1. Thesis Defense College Station, TX (USA) — 05 September 2013 Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu An Integrated Well Performance Study for Shale Gas Reservoir Systems — Application to the Marcellus Shale

2. Outline ● Purpose of the Study: ■ Apply modern well/reservoir analysis techniques to field cases. ■ Present methods used and challenges encountered in our pursuit. ● Validation of the Study: ■ Illustrative cases of non-uniqueness in model interpretations. ■ Ramifications of non-uniqueness in long-term performance. ● Rate-Time and Model-Based Production Analyses: ■ Initial analyses performed contemporaneously, but independently. ■ Integrated analyses based on initial parameter/property correlations. ■ Adjustments made to "tune" parameters based on initial correlation. ■ Observe effect the "tuning" has on EUR. ● Pressure Transient Analysis: ■ Illustrative cases with high-frequency bottomhole pressure gauges. ■ Cases of daily surface pressures and their potential utility. ● Summary & Conclusions: ■ Summary of the work done. ■ Discussion on the key takeaways from the study. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 2/30

3. Purpose of the Study ● Our Primary Objectives: ■ Present a specialized workflow for modern dynamic data analyses. ■ Apply the workflow to production data history of Marcellus shale wells. ■ Discuss challenges encountered in unconventional reservoir analysis. ■ Demonstrate a correlation/"tuning" concept from analysis integration. ■ Address literature void of unconventional PTA with illustrative cases. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 3/30 Source: beckenergycorp.com

4. The Physical System Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 4/30 Figure 1—Schematic of non-interfering fracture behavior for a horizontal well with multiple vertical fractures.

5. Validation of the Study Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 5/30 ● Issue of Non-uniqueness: ■ We can model a single-well diagnostic with infinite combinations. — (i.e. k, x f, F c, etc.) ■ Constraint on value ranges is our own scientific intuition. ■ The case shown below serves as a type-well for the region.

6. Validation of the Study Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 6/30 EUR Variance = 0.36 BSCF (or 24 percent) for this case. ● Long-term Performance Ramifications: ■ The ultimate result is reliable EUR values. ■ We can "bound" (or constrain) our EUR predictions using parameters that adhere to results/analogs gathered from independent sources (e.g., core analysis, pre-frac tests, etc.).

7. Thesis Defense College Station, TX (USA) — 05 September 2013 Rate-Time Analysis Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu

8. ● Rate-Time Concepts: ■ Diagnostic Data — Continuous calculation of loss ratio (D -1 ) and loss ratio derivative (b). — Qualitative evaluation of characteristic behavior. — Adjust model parameters to match diagnostic data (D and b). ■ Flow Rate Data — Upon matching diagnostics, we shift the initial flow rate (q gi ). Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 8/30 Rate-Time Analysis

9. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 9/30 ● We Used Two "Modern" Rate-Time Relations: ■ Modified Hyperbolic Relation — Adaptation of Arps’ hyperbolic model with an exponential "tail." — Captures early-time hyperbolic decline behavior. — Avoids indefinite extrapolation of early-time behavior. ■ Power-Law Exponential Relation — Developed empirically based on observed "power law" behavior. — Provides adequate representation for transient and transition flow. — Conservatively forecasts EUR (serves as a lower bound). ………… Modified Hyperbolic Relation ………..… Power-Law Exponential Relation

10. ● Field Case #1 ■ Modified Hyperbolic Relation — We focus on data > 60 days. — Hyperbolic D(t) character. — Relatively constant b(t). ■ Match Parameters — q gi = 2029 MSCFD — D i = 0.0047 — b = 1.9 — D limit = 10% (default). ■ EUR — 2.88 BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 10/30 Rate-Time Analysis

11. ● Field Case #2 ■ PLE Relation — We focus on data > 20 days. — Power law D(t) and b(t) character. — Excellent q g (t) match. ■ Match Parameters — q gi = 1715 MSCFD — Ď i = 0.068 — n = 0.45 — D ∞ = 0 (default). ■ EUR — 1.63 BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 11/30 Rate-Time Analysis

