1. Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations — Montney Shale Case Histories Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Status Presentation College Station, TX (USA) — 12 August 2012 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 1/38 (Alt.) Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties

2. ● Objectives ● Introduction ● Rate-Time Models: — PLE Model — Logistic Growth Model (LGM) — Duong Model ● Models performance analysis ● Modified rate decline models ● A Parametric correlation study ● Methodology: — Analysis of time-rate model parameters — Correlation of time-rate model parameters with reservoir/well parameters — Development of Parametric Correlations ● Conclusions and Recommendations Outline: Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 2/38

3. ■ Ilk et al., (2011) have demonstrated that rate-time parameters can be correlated with reservoir/well parameters using limited well data from unconventional reservoirs. ■ Theoretical verification and analysis of large number of high quality field data is necessary to test and verify the parametric correlations that correlate reservoir/well parameters with time-rate model parameters. ■ This study will provide the opportunity to investigate performance of modern time-rate models in matching and forecasting rate-time data from unconventional reservoirs. The models considered are: — PLE Model — Logistic Growth Model (LGM) — Duong Model Objectives/ Problem Statement: Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 3/38

4. ■ Modern time-rate models (PLE) have been shown to provide accurate EUR estimates and forecast future production when bottomhole flowing pressure (p wf ) is constant. ■ Time-Rate model Constraints: — Constant Bottomhole Pressure (p wf ) — Constant Completion Parameters (Well lateral length, x f....) ■ Time-Rate model parameters can be correlated with reservoir/well parameters (k, kx f, EUR) ■ A diagnostic Approach — Diagnostic Plots — Data Driven matching process Introduction Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 4/38 'qdb' type diagnostic plot— discussed below

5. ● Time-Rate Analysis: Base Definitions ■ Based on the "Loss Ratio" concept (Arps, 1945). ■ Loss Ratio: ■ Loss Ratio Derivative: ● Approach ■ Continuous evaluation of D(t) and b(t) relations provide a diagnostic method for matching time-rate data. ■ Diagnostic relations are used to derive empirical models. Governing Relations: Time-Rate Definitions Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 5/38

6. ● History: ■ SPE 116731 (Ilk et al., 2008) ■ Derived from data (D(t) and b(t)) ■ Analogous to Stretched-Exponential, but derived independently ■ Has a terminal term for boundary-dominated flow (D ∞ ) ● Governing Relations: ■ Rate-Time relation: ■ PLE Loss Ratio relation: ■ PLE Loss Ratio Derivative relation: Time-Rate Analysis: Power Law Exponential Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 6/38

7. ● History: ■ SPE 137748 (Duong, 2011) ■ Based on extended linear/bilinear flow regime ■ Derived from transient behavior of unconventional-fractured reservoirs ■ Relation extracted from straight line behavior of q/G p vs. Time (Log-Log) plot ● Governing Relations: ■ Duong Rate-Time relation: ■ Duong Loss Ratio relation: ■ Duong Loss Ratio Derivative relation: Time-Rate Analysis: Duong Model Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 7/38

8. ● History ■ SPE 144790 (Clark et al., 2011) ■ Adopted from population growth models ■ Modified form of hyperbolic logistic growth models ● Governing Relations: ■ LGM Cumulative and Rate-Time relation: ■ LGM Loss Ratio relation: ■ LGM Loss Ratio Derivative relation: Time-Rate Analysis: Logistic Growth Model (LGM) Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 8/38

9. ● PLE Model ■ Transient ■ Transitional and ■ boundary-dominated flow regimes. ● LGM Model ■ Transient and ■ Transitional flow regimes. ● Duong Model ■ Transient flow regimes. ● Well 1: k = 2000 nD Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 9/38 Theoretical Consideration: Time-rate analysis

