1. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 1/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties

2. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 2/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Introduction

3. ● Overview ● Performance Analysis of Time-Rate Models ■ Power-Law Exponential Model (PLE) ■ Duong Model ■ Logistic Growth Model (LGM) ● Rationale ■ Diagnostic Functions for Time-Rate Data Analysis ■ "Continuous EUR" Analysis to Estimate Reserves ● Development of New Time-Rate Relations ■ D-Parameter Modification ■ q/G p versus Time – Diagnostic Plot Analysis ■ K/G p -1 versus Time – Diagnostic Plot Analysis ● Rationale ■ Duong Model: D-parameter Modification (MDNG – 1) ■ LGM Model: D-parameter Modification (MLNG – 1) ■ Duong Model: q/G p Data Analysis (MDNG – 2) ■ LGM Model: K/G p -1 Modification (MLNG – 2) Presentation Outline Slide — 3/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Outline) ●○

4. ● Preliminary study: Integration Of Production Data Analysis And Time-rate Analysis Via Parametric Correlations – Theoretical Consideration ● Rationale ■ Time-Rate Data Analysis of Numerical Simulation Cases ■ Cross-Plot Analysis of Time-Rate Model Parameters and Reservoir Properties (Fracture Conductivity (Fc) and EUR) ■ Development of Parametric Correlation ● Summary, Conclusions and Recommendations Presentation Outline Slide — 4/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Outline) ●●

5. ● Definitions ■ D-parameter: Inverse of "loss-ratio" relation. Definition of "loss-ratio" ■ b-parameter: Derivative of "loss-ratio". ■ b -derivative: Constant pressure production: Diagnostic Functions (Background): Slide — 5/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Background) ●○○○○ Johnson, R.H. and Bollens, A.L. 1927. The Loss Ratio Method of Extrapolating Oil Well Decline Curves. Trans. AIME: 160, 228-247.] Arps J.J. 1945. Analysis of Decline Curves. Trans. AIME: 160, 228- 247.] Ilk, D., Currie, S.M., Symmons, D. et al. 2010. Hybrid Rate-Decline Models for the Analysis of Production Performance in Unconventional Reservoirs. Paper presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy. Society of Petroleum Engineers 135616.

6. ● History: ■ SPE 116731 (Ilk et al., 2008) ■ Derived from data D-parameter ■ Analogous to Stretched-Exponential, but derived independently ■ Has a terminal term for boundary-dominated flow (D ∞ ) ● Governing Relations: Time-Rate Relations (Background): Power-Law Exponential Model (PLE) Slide — 6/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ■ Rate-Time Relation: ■ D-parameter ■ b-parameter ■ b-derivative: (Background) ●●○○○

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: Time-Rate Relations (Background): Duong Model Slide — 7/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Background) ●●●○○ ■ Rate-Time Relation: ■ D-parameter ■ b-parameter ■ b-derivative: ■ Base Relation:

8. ● History ■ SPE 144790 (Clark et al., 2011) ■ Adopted from population growth models ■ Modified form of hyperbolic logistic growth models ● Governing Relations: Time-Rate Relations (Background): Logistic Growth Model (LGM) Slide — 8/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Background) ●●●●○ ■ Rate-Time Relation: ■ D-parameter ■ b-parameter ■ b-derivative: ■ Cumulative Production Relation: K = Carrying Capacity

9. ● History ■ SPE 132352 (Currie et al., 2010) ■ Continuous Estimation of EUR with Time ■ To Reduce Uncertainty in Reserve Estimation ■ Time-Rate Relations EUR Estimates are Compared ■ Rate of convergence is compared "Continuous EUR" Analysis (Background): Slide — 9/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Background) ●●●●● Cumulative Production versus Production Time

10. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 10/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Performance Analysis of Time-Rate Relations

11. ● Performance Study ■ PLE model ■ Duong model ■ Logistic Growth Model (LGM) ● A diagnostic Approach ■ Diagnostic Plots ■ Data Driven matching process Introduction Slide — 11/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ● Approach ■ Continuous evaluation of D(t), b(t), and b(t) relations provide a diagnostic method for matching time-rate data.

