Lake Erie Overview: Physical Characteristics, Economic Importance, and Recreational Value
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The Importance and Value of OpenThe Importance and Value of Open--hole hole Geophysical Log Calibration in NonGeophysical Log Calibration in Non Archie Archie RocksRocksGeophysical Log Calibration in NonGeophysical Log Calibration in Non--Archie Archie RocksRocksWeatherford Labs 2012 Open HouseGolden, Colorado – April 12, 2012
Chad HartmanChief Technical AdvisorUnconventional Reservoir Services
C a n adaU n i t e d S t a t e s
B r a z i lM e x i c oT r i n i d a d
K u w a i tL i b y a
A u s t r a l i aI n d i aM a l a y s i a
For contact information, please visit our website: www.weatherfordlabs.com
N o r w ayU n i t e d K i n g d o mK a z a k h s t a n
T r i n i d a dV e n e z u e l aA r g e n t i n a
L i b y aO m a nS a u d i A r a b i aU n i t e d A r a b E m i r a t e sI r a q
M a l a y s i aT h a i l a n dN e w Z e a l a n dI n d o nes i a
Why is Why is “Shale” Core“Shale” Core--toto--Log Calibration Log Calibration Important?Important?pp
• Shale composition results in high, variable matrix density complicating porosity estimatesporosity estimates.
• It is impossible to calculate accurate gas and water saturations from log data unless calibrated with core data.
– Water resistivity (salinity) is unknownWater resistivity (salinity) is unknown
– Clay corrected Archie parameters are unknown
• Clay effects on neutron porosity usually eliminates gas effects. Is there any gas present?
• Low TOC and adsorbed/absorbed hydrocarbons have little impact on log data.
• Density log will respond to both TOC and porosity complicating/invalidating d it it ti tdensity‐porosity estimates
• Accurate quantification of rock mechanical properties are problematic from log data alone unless calibrated with core data (dynamic vs. static)
Key Reservoir Properties Needed From CoreKey Reservoir Properties Needed From Core
Property of interest Core data Most useful log data
"Shale Routine CorePorosity
Shale Routine Core Analysis" (SRP, GRI, TRA)
Density (mostly)
TOC LECO or Programmed Pyrolysis
GR, density, resistivity y y
Fluid saturations "Shale Routine Core Analysis" (SRP, GRI, TRA)
Resistivity with kerogen corrected shale porosity
Mi l XRD FTIR XRF M ECS l lMineralogy XRD, FTIR, XRF Most + ECS‐style logs
Permeability "Shale Routine Core Analysis" (SRP, GRI, TRA)
None really, unless formationis at irreducible SW
Geomechanics Static elastic moduli and associated acoustics
DTC, DTS, RHOB, or their synthetic substitutes
GeochemistryVisual Kerogen Assessment,
variesGeochemistry Reflectance, Pyrolysis
varies
General General Approaches to Shale Gas Log Approaches to Shale Gas Log AnalysisAnalysisyy
• Global stochastic
• Global deterministic
• Local deterministic aka direct calibration to core (recommended)– aka direct calibration to core (recommended)
• Each has distinct advantages & disadvantages
Approaches to Approaches to Shale Log Analysis Shale Log Analysis -- 11
• Global/stochastic– Attempt to determine or guess what components are
present in the shale matrix (mineral, organic, fluid)– Characterize end member properties of each
component – Invert the logs to determine properties that best fit
the observed log character
Guess at mineral and fluid volumes
Compute log responses from a the observed log character– Implementations examples include ELAN, Multimin,
Statmin, etc. – Actual log curves vs. forward models – convergence is
the key criteria for a satisfactory model.
p g pforward model
Compare computed log responses with actual log data
• Key Questions/Risks – How do you know, or guess at, the end member points?
– What are the kerogen endpoints (GR, RhoB, Nphi, etc.)?
g
Satisfactory match?
No
– What is the inorganic grain density of the clay components?
– What are the error functions associated with those end points AND the log data (that is, the total error bar)?
DONE
Yes
bar)?
Approaches to Approaches to Shale Log Analysis Shale Log Analysis -- 22
• Global/deterministic– Characterize properties using a broad collection of core data, either globally or basin/play specific
– Utilize industry accepted general models to characterize properties (i.e. Schmoker 1979, 1981 equations or Passey’s Δ log R)
– Use Service company specific methodology
• Biggest RisksBiggest Risks– Regional or global equations do not apply locally and this fact is unknown
– Variable selected does not really correlate very well with property of interest (i e assumption that GR α TOC→ V is not a simple functioninterest (i.e. assumption that GR α TOC → Vuranium is not a simple function of TOC)
– Co‐linearities (using density to predict both ф and TOC can be problematic)problematic)
Approaches to Approaches to Shale Log Analysis Shale Log Analysis -- 33
• Local/deterministic– Calibrate log models in restricted areas, down to individual wells or single cores
– Simple linear & non‐linear regressions• GR vs. TOC, RHOB vs. TOC
– Multiple linear regression methods
– Multiple non‐linear regression p g
What distinguishes these 3 What distinguishes these 3 approaches?approaches?approaches?approaches?
