1. 07 April 2010 Denver, CO OPTIMIZING DEVELOPMENT STRATEGIES TO INCREASE RESERVES IN UNCONVENTIONAL GAS RESERVOIRS Presented to: RPSEA Unconventional Gas Program Prepared by: Duane McVay, Gulcan Turkarslan, and Rubiel Ortiz, Texas A&M University J. Eric Bickel and Luis Montiel, The University of Texas at Austin

2. 1 Agenda  Background and Motivation  Reservoir Model  Decline Curve Model  Decision Model

3. 2 Project Objectives Objectives -- Develop new technologies for determining optimal dynamic development strategies and testing programs in gas shale and tight sand reservoirs. -- Understand the tradeoff between Stage 1 spacing and duration. Application University and industry partners to determine optimal well spacing and completion methods in the Barnett Shale, Parker County, Texas, and the Gething tight gas formation in Alberta, Canada. Impact: Technology incorporation into operators’ development processes will enable reaching optimal spacing as quickly as possible, accelerating production and increasing reserves.

4. 3 Our goal is to integrate a reservoir and a decision model. + The reservoir model will be based on statistical or reservoir simulation techniques. The decision model will employ decision tress or dynamic programming to determine the optimal development program. The models will incorporate uncertainty in reservoir parameters.

5. 4 Agenda  Background and Motivation  Reservoir Model  Decline Curve Model  Decision Model

6. 5 Our initial work is based on the Berland River Formation. The study area encompasses 650 km 2 and 46 existing wells.

7. 6 We have constructed a stochastic reservoir modeling tool.  Single-well, 1-layer, single-phase reservoir model used  A stochastic modeling tool, @Risk, coupled to CMG IMEX  Generated VBA code in Excel to create the simulation data files and run the simulator  Perform Monte Carlo combined with reservoir simulation

8. 7 We consider drilling either 1, 2, 4 and 8 wells in a 640 acre section.  Drill up to 8 wells in a section  640 (1 well/section)  320 (2 wells/section)  160 (4 wells/section)  80 acres (8 wells/section)

9. 8 We consider uncertainty in net pay, porosity, formation depth, and drainage area. Uncertain Parameters & Distributions Other input parameters

10. 9 We correlate permeability and porosity. The simulation model is calibrated to well performance data.

11. 10 We simulate each possible initial spacing (Stage 1) and one stage of down spacing (Stage 2).  Simulate 10 possible 2-stage downspacing combinations between 640, 320, 160 and 80 acres  Simulate different Stage 1 lengths (1 to 5 years)  Quantify  Best month gas production  Stage end average pressure  Stage end gas production  20-year discounted cumulative production

12. 11 UGR provided a geostatistical study completed by Schlumberger. A variogram analysis was performed to generate possible representations of porosity, permeability and net pay at interwell locations and determine the correlation coefficients between wells separated by specific distances.

13. 12 Agenda  Background and Motivation  Reservoir Model  Decline Curve Model  Decision Model

14. 13 To facilitate the integration of the reservoir and decision models, simulated production was fit with decline curves.  Modeled with hyperbolic decline with switch to exponential decline to account for long transient decline period in unconventional reservoirs.  Wrote VBA code coupled to the Solver add-in of Excel to perform the decline curve regression.  Integrated with the reservoir modeling software.  Determined decline parameters and the associated distribution functions for input to the decision model. 1000 Res. Sims. Stages 1&2 Fit Decline Curves Means Std Devs Correlation Decision Model Optimal Stage 1 Spacing Optimal Stage 2 Spacing for every possible Stage 1 scenario

15. 14 The decline curve model begins with hyperbolic and transitions to exponential. Decline curve analysis performed on simulated 20-year production data Decline curve analysis performed on simulated Stage 1 production data If Else

16. 15 Let’s look at example simulation results for a Stage 1 spacing of 160 acres and the downspacing to 80 acres.

17. 16 Stage 1 and 2 correlation results (1 Year and 3 Year Stage 1)…

18. 17 This analysis provides several insights regarding learning.  Correlation in q i increases with:  Decreasing stage 1 spacing  Decreasing stage 2 spacing  Increasing stage 1 length  Our ability to learn about the initial decline rate for Package 2 is limited.  Our ability to learn about b is muted until we get down to about 160 ac for stage 1.