12. ● 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. ● Numerical Simulation Case: k = 50 nD Slide — 12/80 Theoretical Consideration: Time-rate analysis Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties

13. Slide — 13/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ● General: ■ 50 nd case. ■ All models shown. ● PLE Model: ■ Very good match. ■ Indistinguishable. ● Doung Model: ■ Very good match. ■ Indistinguishable. ● LGM Model: ■ Very good match. ■ Indistinguishable. 50 nd Simulation Case Time-Rate Analysis: PLE, Duong, and LGM Models

14. ● PLE Model ■ Transient ■ Transitional and ■ boundary-dominated flow regimes. ● LGM Model ■ Transient and ■ Transitional flow regimes. ● Duong Model ■ Transient flow regimes. ● Numerical Simulation Case: k = 2,000 nD Slide — 14/80 Theoretical Consideration: Time-rate analysis Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties

15. Slide — 15/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ● General: ■ 2,000 nd case. ■ G p,30yr = 14.2 BSCF ■ All models shown. ● PLE Model: ■ Very good match. ■ Matches BDF regime. ■ G p,30yr = 14.7 BSCF (3.5%) ● Doung Model: ■ Very good match. ■ Does NOT match BDF. ■ G p,30yr = 19.5 BSCF (37.3%) ● LGM Model: ■ Very good match. ■ Fair match of BDF. ■ G p,30yr = 17.5 BSCF (23.2%) 2000 nd Simulation Case Time-Rate Analysis: PLE, Duong, and LGM Models

16. Slide — 16/80 "Continuous EUR" Analysis: ● EUR is estimated as a function of time. ● Continuous EUR approach: ■ To compare performance of PLE, LGM and Duong time-rate relations at estimating reserves. ■ To compare rate of convergence of EUR. ● 30 year reserve estimates are determined. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties

17. Slide — 17/80 Continuous EUR Analysis: Power-Law Exponential Model Numerical Simulation Case – Numerical Simulation Case ● EUR is estimated every 60 days. ● D ∞ -parameter used once boundary- dominated flow regime is attained. ● PLE model results in quality match at all intervals. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Numerical Simulation Case ) ●○○○

18. Slide — 18/80 Continuous EUR Analysis: Duong Model Numerical Simulation Case – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Numerical Simulation Case ) ●●○○ ● EUR is estimated every 60 days. ● Lacks boundary behavior. ● Good match in early transient periods. ● Data match is forced.

19. Slide — 19/80 Continuous EUR Analysis: Logistic Growth Model Numerical Simulation Case – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Numerical Simulation Case ) ●●●○ ● EUR is estimated every 60 days. ● Excellent match during transient and transition flow periods. ● Lacks boundary characteristics. ● Data match is forced.

20. Slide — 20/80 Continuous EUR Analysis: Numerical Simulation Case – 30 year EUR estimate Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ● EUR is estimated every 60 days. ● PLE model EUR values have converged to a reliable reserve estimate. ● Duong and LGM models have converged at a higher reserve estimate. ● LGM and PLE converge faster. (Numerical Simulation Case ) ●●●● ● Model 30 year reserve estimate: ■ PLE= 14.20 BSCF ■ Duong model= 16.1 BSCF ■ LGM model= 15.6 BSCF

21. ● EUR is estimated every 60 days. ● Very good match during transient and transition flow periods. ● Boundary-dominated flow regime is not observed. ● D ∞ -parameter is not used. Slide — 21/80 Continuous EUR Analysis: Power-Law Exponential Model East Texas Gas Well Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well) ●○○○

22. Slide — 22/80 Continuous EUR Analysis: Duong Model East Texas Tight Gas Well Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well) ●●○○ ● EUR is estimated every 60 days. ● Very good match during transient and transition flow periods. ● Boundary-dominated flow regime is not observed.

