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

Thesis DefenseCollege Station, TX (USA) — 05 September 2013

Landon RISERDepartment of Petroleum Engineering

Texas A&M UniversityCollege Station, TX 77843-3116 (USA)

[email protected]

An Integrated Well Performance Study for Shale Gas Reservoir Systems — Application to the Marcellus Shale

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

Slid

e —

2/3

0

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.


Slid

e —

3/3

0

Source: beckenergycorp.com

The Physical System


Slid

e —

4/3

0

Figure 1 — Schematic of non-interfering fracture behavior for a horizontal well with multiple vertical fractures.

Validation of the Study


Slid

e —

5/3

0

●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.

Validation of the Study


Slid

e —

6/3

0

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.).


Rate-Time Analysis



[email protected]

●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).


Slid

e —

8/3

0

Rate-Time Analysis

Rate-Time Analysis


Slid

e —

9/3

0

●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).

limitlimit

limit/1

]exp[

1)(

DDtDq

DDtbD

qtq

i

bi

i

]exp[ )( nii tDtDqtq

………… Modified Hyperbolic Relation

………..… Power-Law Exponential Relation

●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— 2.88 BSCF


Slid

e —

10/

30

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).



Slid

e —

11/

30

Rate-Time Analysis


Model-Based Production Analysis



[email protected]

●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.


Slid

e —

13/

30


:

Derivative of the integral of rate-normalized pseudopressure:

●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)



Slid

e —

14/

30


●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)



Slid

e —

15/

30



Raw Data Plot "Normalized" Data Plot


Slid

e —

16/

30

Vertical Shift Factor = 1.7(increasing permeability)

Horizontal Shift Factor = 1.05(increasing flux area)

Relative Analysis Exercise:


Integration of Rate-Time Analysis and Model-Based Production Analysis



[email protected]

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).


Slid

e —

18/

30

Integration and Correlation


Slid

e —

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

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.


Slid

e —

20/

30


●EUR Crossplot:


Slid

e —

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 (qgi).■ Decline character could be

captured, but area-under-the-curve impacted by erroneous initial point.



Slid

e —

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.


Pressure Transient Analysis



[email protected]



Slid

e —

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.

Pressure Transient Analysis●26 Day Buildup Test


Slid

e —

25/

30

●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.



Slid

e —

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).

Pressure Transient Analysis●Buildup – Drawdown

Dichotomy:


Slid

e —

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?


Summary and Conclusions



[email protected]

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.


Slid

e —

29/

30




[email protected]

An Integrated Well Performance Study for Shale Gas Reservoir Systems — Application to the Marcellus Shale

Documents

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