Thesis Defense College Station, TX (USA) — 05 September 2013

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

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

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. Source: beckenergycorp.com Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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

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

6. Validation of the Study • 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.). EUR Variance = 0.36 BSCF (or 24 percent) for this case. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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 Analysis • 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 (qgi). Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

9. Rate-Time Analysis • 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 Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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

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

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. Model-Based Production Analysis • Production Analysis Concepts: • Diagnostic Plot • Rate-normalized pseudopressure calculated continuously. • Plotted against te. • 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. : Derivative of the integral of rate-normalized pseudopressure: Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

14. Model-Based Production Analysis • Field Case #1 • Diagnostic Discussion • Early skin effect (common). • Stabilization @ 100 days, te. • Linear Flow (1/2 slope). • Moderate conductivity fracture. • Model Parameters • k= 260 nD • xf = 180 ft • Fc = 1 md-ft • nf= 36 (# of fractures) • EUR • 1.92BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

15. Model-Based Production Analysis • Field Case #2 • Diagnostic Discussion • Very similar to Case #1. • Noisier data (operations issues?). • Stabilization @ 200 days, te. • Moderate conductivity fracture. • Model Parameters • k= 230 nD • xf = 100 ft • Fc = 0.42 md-ft • nf= 36 (# of fractures) • EUR • 1.41BSCF Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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

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 Correlationof 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

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

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 (xf, Fc, 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

21. Integration and Correlation • EUR Crossplot: • 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-basedEUR. • Slope or intercept adjustment most appropriate model? • Hypothesis: • Rate-time EUR values proportional to initial flow rate (qgi). • Decline character could be captured, but area-under-the-curve impacted by erroneous initial point. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

22. Integration and Correlation • 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. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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 • 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. Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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

26. Pressure Transient Analysis • 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). Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

27. Pressure Transient Analysis • Buildup – Drawdown Dichotomy: • 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? Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013

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

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