23. Slide — 23/80 Continuous EUR Analysis: Logistic Growth Model East Texas Tight Gas Well Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well) ●●●○ ● EUR is estimated every 60 days. ● Very good match during transient and transition flow periods. ● Boundary-dominated flow regime is not observed.

24. Slide — 24/80 Continuous EUR Analysis: East Texas Tight Gas Well – 30 year EUR Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well) ●●●● ● EUR is estimated every 60 days. ● PLE model has converged faster ● All models result in a similar reserve estimates. ● Model reserve esimates are similar when boundary- dominated flow regime is not observed. ● Model 30 year reserve estimate: ■ PLE= 2.98 BSCF ■ Duong model= 3.15 BSCF ■ LGM model= 2.95 BSCF

25. Slide — 25/80 Continuous EUR Analysis: Power-Law Exponential Model East Texas Tight Gas Well – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well – Num. Sim) ●○○○ ● EUR is estimated every 60 days. ● Very good match during transient, transition and boundary-dominated flow periods. ● Boundary-dominated flow regime is observed. ● D ∞ -parameter used.

26. Slide — 26/80 Continuous EUR Analysis: Duong Model East Texas Tight Gas Well – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well – Num. Sim) ●●○○ ● EUR is estimated every 60 days. ● Very good match during early transient flow periods. ● Poor match quality during boundary flow periods. ● Model match is forced.

27. Slide — 27/80 Continuous EUR Analysis: Logistic Growth Model East Texas Tight Gas Well – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well – Num. Sim) ●●●○ ● EUR is estimated every 60 days. ● Very good match during transient and transition flow periods. ● Poor match quality during boundary flow periods. ● Model match is forced.

28. Slide — 28/80 Continuous EUR Analysis: East Texas Tight Gas Well – Numerical Simulation Case Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Tx Tight Gas Well – Num. Sim) ●●●● ● EUR is estimated every 60 days. ● Simulation data is projected to 30 years. ● PLE model has converged faster. ● PLE model D ∞ -parameter provides a hard constraint. ● Duong and LGM model overestimate reserves and converge slowly. ● Time-rate models 30 year reserve estimate: ■ PLE= 2.32 BSCF ■ Duong model= 3.30 BSCF ■ LGM model= 2.61 BSCF

29. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 29/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties New Time-Rate Relations

30. ● Modified Duong Model ■ With boundary parameter, D DNG ■ Boundary-dominated flow can be modeled. ■ Derivation is based on loss-ratio definition (similar to PLE). The modified form of D-parameter 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 — 30/80 Modified Time-Rate Models: Modified Duong Model – 1 (MDNG 1) Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MDNG – 1) ●○○

31. ● MDNG – 1 ■ The b-parameter is given by: ● MDNG – 1 ■ Boundary-dominated flows can be modeled EUR estimates are constrained. ■ Exponential decline characterizes boundary-dominated flow. Slide — 31/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Modified Time-Rate Models: Modified Duong Model – 1 (MDNG – 1) (New-Relations – MDNG – 1) ●●○

32. ● 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. Slide — 32/80 Added Constant Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MDNG – 1) ●●● Modified Time-Rate Models: Modified Duong Model – 1 (MDNG 1)

33. ● MDNG – 2 ■ 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 — 33/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MDNG – 2) ●○○○ Modified Time-Rate Models: Modified Duong Model – 2 (MDNG – 2)

34. Slide — 34/80 ● 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: Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Modified Time-Rate Models: Modified Duong Model – 2 (MDNG 2) (New-Relations – MDNG – 2) ●●○○

35. ● Modified Duong Model – 2 (MDNG – 2) ■ The D-parameter is given by: ■ The b-parameter is given by: Slide — 35/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Modified Time-Rate Models: Modified Duong Model – 2 (MDNG 2) (New-Relations – MDNG – 2) ●●●○