10. ● Well 1: k = 2000 nD ● PLE ■ Excellent time-rate data match. ■ Accurate estimate of EUR is possible. ● LGM and Duong Models ■ Excellent match during Transient flow regimes. ■ Lack boundary conditions. ■ EUR is overestimated. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 10/38 Theoretical Consideration: Time-rate analysis

11. ● PLE, LGM and Duong Models. ■ All models match transient flow-regimes very well. ■ In the absence of boundary-dominated flow, all models provide reliable EUR estimate. ● Well 2: k = 50 nD Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 11/38 Theoretical Consideration: Time-rate analysis

12. ● Well 2: k = 50nD ● In the absence of boundary-dominated flow, PLE, LGM and Duong Models can: ■ match transient flow regimes very well and ■ provide good estimate of EUR. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 12/38 Theoretical Consideration: Time-rate analysis

13. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories ● Modified Time-Rate Relations Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 13/40

14. ● Modified Duong Model ■ With boundary parameter, D DNG ■ Boundary-dominated flow can be modeled. ■ Derivation is based on loss-ratio definition. The modified form of loss-ratio relation is given by: ■ It is derived by assuming constant loss-ratio during boundary- dominated flow regimes. ■ New time-rate relation can be derived from the loss-ratio relation. It is given by: ■ Cumulative production relation can not be derived. Numerical methods are necessary. Slide — 14/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model – (MODEL 1)

15. ● Modified Duong Model ■ The loss-ratio derivative is given by: ● Modified Duong Model ■ Boundary-dominated flows can be modeled. ■ EUR estimates are constrained. ■ Exponential decline characterizes boundary-dominated flow. Slide — 15/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories (Cont.) Modified Time-Rate Models: Duong Model - (MODEL 1)

16. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 16/38 Modified Duong Model: 'qdb' type diagnostic plot. (MODEL 1) ● Derived based on loss-ratio derivation of Duong Model. ● Modified Duong Model ■ Boundary-dominated flows can be modeled. ■ EUR estimates are constrained. ■ Exponential decline characterizes boundary- dominated flow. Added Constant

17. ● Modified Duong Model ■ With boundary parameter D DNG ■ Boundary-dominated flow can be modeled. ■ Based on q/G p Vs. time diagnostic plot. ■ New q/Gp model-relation: ■ New time-rate relation: ■ New Cumulative production relation: Slide — 17/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model - (MODEL 2)

18. ● Cont. (Model parameters) ■ The loss-ratio relation is given by: ■ The loss-ratio derivative is given by: Slide — 18/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model - (MODEL 2)

19. Slide — 19/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model – (MODEL 2) ● q/G p vs. Time — Diagnostic Plot ● On log-log plot of q/Gp vs. time: ■ Transient flow can be characterized by a power- law relation, and ■ Boundary-dominated flow can be characterized by an exponential decline relation. ■ q/Gp data can be matched with the following relation:

20. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 20/40

21. ● Duong Model Slide — 21/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model (Cont.) ● Modified Duong Model - (Model-2)

22. Slide — 22/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Duong Model: Diagnostic Plot (Cont.)- Montney Shale Wells vs. time Diagnostic Plot ● m – Duong parameter describes rock-types, stimulation practices and fracture properties.

23. Slide — 23/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model - (MODEL 2) ● Numerical Simulation Case, k=8µD. ● Model shows excellent data match for all flow regimes.

24. Slide — 24/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Models Comparison ● Model Comparison ■ Duong Model ■ Model 1 and ■ Model 2 ● Modified Duong Models provide a better match ● EUR is constrained.

25. Slide — 25/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Models Comparison Numerical Simulation Case (k = 8 µD) ● Model Comparison ■ Duong Model ■ Model 1 and ■ Model 2 ● Modified Duong Models provide excellent match to Transient, Transition and boundary-dominated flow regimes. ● Duong Model can also match observed early time Skin and production constraints.

26. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 26/38 ● Modified Logistic Growth Model ■ With boundary parameter D LGM ■ Boundary-dominated flow can be modeled. ■ Modified LGM time-rate relation: Assuming exponential decline during boundary dominated flow regimes. ■ Modified LGM Loss Ratio relation: ■ Modified LGM Loss Ratio derivative relation: Modified Time-Rate Models: Logistic Growth Model ( MODEL 3 )

27. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 27/38 Modified Logistic Growth Model: MODEL 3-qdb' type diagnostic plot. ● Modified Logistic Growth Model: ■ Boundary-dominated flows can be modeled accurately ■ EUR estimates are constrained. ■ Exponential decline characterizes boundary-dominated flow. ● Prior knowledge of gas in place (K) is required. ● Direct formulation of Gp is not possible. Numerical methods are necessary.

28. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 28/38 Modified Logistic Growth Model: (MODEL 4) ● Using Diagnostic plot of [K/Q g – 1] vs. t or t mb From LGM Model we have ● The last relation suggests that a log-log plot of [K/Q g t – 1] versus time shows a power-law relation for transient flow regimes. ● Now, we can suggest the following relation with modification for boundary dominated flow regimes. Where K = Initial Gas in Place. R = Remaining Gas Reserve at t ∞.

29. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 29/38 Modified Logistic Growth Model: (MODEL 4) ● Now, we can derive the associated modified relations. R = Remaining Gas Reserve at t ∞ ● Cumulative Production [G p (t)] relation can be derived for Model 4.

30. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 30/38 Modified Logistic Growth Model: Diagnostic Plot Corrected K/Q-1 Relation (MODEL 4) ● If K is known, we can estimate parameters a and n from the transient flow regime. ●.D LGM can be modified based on boundary behaviors. a= 161 n= 0.79 K= 20,219,576.75 D lgm = 0.00029 R= 0.157

31. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 31/38 Modified Logistic Growth Model: Comparison ● Modified LGM models can match transient and boundary-dominated flow regimes better than LGM model. ● EUR is constrained. ● MODEL 4 provides a better match. ● Gp relation can be derived for MODEL 4. ● Prior knowledge of gas in place (K) is required. (MODEL and MODEL 4)

32. ■ A horizontal well with multiple transverse fractures is modeled. Reservoir Properties: Net pay thickness, h=39.624 m Formation permeability, k=0.25 µD - 5µD Wellbore Radius, r w =0.10668 m Formation compressibility, c f =4.35E-7 kPa -1 Porosity, =0.09 (fraction) Initial reservoir pressure, p i =34,473.8 kPa Gas saturation, s g =1.0 (fraction) Skin factor, s=0.01 (dimensionless) Reservoir Temperature, T r =100 °C Fluid Properties: Gas specific gravity, γ g =0.6 (air=1) Hydraulically Fractured Well Model Parameters: Fracture half-length, x f =50 m Number of Fractures=20 Horizontal well length, l=1,500 m Production parameters: Flowing pressure, p wf =3447.4 kPa Producing time, t=10,598 days (30 Years) Theoretical Consideration: Synthetic Case Examples ■ The model inputs are as follows: Transverse Fractures Horizontal well with multiple transverse fractures Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 32/38 ● Synthetic Examples ■ 14 Models with permeability (k) ranging from 0.25 µD - 5µD. ■ All other reservoir/well and fluid parameters are identical.

33. ● PLE model parameters are related to EUR estimates from PDA Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 33/38 Parameter Analysis: PLE Time-Rate Model

34. ● PLE model parameters are related to permeability Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 34/38 Parameter Analysis: PLE Time-Rate Model

35. ■ A parametric correlation that relates reservoir permeability with rate- time model parameters can be produced. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 35/38 Parameter Correlation: Permeability

36. ■ A parametric correlation that relates EUR estimates with rate-time model parameters can be produced. ■ The parametric correlation may not be unique. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 36/38 Parameter Correlation: EUR