• Each method has its advantages in certain settings or types of wells– Rich suite of modern logs available or just old public domain logs coverage?Rich suite of modern logs available, or just old, public domain logs coverage?– Is core data available for log calibration?
• Transparency– Some methods are basically black boxes & are difficult or impossible to reproducey p p– Others are totally transparent, you can write out a set of equations and you may
agree or not, but there they are
• TransportabilityS l ti i i t ft l d ll it– Some solutions require proprietary software, or only one vendor can really run it.
– Others require very specific logging measurements
• Return on Investment– Some solutions are powerful and accurate but may be expensive– Some solutions are powerful and accurate, but may be expensive.– Sometimes close enough is good enough – Basically comes down to client specific objectives and required level of certainty to
make informed business decisions
Local Deterministic Shale Gas LogLocal Deterministic Shale Gas Log Analysis – Most Accurate and Recommended Approach Recommended Approach
Practical Practical Issues when Performing Issues when Performing Local/Deterministic Log EvaluationLocal/Deterministic Log Evaluationgg
• Sample depths have to be accurate
• Core‐log depth shifting has to be precise
• Need to work in consistent units e g fractional bulk volume– e.g. fractional bulk volume
• Need to extract log data to correlate with core values at the appropriate depths
– Its typically inappropriate to average log data because it’s already sampling a much larger reservoir volume than discrete core data.
Merge and Merge and Convert Core Data Convert Core Data into into One SpreadsheetOne Spreadsheetpp
Perform CorePerform Core--log Depth log Depth Shift CorrectionsShift Corrections
• Merge in the core gamma scang g
• Perform depth shifts, honoring the physical breaks (i.e. don’t interpolate)
• Convert core depths to log depth
• It’s important to keep an audit trail so you know where things are and whereyou know where things are and where they were!
Export the Export the Log DataLog Data
• Once we know where the core data belongs, we export the log d l d h f l i icurve data at every core sample depth for analysis in a
statistical software package
• Merge the core and log data setsMerge the core and log data sets
• The more data you have at the same depths, the better
Some Critical Variables are not Some Critical Variables are not Measured in the LaboratoryMeasured in the Laboratoryyy
Sondergeld et al, SPE 131768g ,
• Inorganic grain density and kerogen density are not directly measured.
• Have to solve for these two properties by comparing modelHave to solve for these two properties by comparing model prediction to core measured TOC, XRD, porosity, and log RhoB.
Harness the Sophisticated Computing Harness the Sophisticated Computing Power of Statistical SoftwarePower of Statistical Software
3
XRD_CLAY / Standardized residuals
65
Pred(XRD_CLAY) / XRD_CLAY1
2
d residu
als
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RD_C
LAY
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20 25 30 35 40 45 50 55 60 65 70
Stan
dardized
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20 25 30 35 40 45 50 55 60 65XR
‐3XRD_CLAY
Pred(XRD_CLAY)
XRD_CLAY = ‐60.48‐44.90*PHIDN+0.1973*DTC+2.655*ct_root
Omit Any Extreme Outliers from the Data Set
Output Output -- Multiple Linear or Nonlinear Multiple Linear or Nonlinear Regressions Regressions from a from a List List of of Possible VariablesPossible VariablesSummary of the variables selection:
f ll ' k k ' h ' 'No. of variables Variables MSE R² Adjusted R²
Mallows' Cp
Akaike's AIC
Schwarz's SBC
Amemiya's PC
1 ct_root 25.062 0.687 0.620 8.135 127.579 130.906 0.329
2 DTC / ct_root 20.847 0.747 0.691 1.911 121.330 126.321 0.280
3 PHIDN / DTC / ct_root 18.829 0.778 0.727 ‐0.344 118.261 124.915 0.258
4 PHIDN / DTC / DTS / ct_root 19.219 0.780 0.728 1.396 119.928 128.246 0.269
The best model for the selected selection criterion is displayed in blue
Predictive power of multiple variables tends to no longer improve above ~5 curves
Using Best Fit Solutions, Solve for All Using Best Fit Solutions, Solve for All Key VariablesKey Variablesyy
• TOC volumesRh M i i• RhoMa inorganic
• Rho organic matter (kerogen)• Porosity & fluid saturationsPorosity & fluid saturations• Mineral volumes• Permeability• Static Elastic Moduli• Gas content
Its important to recognize development of unique zonal models are often required depending on vertical heterogeneity and statistical significance of the core data from each encountered facies
Gas Gas Content/OGIP CalculationsContent/OGIP Calculations