36. Slide — 36/80 ● Model schematics ■ Duong Model ■ MDNG – 1 ■ MDNG – 2 ● New relations show boundary characteristics to model boundary- dominated flow regimes. ● EUR is constrained. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Modified Time-Rate Models: Duong and Modified Duong Models ( ) (New-Relations) ●●●●

37. Slide — 37/80 ● Modified Logistic Growth Model ■ With boundary parameter D LGM ■ Boundary-dominated flow can be modeled. ■ Derivation is based on loss-ratio definition. The modified form of D-parameter is given by: ■ 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. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MLGM – 1) ●○○○ Modified Time-Rate Models: Modified LGM Model – 1 (MLGM – 1)

38. Slide — 38/80 ● MLGM – 1 ■ The b-parameter is given by: ● MLGM – 1 ■ Boundary-dominated flows can be modeled. ■ EUR estimates are constrained. ■ Exponential decline characterizes boundary-dominated flow. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MLGM – 1) ●●○○ Modified Time-Rate Models: Modified LGM Model – 1 (MLGM – 1)

39. Slide — 39/80 ● 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. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MLGM – 1) ●●●○ Modified Time-Rate Models: Modified LGM Model – 1 (MLGM – 1)

40. Modified Time-Rate Models: Modified LGM Model – 2 (MLGM – 2) Slide — 40/80 ● Using Diagnostic plot of [K/Q g – 1] vs. time From LGM Model we have ● The last relation suggests that a log-log plot of K/Q g – 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 ∞. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MLGM – 2) ●○○○

41. Slide — 41/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ● 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 Modified Logistic Growth Model: Diagnostic Plot Corrected K/Q-1 Relation (MODEL 4)

42. Slide — 42/80 ● Now, we can derive the associated modified relations. R = Remaining Gas Reserve at t ∞ ● Cumulative Production [G p (t)] relation can be derived for MLGM – 2. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (New-Relations – MLGM – 2) ●●●○ Modified Time-Rate Models: Modified LGM Model – 2 (MLGM – 2)

43. Slide — 43/80 ● Model schematics ■ LGM ■ MLGM – 1 ■ MLGM – 2 ● New relations show boundary characteristics to model boundary- dominated flow regimes. ● EUR is constrained. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Modified Time-Rate Models: LGM and Modified LGM Models ( ) (New-Relations) ●●●●

44. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 44/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties New Time-Rate Relations Validation

45. Slide — 45/80 ■ Numerical Simulation Model – East Texas Tight Gas Well ● Hydraulically fractured vertical well in a tight gas reservoir. ● About 6 years of production data is available. ● Boundary-dominated flow regime is established. ● k = 0.005 md ● G p,max = 1.82 BSCF Numerical Simulation Case: East Texas Tight Gas Well – MDNG – 2 Model Analysis Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Texas Tight Gas Well) ●○○○○

46. Slide — 46/80 ● MDNG-2 model parameters are estimated on a log-log plot of q/G p versus time plot. ● q t1 is estimated from the rate data at t = t 1. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (East Texas Tight Gas Well) ●●○○○ ■ q/G p versus time Plot Analysis Numerical Simulation Case: East Texas Tight Gas Well – MDNG – 2 Model Analysis

47. Slide — 47/80 ● MLGM-2 model parameters are estimated on a log-log plot of K/Q(t)-1 versus time plot. ● A prior estimate of initial gas in place (K) is required ● Gas in Place (K) = 2.65 BSCF (numerical simulation input) Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ■ K/Q(t)-1 versus time Plot Analysis Numerical Simulation Case: East Texas Tight Gas Well – MLGM – 2 Model Analysis (East Texas Tight Gas Well) ●●●○○

48. Slide — 48/80 ● Model Match on q, D-,b- parameter and b-derivative versus time Log-Log plot ● All models match transient flow regimes. ● PLE and the new models match the transient and the boundary-dominated flow regimes. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Numerical Simulation Case: East Texas Tight Gas Well – PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 1 models (East Texas Tight Gas Well) ●●●●○

