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The Analysis and Interpretation of Water-Oil Ratio Performance in Petroleum Reservoirs

The Analysis and Interpretation of Water-Oil Ratio Performance in Petroleum Reservoirs. Valentina Bondar. Texas A&M University Harold Vance Department of Petroleum Engineering. 12 January 2001. Outline. Introduction Conventional WOR Analysis (Steady-State WOR Model)

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The Analysis and Interpretation of Water-Oil Ratio Performance in Petroleum Reservoirs

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  1. The Analysis and Interpretation of Water-Oil Ratio Performance in Petroleum Reservoirs Valentina Bondar Texas A&M University Harold Vance Department of Petroleum Engineering 12 January 2001

  2. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  3. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  4. Objective • Provide the development of a pseudo-steady-state WOR equation. • Estimate and compare values of "movable" oil using various straight-line extrapolation methods. • Introduce two new methods for esti-mating Np,mov. • Perform "qualitative" analysis of oil and water production data.

  5. Introduction • 20 Wells in the North Robertson Unit (West Texas) • 8 Wells in the West White Lake Field (South Louisiana)

  6. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  7. Linear log(krw/kro) versus Sw Conventional WOR Analysis Steady-State WOR Model

  8. fw Np Conventional WOR Analysis log(fw) versus Np

  9. fw = 1 Conventional WOR Analysis log(fw) versus Np

  10. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  11. Blasingame and Lee bpss m Pseudosteady-State WOR Model

  12. Pseudosteady-State WOR Model

  13. Pseudosteady-State WOR Model

  14. Pseudosteady-State WOR Model

  15. mw mo fw tw to bppsw bppso Pseudosteady-State WOR Model log(fw) versus Np

  16. fw tw to Pseudosteady-State WOR Model log(fw) versus Np

  17. Pseudosteady-State WOR Model Results from the PSS WOR modelversus the field production data

  18. Pseudosteady-State WOR Model log(fw) versus Np

  19. Pseudosteady-State WOR Model Results from the PSS WOR modelversus the field production data

  20. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  21. Analysis of WOR Data Estimation of Movable Oil • Conventional techniques • log(qo) versusproduction time, t • qo versus cumulative oil production, Np • fo versus cumulative oil production, Np • log(fw) versus cumulative oil production, Np • Ershagi's X-function • New techniques • 1/fw versus cumulative oil production, Np • 1/qo versus oil material balance time, to

  22. Analysis of WOR Data Qualitative Analysis • log(fwc) versus cumulative oil production, Np • log(WORc) versus cumulative oil production, Np • log(WOR) versus total production, (Np+Wp) • log(fo) versus total material balance time, tt • WOR and WOR associated functions versus time, t (to)

  23. Analysis of WOR Data Estimation of Movable Oil • Conventional techniques • log(qo) versusproduction time, t • qo versus cumulative oil production, Np • fo versus cumulative oil production, Np • log(fw) versus cumulative oil production, Np • Ershagi's X-function • New techniques • 1/fw versus cumulative oil production, Np • 1/qo versus oil material balance time, to

  24. Analysis of WOR Data log(qo) and log(qw) versus t

  25. qo=0 Analysis of WOR Data qo versus Np

  26. fo=0 Analysis of WOR Data fo versus Np

  27. fw = 1 Analysis of WOR Data log(fw ) versus Np

  28. X-function= -5.6 @ fw = 0.99 Analysis of WOR Data Ershagi’s X-plot Np=145,000 STB X = ln((1/fw)-1)-1/fw

  29. Analysis of WOR Data Estimation of Movable Oil • Conventional techniques • log(qo) versusproduction time, t • qo versus cumulative oil production, Np • fo versus cumulative oil production, Np • log(fw) versus cumulative oil production, Np • Ershagi's X-function • New techniques • 1/fw versus cumulative oil production, Np • 1/qo versus oil material balance time, to

  30. 1/fw=1 Analysis of WOR Data 1/fw versus Np

  31. 1/qo Np /qo Analysis of WOR Data 1/qo versus Np/qo

  32. Analysis of WOR Data Reciprocal of qo versus oil material balance time

  33. b Analysis of WOR Data 1/qo versus Np/qo

  34. Analysis of WOR Data 1/qo versus Np/qo Np,mov = 164,500 STB

  35. Analysis of WOR Data fwc versus Np Np,mov = 164,500 STB

  36. Analysis of WOR Data Comparison of the estimated Np values

  37. Analysis of WOR Data Qualitative Analysis • log(fwc) versus cumulative oil production, Np • log(WORc) versus cumulative oil production, Np • log(WOR) versus total production, (Np+Wp) • log(fo) versus total material balance time, tt • WOR and WOR associated functions versus time, t (to)

  38. Analysis of WOR Data WORversus (Np+Wp)

  39. Analysis of WOR Data fo versus (Np+Wp)/(qo+qw)

  40. Analysis of WOR Data WOR and WOR' versus (Np/qo)

  41. Analysis of WOR Data WOR integral andintegral-derivative versus (Np/qo)

  42. Outline • Introduction • Conventional WOR Analysis (Steady-State WOR Model) • Pseudosteady-State WOR Model • Analysis of WOR • Conclusions and Recommendations

  43. Conclusions Pseudosteady-state WOR model • We have developed a new pss WOR model for boundary-dominated reservoir behavior. • The proposed pss WOR model provides the best representation of the oil and water production data for the cases that we in-vestigated. • The only significant limitation of the our model is that it does not provide a mechan-ism for the prediction of future production

  44. Conclusions (cont.) Estimation of Movable Oil • We provide a compilation of the "conven-tional" straight-line extrapolation methods. These techniques should be applied simultaneously in order to obtain consis-tent estimates of movable oil. • We proposed two new methods for estimating movable oil reserves: • 1/fw versus Np • 1/qo versus Np/qo

  45. Conclusions (cont.) Estimation of Movable Oil • The results obtained by these new methods correspond quite well to the results obtained "conventional" WOR techniques. Analysis of Oil and Water Production Data • We note a straight-line behavior for the fwc and WORc functions plotted versus Np. However, the extrapolation of these straight-line trends does not lead to similar result for movable oil as the "conventional" extrapolation techniques.

  46. Conclusions (cont.) Analysis of Oil and Water Production Data • We have extended the diagnostic plots proposed by Chan. The following obser-vations are noted: • unit slope of the WOR and WOR integral and integral-derivative functions when plotted versus t, to, tt. • the WOR' function is typically very erratic and can not be used for routine analysis due to poor overall behavior.

  47. Conclusions (cont.) Analysis of Oil and Water Production Data • We believe that the X-plot method provides no substantive advantage over the "conventional" extrapolation techniques. The extrapolation of the X-function tends to significantly overestimate the value of movable oil.

  48. Recommendations • Investigate the possibility of using the proposed pss WOR model for the estimation of movable oil. • Examine a possibility to develop an analysis scheme to estimate pss parameters (bpsso, bpssw, mo, and mw). We suggest that the para-meters can be further used for reservoir analysis. • We suggest further qualitative and quantitative analysis for the various WOR trends as a function of time, cumulative production, material balance time. A”type curve" approach may be possible.

  49. The Analysis and Interpretation of Water-Oil Ratio Performance in Petroleum Reservoirs Valentina Bondar Texas A&M University Harold Vance Department of Petroleum Engineering 12 January 2001

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