19. 18 Agenda  Background and Motivation  Reservoir Model  Decline Curve Model  Decision Model

20. 19 Our initial decision model allows for one stage of downspacing. This is an “option” analysis. We make the Stage 2 decision after learning about Stage 1.

21. 20 We make the following economic assumptions for illustrative purposes:

22. 21 If Stage 1 is 1 yr in length then the optimal strategy is 640 ac to start and then downspace to 160 ac if high production is observed.

23. 22 Notice that the dynamic strategy is about $660K better than a static 160 ac strategy. $2.09 MM - $1.43 MM = $0.66 MM

24. 23 A three-year Stage 1 is worse than a 1-year Stage 1, but the dynamic strategy is still better.

25. 24 Let’s look at a higher price environment.

26. 25 With a 1-year stage 1, 640s are best to start and we downspace to 160s unless we see low production.

27. 26 However, under a 3-year Stage 1 we should start with 160s and not downspace.

28. 27  We are able to integrate the reservoir model and the decision model via the use of decline curves.  The reservoir / decline curve studies show that we are able to learn about reservoir parameters.  Initial spacing and stage lengths are substitutes to some degree.  We believe we can learn about each decline curve parameter, but learning about initial production rate appears strongest.  Optimal dynamic development strategies may be worth millions more than static strategies. In conclusion…

29. 28 Future Plans  Refine reservoir model.  Expand decision model to allow for learning about all decline curve parameters.  Incorporate completion efficiency into the reservoir/decision modelling.  Construct an areal, multi-well model of the same reservoir to better model heterogeneity and interference between wells.  Incorporate pilot down spacing into the reservoir/decision modeling to determine the optimum number and length of pilots.

30. 29 07 April 2010 Denver, CO OPTIMIZING DEVELOPMENT STRATEGIES TO INCREASE RESERVES IN UNCONVENTIONAL GAS RESERVOIRS Presented to: RPSEA Unconventional Gas Program Prepared by: Duane McVay, Gulcan Turkarslan, and Rubiel Ortiz, Texas A&M University J. Eric Bickel and Luis Montiel, The University of Texas at Austin

31. 30 Deliverables 2/28/10Preliminary integrated reservoir and decision making models based on synthetic data. 8/17/10Extend model and apply to UGR test reservoir 5/17/11Extend model to second test reservoir 8/17/11Final report

32. 31 Backup

33. 32 Background  High risk associated with low-permeability gas sands  Complex heterogeneities  Variable reservoir properties  Uncertain completion and stimulation efficiency  Sound development decisions needed  Efficient completion practices  Optimal well spacing  A trade-off between conserving capital and protecting the environment to avoid over drilling, but maximizing production by quickly achieving the optimum well spacing

34. 33 Background  Determination of optimum development strategy in tight gas sands is technically challenging.  Large variability in rock quality  Wide range of depositional environments  Large number of wells  Limited reservoir information  Time & budget constraints

35. 34 Background  Traditional methods for determining optimal well spacing  Statistical comparison of the performance of wells drilled at different spacings  Applicable only when sufficient production data from multiple infill programs are available  In emerging plays, historical infill programs are not available to evaluate optimal spacing with traditional methods.

36. 35 Permeability Model  Permeability model based on a porosity-permeability correlation  A scalar factor of 1.92 added to calibrate the existing simulation model to the well performance data  Uncertainty incorporated into the permeability model by attributing a normal distribution to the porosity-permeability correlation.

37. 36 Permeability Model

38. 37 Drainage Area Aspect Ratio (Y/X)

39. 38 Reservoir Model Features Distance between wells The wells to be modeled were selected based upon the most prevalent separation distance from well 1

40. 39 Reservoir Model Features  Only one new well is modeled for each stage (i.e., 4 wells/section)  640 acres – Well 1  320 acres – Well 2  160 acres – Well 3  80 acres – Well 4  Each set of four wells are modelled individually but sampled from the same reservoir property distributions Move to backup

41. 40 Probabilistic Pressure Model  Employ a simple probabilistic pressure model  Estimate the possibility of communication among wells based on the available maps of Berland River Area  Assumption  75% chance of drainage interference – the final average pressure of Stage 1 assigned as the initial pressure of Stage 2  25% chance of no communication – the initial reservoir pressure assigned as the initial pressure of Stage 2