49. Slide — 49/80 ● PLE and New time-rate relations result in excellent match to the boundary conditions. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Numerical Simulation Case: East Texas Tight Gas Well – PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 2 models (East Texas Tight Gas Well) ●●●●● Numerical Simulation Case Gp,max = 1.82 BSCF ■Duong Model= 2.37 BSCF ■LGM= 1.95 BSCF ■PLE= 1.84 BSCF ■MDNG – 1 = 1.80 BSCF ■MDNG – 2 = 1.79 BSCF ■MLGM – 1 = 1.77 BSCF ■MLGM – 2 = 1.78 BSCF

50. Slide — 50/80 ■ Numerical Simulation Model – Barnet Shale Gas Well ● Hydraulically fractured well in a shale gas reservoir. ● About 10 years of production data is available. ● Boundary-dominated flow regime is established. ● G p,max = 0.84 BSCF Field Case: Barnet Shale Gas Well Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Barnet Shale Gas Well) ●○○○○

51. Slide — 51/80 ● MDNG-2 model parameters are estimated on a log-log plot of q/G p versus time plot. ● q t1 is estimated from the rate data at t = t 1. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Barnet Shale Gas Well) ●●○○○ ■ q/G p versus time Plot Analysis Field Case: Barnet Shale Gas Well – MDNG – 2 Model Analysis

52. Slide — 52/80 ● MLGM-2 model parameters are estimated on a log-log plot of K/Q(t)-1 versus time plot. ● A prior estimate of initial gas in place (K) is required ● Gas in Place (K) (estimate)= 1.31 BSCF Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ■ K/Q(t)-1 versus time Plot Analysis Field Case: Barnet Shale Gas Well – MLGM – 2 Model Analysis (Barnet Shale Gas Well) ●●●○○

53. Slide — 53/80 ● Model Match on q, D-,b- parameter and b-derivative versus time Log-Log plot ● All models match transient flow regimes. ● PLE and the new models match the transient and the boundary-dominated flow regimes. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Field Case: Barnet Shale Gas Well – PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 1 models (Barnet Shale Gas Well) ●●●●○

54. Slide — 54/80 ● PLE and New time-rate relations result in excellent match to the boundary conditions. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Field Case: Barnet Shale Gas Well– PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 2 models (Barnet Shale Gas Well) ●●●●● Barnet Shale Gas Well Gp,max = 0.84 BSCF ■Duong Model= 1.20 BSCF ■LGM= 1.10 BSCF ■PLE= 0.79 BSCF ■MDNG – 1 = 0.78 BSCF ■MDNG – 2 = 0.82 BSCF ■MLGM – 1 = 0.80 BSCF ■MLGM – 2 = 0.78 BSCF

55. Slide — 55/80 ■ Numerical Simulation Model – Mexico Tight Gas Well ● Hydraulically fractured vertical well in a tight gas reservoir. ● k < 0.001 md ● About 40 years of production data is available. ● Boundary-dominated flow regime is established. ● G p,max = 13.52 BSCF Field Case: Mexico Tight Gas Well Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Mexico Tight Gas Well) ●○○○○

56. Slide — 56/80 ● MDNG-2 model parameters are estimated on a log-log plot of q/G p versus time plot. ● q t1 is estimated from the rate data at t = t 1. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties (Mexico Tight Gas Well) ●●○○○ ■ q/G p versus time Plot Analysis Field Case: Mexico Tight Gas Well – MDNG – 2 Model Analysis

57. Slide — 57/80 ● MLGM-2 model parameters are estimated on a log-log plot of K/Q(t)-1 versus time plot. ● A prior estimate of initial gas in place (K) is required ● Gas in Place (K) =22.96 BSCF Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties ■ K/Q(t)-1 versus time Plot Analysis Field Case: Mexico Tight Gas Well – MLGM – 2 Model Analysis (Mexico Tight Gas Well) ●●●○○

58. Slide — 58/80 ● Model Match on q, D-,b- parameter and b-derivative versus time Log-Log plot ● All models match transient flow regimes. ● PLE and the new models match the transient and the boundary-dominated flow regimes. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Field Case: Mexico Tight Gas Well – PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 1 models (Mexico Tight Gas Well) ●●●●○

