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Thesis Defense College Station, TX (USA) — 05 September 2013
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Transcript of 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)
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.
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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.
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Source: beckenergycorp.com
The Physical System
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Figure 1 — Schematic of non-interfering fracture behavior for a horizontal well with multiple vertical fractures.
Validation of the Study
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●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
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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.).
Thesis DefenseCollege Station, TX (USA) — 05 September 2013
Rate-Time Analysis
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
●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).
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Rate-Time Analysis
Rate-Time Analysis
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●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
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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— 1.63 BSCF
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Rate-Time Analysis
Thesis DefenseCollege Station, TX (USA) — 05 September 2013
Model-Based Production Analysis
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
●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.
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Model-Based Production Analysis
:
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)
■ EUR— 1.92 BSCF
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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— 1.41 BSCF
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Model-Based Production Analysis
Model-Based Production Analysis
Raw Data Plot "Normalized" Data Plot
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Vertical Shift Factor = 1.7(increasing permeability)
Horizontal Shift Factor = 1.05(increasing flux area)
Relative Analysis Exercise:
Thesis DefenseCollege Station, TX (USA) — 05 September 2013
Integration of Rate-Time Analysis and Model-Based Production Analysis
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
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).
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Integration and Correlation
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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.
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Integration and Correlation
●EUR Crossplot:
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●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.
Thesis Defense — Landon RISER — Texas A&M University College Station, TX (USA) — 05 September 2013
Integration and Correlation
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●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 DefenseCollege Station, TX (USA) — 05 September 2013
Pressure Transient Analysis
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
Pressure Transient Analysis
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●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
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●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
Pressure Transient Analysis
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●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:
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●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 DefenseCollege Station, TX (USA) — 05 September 2013
Summary and Conclusions
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
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.
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Thesis DefenseCollege Station, TX (USA) — 05 September 2013
Landon RISERDepartment of Petroleum Engineering
Texas A&M UniversityCollege Station, TX 77843-3116 (USA)
An Integrated Well Performance Study for Shale Gas Reservoir Systems — Application to the Marcellus Shale