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  • 1. Measuring the Successof Waterfloods G. Renouf Saskatchewan Research Council

2. Acknowledgments

    • Petroleum Technology Research Council
    • BP Exploration (Alaska) Inc.
    • Canadian Natural Resources Ltd.
    • Canetic Resources Inc.
    • Husky Oil Energy Ltd.
    • Nexen Inc.
    • Shell International Exploration & Production BV
    • Total E&P Canada Ltd.

3. Outline of Talk

  • Motivation
  • Recap of previous studies
  • Findings
  • Conclusions and recommendations

4. Motivation

  • Waterflooding common in heavy oil reservoirs
    • 139 in AB and 68 in SK represent 24% of heavy oil in place
  • Additional recovery ranges from 0.4% to 47%.
  • We need to understand the reasons behind the extra-successful and failed waterfloods.
  • Extensive body of knowledge on waterflooding lighter oils is it applicable to heavy oil waterflooding?

5. Previous Studies

  • 2004 Study Heavy Oil Waterflooding Scoping Study
    • Statistics of 54 heavy oil waterfloods
    • Interviews with 25 engineering & field staff
    • 8 abandoned waterfloods examined
  • 2006 Study Measuring the Success of Western Canadian Waterfloods
    • Multivariate analysis
    • Compares heavy to medium oil waterfloods

6. Multivariate vs Univariate Analysis

  • SI=secondary production as % of OIP/#yrs wf

7. Multivariate Analysis

  • General
    • Reservoir and operating parameters are xs (41 xs)
    • Success measurements are ys (8 ys)
    • Each waterflood is an observation point (168 points)
  • Details
    • Can include qualitative xs or ys
    • Types:PCA, PLS, PCR
    • We used PLS

8. 12.42 1992 808 14.5 969 1.073 14 30 5.9 826 0.31 0.74 0.12 0.57 9 1995 900 15.9 960 1.073 20 30 6.34 1166 1.46 1.99 0.27 1.70 8 1997 860 14.5 969 1.08 15 31 5.72 186 0.79 1.12 0.11 1.13 4.83 2000 860 16.0 959 1.054 33.1 31 5.5 190 0.00 0.00 0.08 0.00 16.58 1988 863 17.0 953 1.063 23 28.5 7.47 777 0.70 1.26 0.14 1.02 9 1996 863 17.0 953 1.063 23 32 5.75 502 0.00 0.00 0.26 0.00 16.33 1988 818 14.1 972 1.039 25 30 4 1344 0.21 0.08 0.20 0.17 45.92 1959 823 12.6 982 1.078 31 24.1 11.46 3076 0.17 0.19 0.12 0.30 16.58 1988 851 15.3 964 1.039 35 30 4.83 2736 0.57 0.48 0.14 0.50 16.5 1988 851 15.3 964 1.039 35 30 9.16 259 0.63 0.34 0.22 0.44 16.58 1988 814 12.7 981 1.039 31 29.6 6.8 793 0.19 0.04 0.10 0.14 34.83 1969 823 13.2 978 1.05 25 26.6 3.72 719 0.27 0.13 0.13 0.33 22.67 1981 532 16.0 959 1.02 21 30 4.1 421 0.29 0.23 0.11 0.21 16.67 1988 787 14.7 968 1.111 32 28 4.75 1409 0.92 0.24 0.11 0.85 16.58 1988 737 14.4 970 1.029 20 28 4.5 1125 0.36 0.42 0.06 0.32 14.58 1970 541 13.5 976 1.017 15 34 3.9 987 0.43 0.41 0.67 0.28 7.58 1997 625 18.1 946 1.036 25 30 5.95 227 0.31 0.23 0.08 0.32 14.58 1990 625 18.1 946 1.031 25 30 4.5 227 0.35 0.93 0.09 0.61 16.17 1988 625 18.1 946 1.031 25 30 5.46 420 0.88 0.58 0.07 0.57 19.08 1985 625 18.1 946 1.031 25 30 9.58 518 0.62 0.67 0.08 0.68 20.33 1984 712 17.9 947 1.033 23 35 2.75 971 0.16 0.10 0.23 0.11 11.5 1968 518 15.6 962 1.031 21 30 6.2 1263 0.03 0.28 0.38 0.28 20.67 1971 472 15.4 963 1.033 16 35.7 4.33 874 0.16 0.15 1.28 0.15 2.67 1992 475 14.8 967 1.023 20 30 4.9 2229 0.32 0.32 0.32 0.43 13.08 1971 493 14.4 970 1.016 20 32.37 3.99 1749 0.12 0.20 0.74 0.16 30.58 1972 563 15.3 964 1.02 18 35 4.15 923 0.05 0.28 0.27 0.17 38.42 1966 566 15.3 964 1.02 18 35 4.52 2220 0.06 0.44 0.27 0.20 10.92 1970 491 15.1 965 1.018 15.3 36 2.82 1604.5 0.09 0.24 0.34 0.26 14.25 1969 535 14.7 968 1.017 13 35 5.18 1522 0.07 0.07 0.29 0.06 9. Scatter Plot 10. Loading Plot 11. Variable Importance Plot 12. Previous Studies

  • Most important reservoir parameters for medium oils: permeability; heterogeneity; reservoir temperature; porosity
  • Most important reservoir parameters for heavy oils: viscosity/permeability; formation volume factor; production depth
  • Comparison between the importance of injection throughput rate and years to fill-up shows pressure maintenance might not be only benefit.