59. Slide — 59/80 ● PLE and New time-rate relations result in excellent match to the boundary conditions. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Field Case: Mexico Tight Gas Well– PLE, Duong, LGM, MDNG – 1, MDNG – 2, MLGM – 1, and MLGM – 2 models (Mexico Tight Gas Well) ●●●●● Mexico Tight Gas Well Gp,max = 13.52 BSCF ■Duong Model= 14.84 BSCF ■LGM= 15.22 BSCF ■PLE= 13.70 BSCF ■MDNG – 1 = 13.50 BSCF ■MDNG – 2 = 13.80 BSCF ■MLGM – 1 = 13.72 BSCF ■MLGM – 2 = 13.83 BSCF

60. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 60/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Development of Parametric Correlation

61. ■ 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 Consideration (Preliminary Study) is presented using production data generated from unconventional reservoirs. Objectives/ Problem Statement: Slide — 61/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Ilk, D., Rushing, J.A., and Blasingame, T.A. 2011. Integration of Production Analysis and Rate-Time Analysis Via Parametric Correlations -- Theoretical Considerations and Practical Applications. Paper presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA. 180556.

62. Reservoir Properties Net pay thickness, h=160 ft Formation permeability, k=0.5 µD Fracture conductivity=0.005 – 0.7 md-ft Wellbore Radius, r w =0.1 ft Formation compressibility, c f =3 x 10 -6 psi -1 Porosity, f=0.05 (fraction) Initial reservoir pressure, p i =5000 psi Gas saturation, s g =1.0 fraction Skin factor, s=-5 (dimensionless) Reservoir temperature, T r =212 °F Fluid properties: Gas specific gravity, γ g =0.7 (air = 1) Hydraulically fractured well model parameters: Fracture half-length, x f =164.0 ft Number of fractures=15 Horizontal well length=6561.7 ft Production parameters: Flowing pressure, p wf =500 psia Production time, t=10,958 days (30 years) ■ A horizontal well with multiple transverse fractures is modeled. ■ The model inputs are as follows: Transverse Fractures Horizontal well with multiple transverse fractures Slide — 62/80 ● Synthetic Examples ■ 15 Models with F c ranging from 0.005- 0.7 md-ft. ■ All other reservoir/well and fluid parameters are identical. Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Synthetic Case Example ● Time-rate models ■ PLE model ■ Duong model ■ LGM model

63. Slide — 63/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: PLE model analysis (PLE model Analysis) ●○○○○ β-parameter b-parameter Rate

64. Slide — 64/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: PLE model analysis Cross-Plot Analysis – Fracture Conductivity (F c ) (PLE model Analysis) ●●○○○ F c vs. n ● Discussion: ■ Fracture conductivity is correlated with PLE model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

65. Slide — 65/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: PLE model analysis Cross-Plot Analysis – 30 year EUR (EUR 30yr ) (PLE model Analysis) ●●●○○ EUR 30yr vs. n ● Discussion: ■ EUR is correlated with PLE model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

66. Slide — 66/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: PLE model analysis F c and EUR 30yr Correlation (PLE model Analysis) ●●●●○ Calculated F c vs. Model F c ● Discussion: ■ EUR 30yr and Fc can be estimated using PLE model parameters: Calculated EUR 30yr vs. Model EUR 30yr

67. Slide — 67/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Duong model analysis (Duong model Analysis) ●○○○○ β-parameter b-parameter Rate

68. Slide — 68/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Duong model analysis Cross-Plot Analysis – Fracture Conductivity (F c ) (Duong model Analysis) ●●○○○ F c vs. m ● Discussion: ■ Fracture conductivity is correlated with Duong model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

69. Slide — 69/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Duong model analysis Cross-Plot Analysis – 30 year EUR (EUR 30yr ) (Duong model Analysis) ●●●○○ EUR 30yr vs. m ● Discussion: ■ EUR is correlated with Duong model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