• Free gas calculated from porosity ‐ Sw computationsR i T t d P G di t d B– Requires Temperature and Pressure Gradients and Bg
– Need to correct for volume occupied by adsorbed gas present under in‐situ conditions (SPE 131772)
Ad b d l l t d L i t d th• Adsorbed gas calculated Langmuir parameters and the computed TOC log trace
– We solve for VL as f (TOC)– PL also comes from Solver instead of average– Typically core analytical programs contain insufficient isotherm data– Often temperature variation with depth is ignored
• Validate/Compare calculated total gas content results to “ground truth” canister gas empirical measurements
Marcellus Core Calibrated Log Interpretation Marcellus Core Calibrated Log Interpretation Illustrating Adsorbed Gas SaturationIllustrating Adsorbed Gas Saturation
GIP = 1359.7 A h ρ Gst
• Drainage Area (A) –can be arbitrarily set to 640 acres
• Reservoir thickness (h), bulk d ( ) d ldensity (ρ), and total gas content (Gst) are all calculated from the log traces that were developed and at same resolution of the
l d ( f ).LAS logs digits (~0.5 ft).
• Resulting GIP estimates per depth “layer” are summed to provide accurate and high resolution estimates of GIP throughout the target formation
Real World Application ExampleReal World Application Example
• Client – Perpetual Energy Inc.• Target – Colorado Shale Group in Perpetual’s lease holds located in
h lbsouthern Alberta• Scope of work
– Three locations were selected and wells were drilled. Minimum of 200 meters of core were acquired at each wellof core were acquired at each well
– A comprehensive core and gas analytical program was conducted on each well– Each well was logged with a complete modern log suite
• Local Deterministic log modeling was done for each wellocal eterministic log modeling was done for each well• All core and log data used to calibrate each individual curve fit equation were merged and used to develop average relationships to describe behavior across the entire dataset.
• Although there is some loss of accuracy in the property predictions within each individual well, this maximized the applicability of the final model over Perpetual’s entire lease holdings. Furthermore, we selected the most general log predictors possible in all cases to allow the modelthe most general log predictors possible in all cases to allow the model to be applied against older wells and datasets with limited logging suites.
• Common Colorado Shale• Common Colorado Shale Group log model with calculated gas storage capacities
• Common Colorado Shale Group log model with calculated lithologygy
Perpetual’sPerpetual’s Application Application of the Common Log of the Common Log Model to Field Development, Southern AlbertaModel to Field Development, Southern Alberta
• Perpetual applied the Colorado Shale Group common log model to 3610 wells, all having the required suite of logs.
• From the resulting variable models Perpetual developed a suite of maps detailing (among other things) free GIP, adsorbed GIP, and Brittleness (derived from the mineralogical models).
• Using GIP and Brittleness maps as a guide, Perpetual designed a 4 well completion program to test the range of variables and see how the rocks responded to fracture stimulation.
• The results of those completion tests appear to confirm the areas where higher productive capability were predicted from the common log model.
• Perpetual intends to perform a second phase of completion testing early this summer.
Isopach Map Indicating High and Low Isopach Map Indicating High and Low Areas of Production Potential Areas of Production Potential
Criteria based on synergy between GIP and brittleness
ConclusionsConclusions
• Log data are far more abundant than core data, and sample a g , pgreater volume of the reservoir, so accurate log models are essential for our evaluations of plays, areas, and wells
W t lib t d l t th k ( b i l )• We use cores to calibrate our models to the rock (obviously)
• If the core data are inaccurate, or the meaning of the measurement is unclear, our models will be useless and ,misleading
Recognition and ThanksRecognition and Thanks
I wish to recognize and express my deep gratitude to Bob Cluff from the g ff f
Discovery Group and Byron Cooper from Perpetual Energy for their f p gy f
significant contribution to the contents of this presentationf p
Thanks for Your Participation!Thanks for Your Participation!For additional information, please contact:For additional information, please contact:, p, p
Weatherford Laboratories16161 Table Mountain Parkway
Chad Hartman
Chief Technical Advisor
[email protected] Golden, Colorado 80403Dir: 720‐898‐8200Cell: 303‐519‐9918
@
Matt Bjorum
General Manager