13. Previous studies, continued

  • Operating parameters also differed for heavy vs medium oil waterfloods
  • Horizontal & directional production and injection wells very important for heavy oil waterflooding; reducing conversion of producers to injectors
  • Well spacing important to success of both types
  • Screening criteria not very discriminating
  • Categorization reasonably successful

14. Tasks to Improve Database

  • Add more points (more waterfloods)
  • Add more variables (more reservoir and operating parameters)
  • Make sure each y-variable (each success measure) is as accurate as possible

15. Add More Points, More Variables

  • Original plan
    • Incorporate Alaska waterfloods
    • Looked at 11 Alaskan fields
    • 8 of 11 used gas and WAG injection with waterflooding
  • Actual 2007 tasks
    • Grew database from 83 to 168 waterfloods
    • New category from Alberta EUB data
  • 8 new parameters including: pumping, flowing wells, operating company

16. Missed WF 250031 43 WATER FLOODUPPER MANNVILLE EEALDERSON250028 43 UPPER MANNVILLE BBALDERSON250027 43 WATER FLOODUPPER MANNVILLE AAALDERSON250026 43 WATER FLOODUPPER MANNVILLE ZALDERSON250025 43 WATER FLOOD AREAALDERSON250025 43 PRIMARY AREAALDERSON250025 43 TOTALUPPER MANNVILLE YALDERSON250021 43 UPPER MANNVILLE UALDERSON250020 43 UPPER MANNVILLE TALDERSON250019 43 WATER FLOODUPPER MANNVILLE SALDERSONPool Field Pool Field Field and Pool Codes Field and Pool 17. Accuracy of SuccessMeasurements

  • Two Success measurements proven themselves
    • SI=Secondary Production/OIP/Yrs WF*100%
    • SI-FU=SI calcd year after fill-up
  • SI Calculation: After WF start, oil is produced as both primary and secondary, want to fractionate the total
    • Primary
    • Secondary
  • SIR estimates Coleville
    • 4.6% primary
    • 19.5% enhanced
  • At WF start, 2.5% OOIP produced, leaving 2.1% primary

Primary =2.1 2.1+19.5 Secondary =19.5 2.1+19.5 18. Primary Production Measurement SI Calcn assumes constantproduction rate Actual productionrate declinesnon-linearly WF Start 19. Decline Fitting

  • Fit pre-waterflood production data with 3 types of equations
    • Exponential
    • Harmonic
    • Hyperbolic
  • Production data were individual wells or groupings of similarly-behaved wells
  • 38% of waterfloods could not be decline-fitted
  • Hyperbolic generally best (68% of production data)

20. Decline Calculations: Original Plan SI SI-FU SI-Decline SI-FU Decline EXPONENTIAL HARMONICHYPERBOLIC 21. Exponential vs Hyperbolicvs Harmonic 772,000 711,000 565,000 37% 22. Decline Equation 23. Decline Calculations SI SI-FU SI-Decline SI-FU Decline SI SI-FU SI-Exp SI-Har SI-Hyp SI-FU-Exp SI-FU-Har SI-FU-Hyp 24. Examples of SI 1.27 1.98 Viking-Kinsella X 0.45 0.68 Taber Glauc K 0.78 0.77 Sibbald 1.66 Mantario North 0.31 0.51 Cactus Lake unit 2 0.71 0.85 Senlac 0.15 0.06 Aberfeldy SI-Hyp SI Waterflood 25. Multivariate PLS Models 53 WFs 75 WFs Better than 2007 0.505 SI-Hyp, SI 50%, SI-FU-Har 10%, SI-FU-Exp 10% Medium 0.397 SI-Hyp, SI 50%, SI-FU-Har 10%, SI-FU-Exp 10% Heavy - Used 0.435 SI-Hyp, SI-Exp,SI-FU Hyp 10%, SI-FU Exp 10% Heavy - Best 0.507 SI-Exp, SI 50%, SI-Hyp 10%,SI-Har 10% All 0.532 SI, WOR 5% Medium - 2006 0.704 SI, WOR 5% Heavy - 2006 0.545 SI, WOR 80% All WFs - 2006 Q 2 Cum Y Variables Dataset 26. Waterfloods Newly Addedto Database 27. Heavy vs Medium WFs 28. Important Parameters toHeavy Oil WFs 29. Effect of Net Pay 30. Viscosity Related Parameters

  • Viscosity and Viscosity/Permeability significant to success of heavy oil wfs and insignificant to medium oil wfs
  • Viscosity data for only 30 of 168 waterfloods
  • Dataset restricted to these 30 waterfloods
    • Inconsistent results: about same level of importance heavy oil wfs, more important to medium oil wfs
  • Tested Viscosity predictor for Alaska reservoirs
  • Poor prediction of viscosity
  • Formula was important to medium oil wf success

31. Injection Rate Parameters 32. VRRcum vs VRR Deviation from 1

  • Heavy WFs: 13% VRR > 1.10
  • Medium: 32% VRR > 1.10