70. Slide — 70/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Duong model analysis F c and EUR 30yr Correlation (Duong model Analysis) ●●●●○ Calculated F c vs. Model F c ● Discussion: ■ EUR 30yr and Fc can be estimated using Duong model parameters: Calculated EUR 30yr vs. Model EUR 30yr

71. Slide — 71/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: LGM model analysis (LGM model Analysis) ●○○○○ β-parameter b-parameter Rate

72. Slide — 72/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: LGM model analysis Cross-Plot Analysis – Fracture Conductivity (F c ) (LGM model Analysis) ●●○○○ ● Discussion: ■ Fracture conductivity is correlated with Lgm model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

73. Slide — 73/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: Duong model analysis Cross-Plot Analysis – 30 year EUR (EUR 30yr ) (LGM model Analysis) ●●●○○ ● Discussion: ■ EUR is correlated with LGM model parameters for: ■ Constant (slowly changing) bottomhole pressure. ■ Constant (slowly changing) completion parameters (well length, number of fractures…).

74. Slide — 74/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Theoretical Consideration: LGM model analysis F c and EUR 30yr Correlation (LGM model Analysis) ●●●●○ Calculated F c vs. Model F c ● Discussion: ■ EUR 30yr and Fc can be estimated using LGM model parameters: Calculated EUR 30yr vs. Model EUR 30yr

75. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 75/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Summary and Conclusions

76. Slide — 76/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Summary: (Summary and Conclusions) ●○○ ● Duong Model ■ Very good data match in transient flow regimes. ■ Reliable reserve estimates in transient flow regimes. ■ Very Poor data match for boundary-dominated flow regimes. ■ Reserves are significantly overestimated beyond transient flow regime. ■ Do not conform to diagnostic functions (D-,b-parameter and b-derivative) during boundary conditions. ● Logistic Growth Model (LGM) ■ Very good data match in transient and transition flow regimes. ■ Reliable reserve estimates in transient and transition flow regimes. ■ Poor data match for boundary-dominated flow regimes. ■ Reserves are overestimated beyond transient/tranistion flow regimes.

77. Slide — 77/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Summary: ● MDNG – 1 and MLGM – 1 flow regime ■ Based on the "loss-ratio" relation. ■ Similar to PLE model. ■ Excellent match for Transient, transition and boundary- dominated flow regimes. ● MDNG – 2 and MLGM – 2 models ■ Based on data Diagnostic Functions. ■ Excellent match for transient, transition and boundary- dominated flow regimes. ■ Very flexible. ● It is possible to integrate time-rate model parameters with reservoir/well parameters using parametric correlations when production and completion strategies remain constant (fairly changing). (Summary and Conclusions) ●●○

78. Slide — 78/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Conclusion: (Summary and Conclusions) ●●● ● The limitations of Duong and LGM models is investigated. ● Duong and LGM models overestimate reserves. ● Diagnostic Functions can be used to generate representative time-rate relations. ● New time-rate relations match all flow regimes observed from production data of unconventional reservoirs. ● New time-rate relations provide constrained reliable reserve estimates. ● The preliminary investigation to use parametric correlations for estimation of reservoir properties can be applied to field cases.

79. Slide — 79/80 Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Recommendation for Future Work: (Recommendation) ● ● Identification of other diagnostic function will allow accurate model parameter estimation and assists in the derivation of reliable models. ● The newly derived relations should be tested using production data from other reservoir systems to check their flexibility/accuracy. ● The developed parametric correlations methodology should be applied to field cases.

80. Yohanes ASKABE Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) yohanes.askabe@pe.tamu.edu Slide — 80/80 Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties Yohanes Askabe — MS Thesis Defense — October 12, 2012 Department of Petroleum Engineering — Texas A&M University Rate-Decline Relations for Unconventional Reservoirs and Development of Parametric Correlations for Estimation of Reservoir Properties End of Presentation