12. Thesis Defense College Station, TX (USA) — 05 September 2013 Model-Based Production Analysis Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu

13. ● Production Analysis Concepts: ■ Diagnostic Plot — Rate-normalized pseudopressure calculated continuously. — Plotted against t e. — Diagnostic analog to well testing. — Constant-rate equivalent. ■ Method of Use — Load pressure and rate histories. — QA/QC. — Extract flow period(s) of interest. — Qualitative evaluation (diagnostics). — Incorporate subsurface data. — Build analytic model(s). — Forecast model(s) to obtain EUR. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 13/30 Model-Based Production Analysis

14. ● Field Case #1 ■ Diagnostic Discussion — Early skin effect (common). — Stabilization @ 100 days, t e. — Linear Flow (1/2 slope). — Moderate conductivity fracture. ■ Model Parameters — k= 260 nD — x f = 180 ft — F c = 1 md-ft — n f = 36(# of fractures) ■ EUR — 1.92 BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 14/30 Model-Based Production Analysis

15. ● Field Case #2 ■ Diagnostic Discussion — Very similar to Case #1. — Noisier data (operations issues?). — Stabilization @ 200 days, t e. — Moderate conductivity fracture. ■ Model Parameters — k= 230 nD — x f = 100 ft — F c = 0.42 md-ft — n f = 36(# of fractures) ■ EUR — 1.41 BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 15/30 Model-Based Production Analysis

16. Raw Data Plot"Normalized" Data Plot Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 16/30 Vertical Shift Factor = 1.7 (increasing permeability) Horizontal Shift Factor = 1.05 (increasing flux area) Relative Analysis Exercise:

17. Thesis Defense College Station, TX (USA) — 05 September 2013 Integration of Rate-Time Analysis and Model-Based Production Analysis Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu

18. Integration and Correlation of Well/Reservoir Metrics ● The Workflow: ■ Independently analyze rate-time data with modern rate-time relations — Power-Law Exponential and Modified-Hyperbolic relations. — Model based on the D- and b-parameter behavior (diagnostic). — Tabulate model parameter results. ■ Independently analyze pressure-rate-time data with analytical models — Inspect the pressure-flowrate relationship for consistency. — Evaluate the diagnostic response from RNP output. — Create analytical well models that represent the data. ■ Combine the key results from the two analyses — High-quality flowrate data with minimal interruptions is crucial. — Constrain the integration to the wells with the highest quality data. — Crossplot model results from rate-time with well/reservoir analysis. — Iteratively refine initial correlations by imposition. — Observe resultant change in correlation(s). Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 18/30

19. Integration and Correlation Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 19/30 Correlation of Modified Hyperbolic b(t); and k from Diagnostic Plot: b = 2.4 k = 170 nD b-parameter k from derivative correlate

20. Integration and Correlation ● Tuning Exercise: ● Concept: ■ Based on idea of interrelatedness of flow properties and decline parameters. — Rate-decline a function of pressure distribution. — Pressure distribution according to rock/formation properties. ● Process: ■ Crossplot k and hyperbolic b(t). ■ Tune k values to linear trend. ■ Adjust flow properties (x f, F c, etc.) accordingly to obtain new match. ■ Re-forecast updated model for new EUR value. ■ Observe changes in updated EUR correlation. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 20/30

21. Integration and Correlation ● EUR Crossplot: Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 21/30 ● Graphical Observations: ■ We observe a >1:1 relationship. ■ R-squared value = 0.78. ● Conceptual Comments: ■ Pre-tuning R-squared value on the order of 0.6. ■ Error increases with increasing model-based EUR. ■ Slope or intercept adjustment most appropriate model? ● Hypothesis: ■ Rate-time EUR values proportional to initial flow rate (q gi ). ■ Decline character could be captured, but area-under-the-curve impacted by erroneous initial point.

22. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Integration and Correlation Slide — 22/30 ● EUR Histogram (PA and Rate-Time) ■ Alternate Graphic to Correlation Plot — Pseudo-Gaussian distribution. — Narrower range for PA. — Two "outlier" EURs from Rate- time. ■ Bin Selection — "Like" binning for comparison. — Manipulative binning could produce more similar continuous curve (w/ offset). ■ Conundrum — We’re still left uncertain precisely why rate-time analysis consistently overestimates EUR w.r.t. model-based forecasting.

23. Thesis Defense College Station, TX (USA) — 05 September 2013 Pressure Transient Analysis Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu

24. Pressure Transient Analysis Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 24/30 ● Brief Rundown: ■ Challenges faced in pressure transient analysis in shale reservoirs — Non-uniqueness — Expense (in terms of money and time) — Technology ■ Benefits realized from PTA — Independent source of information. — Confirmation of model parameters from production analysis. ■ What follows — An illustrative example of a traditional pressure buildup test. — Discussion of potential use of daily surface pressure data. — Demonstration of static and dynamic flow dichotomy.

25. Pressure Transient Analysis ● 26 Day Buildup Test Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 25/30 ● Diagnostic Attributes: ■ Half-slope (High F cD ). ■ Minimal Wellbore Storage. ■ Minimal skin effect. ● Model: ■ Modeled with k from PA. ■ Adjusted x f, F c, and skin factor to obtain match. ■ Requires lower x f, but greater F c (than PA) to obtain match. ■ This is a common theme: — We observe higher conductivity response during shut-in than in drawdown.

26. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Pressure Transient Analysis Slide — 26/30 ● The Case for Daily Surface Pressure ■ Surface Pressures Overlay — Both derivative and pressure drop ■ For Dry Gas — Pressure drop largely conserved — Liquid dropout a non-issue ■ Qualitative/Quantitative — If we don’t feel comfortable modeling surface buildups, we can potentially benefit from diagnostics (qualitative).

27. Pressure Transient Analysis ● Buildup – Drawdown Dichotomy: Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 27/30 ● Diagnostic Dichotomy: ■ Half-slope (1/2) Buildup. ■ Quarter-slope (1/4) Drawdown. ■ Minimal skin effect. ● Fracture Behavior ■ All buildups display linear flow (1/2). — High fracture conductivity ■ Most drawdowns are bilinear (1/4). — Low (finite) conductivity ■ Does fracture flow depend appreciably on effective stress? ■ How can we account for this dichotomy? ■ What are the long-term implications of a stress dependent conductivity?

28. Thesis Defense College Station, TX (USA) — 05 September 2013 Summary and Conclusions Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu

29. Summary and Conclusions ● Summary: ■ Performed independent production data and rate-time analyses. ■ Integrated the two analyses with an iterative correlation scheme. ■ Discussed challenges in unconventional well performance analysis. ■ Presented a workflow that attempts to reduce non-uniqueness. ■ Introduced PTA as an analysis tool in unconventional reservoirs. ● Conclusions: ■ From this work we conclude the following: — Rate-time diagnostics exhibit primarily hyperbolic decline character for our 55-well data set. — PLE relation produces the most conservative EUR estimates. — Bilinear flow (1/4 slope) is the predominant flow regime. — Linear flow (1/2 slope) is the exclusive PTA diagnostic. — Correlation scheme using a "tuning" technique improved the EUR relationship between model-based and rate-time analyses. — Model-based production analysis is an effective tool for cases of erratic production history, while rate-time analysis requires smooth, lightly-interrupted flow periods. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013 Slide — 29/30

30. Thesis Defense College Station, TX (USA) — 05 September 2013 Landon RISER Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) landon.riser@pe.tamu.edu An Integrated Well Performance Study for Shale Gas Reservoir Systems — Application to the Marcellus Shale