37. ● Field data example: Montney Shale, (Brassey) Wells ■ Careful analysis of pressure/production data is necessary to accurately estimate reservoir/well parameters (k, EUR, xf). ■ Decline curve analysis is then carried out to estimate EUR. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 37/38 Field Data Example: Permeability

38. ■ EUR is normalized by initial BHP (P i ), and number of effective fractures. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 38/38 Field Data Example: EUR ● Field data example: Montney Shale, (Brassey) Wells

39. ■ It is possible to integrate time-rate model parameters with reservoir/well parameters using parametric correlations. ■ Parametric correlations solve the uncertainty regarding the number of unknown parameters in model based production data analysis. ■ Modern rate decline models are successful at modeling different flow regimes observed from unconventional reservoirs. In summary: — PLE Model › Transient, transition, and, boundary-dominated flow regimes are successfully modeled. — Logistic Growth Model (LGM) › Transient, and transition flow regimes are successfully modeled. — Duong Model › Only transient flow regimes are matched. › EUR is overestimated. › Doesn’t conform to ‘qdb’ type diagnostic plot. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 39/38 Conclusion:

40. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Extra Slides Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 40/38

41. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 41/38

42. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 42/38

43. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 43/38

44. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 44/38 Summary: Model Comparison Rate Decline Models Model RelationsDiagnostic PlotsRecommendation Power-law exponential ●Use diagnostic relation Duong Model●Use diagnostic relation ●Do not match boundary flow regimes. Modified Duong Model Logistic Growth Model (LGM) Modified LGM

45. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 45/38 Modified Logistic Growth Model: Corrected K/Q-1 Relation ● LGM K (Carrying capacity) is equivalent to Gas in Place volumetric estimate. ● Gas in place estimate should be available to use this model ● K/Q(t)-1 vs. t mb diagnostic plot can be used.

46. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 46/40 PLE Duong LGM Modified LGM MODEL 2 Modified LGM MODEL 1 Modified Duong MODEL 2 Modified Duong MODEL 1

47. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 47/40 PLE Duong LGM Modified Duong MODEL 2 Modified Duong MODEL 1 Modified LGM MODEL 1

48. Slide — 48/38 Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Modified Time-Rate Models: Duong Model - (MODEL 2)

49. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 49/40 Power Law Exponential (PLE) Model Loss Ratio Relation Basis for PLE Model Rate-Time Relation Loss-Ratio Derivative

50. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 50/40 Duong Time-Rate Relation q/G p vs. Production Time Log-Log Plot Basis for Duong Model Rate-Time Relation Cumulative-Time Relation Loss-Ratio Loss-Ratio Derivative

51. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 51/40 Logistic Growth Model (LGM) K/Q(t) -1 vs. Production Time Log-Log Plot Basis for LGM Rate-Time Relation Cumulative-Time Relation Loss-Ratio Loss-Ratio Derivative

52. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 52/40 Modified Duong Model (Model 1) Loss Ratio Relation Basis for Modified Duong Model Rate-Time Relation Loss-Ratio Derivative

53. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 53/40 Modified Duong Model (Model 2) q/G p vs. Production Time Log-Log Plot Basis for Modified Duong Model (Model 2) Rate-Time Relation Cumulative-Time Relation Loss-Ratio Loss-Ratio Derivative

54. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 54/40 Modified Logistic Growth Model (Model 1) Loss Ratio Relation Basis for Modified Logistic Growth Model (Model 1) Rate-Time Relation Loss-Ratio Derivative

55. Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Status Presentation — Yohanes ASKABE — Texas A&M University College Station, TX (USA) — 12 August 2012 Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations Montney Shale Case Histories Slide — 55/40 Modified Duong Model (Model 2) Basis for Modified LGM (Model 2) Rate-Time Relation Cumulative-Time Relation K/Q(t) -1 vs. Production Time Log-Log Plot Loss-Ratio Loss-Ratio Derivative