%) 11
Permeability • What is the permeability in your static 3 D model? Kg @ Swir @ Stress (m. D) Kair after harsh drying (m. D) • 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 (m. D) Kair at 400 psi (m. D) 12
Capillary pressure (drainage) Height above FWL (ft) • Principal application in saturation-height modelling • Pc (Height) versus Sw by rock type, rock quality and height J Function Water Saturation (-) Carbonate J function by R 35 bin Normalised Sw 13
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
Capillary pressure (drainage) • Centrifuge • Pc maximum at inlet face of plug Inlet face Pc (psi) • Calculation of inlet face saturation Water Saturation 15
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 Scaled Lab Pc (psi) PP Pc Centrifuge 16 Water Saturation
Capillary pressure (drainage) • Porous plate Water Saturation Air-Water Capillary Pressure (psi) • Good but slow • Potential loss of capillary contact • Potentially slow drainage Time (days) Water Saturation 17
• errors later corrected • Plugs found to be fractured 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 Capillary Pressure (psi) Imbibition Pc (water-oil) Water Saturation 18
Relative permeability • “Most relative permeability data are rubbish – the rest are wrong!” Jules Reed, LR Senergy, 2013 >200 samples – 6 usable 19
Why are they rubbish? • Plugs unrepresentative or plugged incorrectly • Swir too high and/or non-uniform • Wettability contaminated or unrepresentative 20
Why are they wrong? • Coreflood testing invalidates analytical theory Water Saturation (-) Water Saturation • Flow is linear and uni-directional • Capillary effects are negligible Ncres x 100 Ncres x 10 Ncres Sample Length along core (slice) 21
Water Saturation Differential Pressure Capillary end effects Ncres x 100 Ncres x 10 Ncres Sample Length Saturation is controlled by capillary number (Nc) Ncres x 100 Ncres x 10 Ncres Sample Length Nc = k DP s Dx
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
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 24
What are the solutions? • Relative permeability • Ensure wettability is representative • Test design • In situ saturation monitoring • Coreflood simulation 25
0 % Sw(Na. I) 100 % Water Saturation • Reveals what is going on in the core plug X-ray adsorption Relative permeability - ISSM 26 Length along core (slice)
Relative permeability - coreflood simulation • Recommended practice for ALL relative permeability tests • Several non-unique solutions are possible so need to sense check 27
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
Test specification example – centrifuge Pc 29
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
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 31
Questions? 32
- Slides: 32