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
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:
●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
■Illustrative cases with high-frequency bottomhole pressure gauges.
■Cases of daily surface pressures and their potential utility.
Purpose of the Study
●Our Primary Objectives:
Source: beckenergycorp.com
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Slide — 3/30
■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.
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
Slide — 4/30
The Physical System
Validation of the Study
● Issue of Non-uniqueness:
■ We can model a single-well diagnostic with infinite combinations.
—
(i.e. k, xf, Fc, etc.)
Slide — 5/30
■ 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
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
Slide — 6/30
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
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
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
diagnostics, we shift the
initial flow rate (qgi).
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Slide — 8/30
— Upon matching
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
qi


D

D
limit


1/ b
q (t )   1  bDi t 
 ………… Modified Hyperbolic Relation
q exp[ D t ] D  D 
limit
limit 
 i
q(t )  qi exp[  D t  Di t n ] ………..… Power-Law Exponential Relation
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Slide — 9/30
— Developed empirically based on observed "power law" behavior.
— Provides adequate representation for transient and transition flow.
— Conservatively forecasts EUR (serves as a lower bound).
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
— 2.88 BSCF
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Slide — 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.
— qgi
— Ďi
— n
— D∞
■ EUR
= 1715 MSCFD
= 0.068
= 0.45
= 0 (default).
— 1.63 BSCF
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Slide — 11/30
■ Match Parameters
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
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
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
Integral of rate−normalized pseudopressure:
𝑰 𝒕𝒆 =
𝟏
𝒕𝒆
𝒕𝒆
𝟎
𝒎 𝒑𝒊 − 𝒎 𝒑𝒘 𝝉
𝒒𝒈 𝝉
𝒅𝝉
Derivative of the integral of rate-normalized pseudopressure:
𝑰 ′ 𝒕𝒆 =
𝝏𝑰 𝒕𝒆
𝝏𝒍𝒏 𝒕𝒆
Slide — 13/30
— 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.
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
■ EUR
= 260
= 180
=1
= 36
— 1.92 BSCF
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
nD
ft
md-ft
(# of fractures)
Slide — 14/30
— k
— xf
— Fc
— nf
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
= 230 nD
= 100 ft
= 0.42 md-ft
= 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
— k
— xf
— Fc
— nf
Model-Based Production Analysis
Relative Analysis Exercise:
"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
Slide — 16/30
Raw Data Plot
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
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.
— 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
■ Combine the key results from the two analyses
Integration and Correlation
Correlation of Modified Hyperbolic b(t); and k from Diagnostic Plot:
b-parameter
b = 2.4
Thesis Defense — Landon RISER — Texas A&M University
College Station, TX (USA) — 05 September 2013
correlate
k = 170 nD
Slide — 19/30
k from derivative
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:
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
■ Crossplot k and hyperbolic b(t).
■ Tune k values to linear trend.
■ Adjust flow properties (xf, Fc,
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-based EUR.
■ Slope or intercept adjustment most
appropriate model?
●Hypothesis:
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
Slide — 21/30
■ Rate-time EUR values proportional
Integration and Correlation
●EUR Histogram (PA and Rate-Time)
■ Alternate Graphic to Correlation Plot
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
Slide — 22/30
— Pseudo-Gaussian distribution.
— Narrower range for PA.
— Two "outlier" EURs from Rate-
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
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
Slide — 24/30
— 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
Pressure Transient Analysis
●26 Day Buildup Test
●Diagnostic Attributes:
■ Half-slope (High FcD).
■ Minimal Wellbore Storage.
■ Minimal skin effect.
●Model:
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
Slide — 25/30
■ Modeled with k from PA.
■ Adjusted xf, Fc, and skin factor to
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
Slide — 26/30
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
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
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
Slide — 27/30
■ Does fracture flow depend
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
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:
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
— Rate-time diagnostics exhibit primarily hyperbolic decline
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