FESM Image Petrophysics Final
Transcript of FESM Image Petrophysics Final
7/18/2019 FESM Image Petrophysics Final
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Image PetrophysicsA Fresh Look at Image Logs
Richard Holland
© 2009 Weatherford. All rights reserved.
Conventional Uses of Bore Hole Image Tools
• Visualization of complex structures
• Identification of faults and fractures and their orientation
• Determination of structural dip
• Definition of cross beds, thin beds and net-to-gross ratio insand-shale sequences
• Identification of sand-shale facies and sand thicknesscounts
• Secondary porosity evaluation
• Depth matching, orientation and substitution of cores
• Structural and breakout analysis
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Evolution of Bore Hole Image Tools
Baker AtlasStar (1995)
HalliburtonEMI (1994)
SchlumbergerFMI (1991)
PrecisionHMI 2001
WeatherfordCMI (2006)
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Technology Driver
• To generate high-resolution petrophysical propertiesfrom Bore-hole Images
• To quantify textural heterogeneity and improve core-facies predictability and petrophysical facies grouping
• To implement this methodology on all standard industryimage tools
• To extend the discrete single well analysis to a full field
approach
3
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MOTIVATION
• Combining borehole images with core and logs – Generally an empirical approach
– Based on facies or facies associations
• Textural mapping from images
– Permits indicative grain size distribution
– Can be used in further permeability analysis.
• Methodology
– Use textural mapping to identify flow units whichincorporates grain size
– Assign permeability relationship to flow units.
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Implications in Carbonate Reservoirs
• Carbonate Reservoirs:
– 60% of world oil reserves (est)
– 40% of world gas reserves (est)
• Carbonates differ from Clastics
– Deposition
– Digenesis
– Texture• Concept
– Provide an idea of porosity variation around the well-bore
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Methodology
• The Bore-hole Image Tool acquires multiple resistivity/conductivitysamples per depth interval; high depth resolution
• A distribution of the resistivity/conductivity can be constructed for eachinterval. Porosity distribution is binned
• The distribution bears remarkable similarity to grain size distr ibution andcan be calibrated to give a mean grain size and grain size distributionmap
Matrix Secondary
f
Porosity Distribution0% 100%
Porosity distribution fromArchie (relates conductivityto porosity!)
Porosity bin
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Visualizing The Conductivity Spectrum
Homogenous MildlyHeterogeneous
VeryHeterogeneous
Conductivity Histograms
Well Sorted Poorly Sorted Bi-modal
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Image Tool Resolution
AMZ/AND/ALD = 6”
1”
AcousticImages = 0.4”
ElectricalImages = 0.2”
Analysis & Certainties Are Tool Resolution Dependent
GeneratedVertical
Resolution
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Image Petrophysics
• Captures radial variability inporosity (Note the core plugdistribution – grey dots)
• PHIT from image (calibratedagainst PHIT from logs)
• Computes high resolutionvolumes
• HR Volumes and Porositydistribution can be calibrated
to Core• Variability of the rock is
captured
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Porosity Distribution
Porosity distribution
Sort Index
Porosity Bins
Porosities falling in thesame bin indicate wellsorted grains.Sort index is higher
Porosities falling inmultiple bins indicatepoorly sorted grains.Sort index is lower
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Permeability
• Permeability Equation:
• KAφeff2
• KA adjusted to matchcore permeability
• Alternatively:
• KA*φeff2 * 10^(KB φSimg/ φPimg)
φSimg: secondary porosity fromimage
φPimg: primary porosity fromimage
Ref: Doyen, 1988
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• Predict core facies• Evaluate lateral andvertical heterogeneityvariability
• Define core basedstacking cyclicty andsurfaces
• Define Flow units• Quantify porosity and
Permeability• Core facies in un-cored
wells
Geology-Petrophysics Integration
Core Facies Predictions
Core Plugs
Flow Units
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Clastics Approach
Core
Core Fabric Log &Description
Pseudo Lithology Log
Distribution of conductivityspectrum from BH Images
Sorting & Grain sizeIndex & Histograms
Grain Sorting IndexLog
Porosity Image map &Volumetric (OH Log-BHI)
HR Porosity Log &Volumetric
Sedimentological Log
Facies Analysis
LQC & Processing
Conventional Advanced
Manual Interpretation
Well Completion & Reservoir Characterization
Geostatistical and/or Neural Network
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BH Image Conductivity Ranges &Sedimentary Logs
BH Image Conductivity
Image Conductivity Converted to Lithology
Gamma RayLow High
Sandstone Argillaceous Sst Siltstone Mudstone
X-bedded
Laminated
Massive
Massive-Cemented
Rippled
Deformed
Hetrolithicmudstone
Massive(mottled)
Higher GR valuesmy be related to K-feldspars –needSpectral GR
More conductive – bedded sst higherpermeability
Distribution of conductivity spectrum
Conventional Approach ???
C o n v e n t i o n a
l
A p p r o a c
h ? ? ?
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Grain Size Analysis
Very Good Good SomeFair Low Very Low
Permeability
1000 mD 100 mD 10 mD 1 mD 0.1 mD
Resistivity
Very Good Good SomeFair Low Very Low
Porosity
30 pu 20 pu 10 pu 5 pu 0.5 pu
Very WellSorted
Well Sorted ModeratelySorted
Poorly Sorted
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Grain Size Analysis
SandSiltySand
SandySilt
Silt ClayeySilt
Clay
Sand Silt Clay
Very Good Good SomeFair Low Very Low
Permeability
1000 100 10 1 0.1
Resistivity
We are essentially mapping the grain size distribution onto the resistivity spectrum.
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So what does this mean?
• By combining images with core and petrophysical analysis, the textural make upof the rock can be incorporated into the computation of porosity and permeability
• By getting a measure of the grain size and the sorting, the Kozeny – CarmanEquation can be applied.
=150 V 0 µ µ µ µ (1 – φ φφ φ )2
ε εε ε s D p φ φφ φ 32 2
∆∆∆∆p L
Where: ∆p is the pressure dropL is the total height of the bedV 0 is the superficial velocityµ is the fluid viscosityφ is the porosityε is the sphericity of the particles in the bedD p is the diameter of the spherical particle
Kozeny – Carman Equation:
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Translating Images into Texture
IncreasingGrain Size
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Translating Images into Texture
IncreasingGrain Size
BetterSorting
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The Result So Far
BHI Data Texture SortingImage
VolumetricsImage
Permeability
ImageProcessing
PetrophysicalEvaluation
ImagePetrophysics
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Core vs. Image
Comparisonof core vs.imagelog forapproximately1.1 M of core.
Core and imageshow similar
features.Lamination seenacross the core
1 .1 M of
c or e
an
d
i m a g e
A p pr ox 2
0 c m
of
c or e
an
d i m
a g e
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GR Core Image Texture Sorting
ImageVolumetrics
NMR/ConventionalVolumetrics
ImagePerm
•The image analysisallows:
•Texture or grainsize index to beestimated
•Better estimation ofvolumetrics
•Sorting indexmeasure
•Quantification ofdistribution of sandsand location ofsands
•Coarsening andfining cycles to be
located.
Core points
2 . 5 M of i m
a g e
Texture Analysis
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Approach
• Neural Network
– Self organized map
– Multiple realizations
• NNT clusters (Flowunits)
– Used to control perm
• Porosity
• Sorting• Kozeny Carmen Equation used
REALIZATIONS
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GrainsizeUses10 divisions fromClay to cobble
P r e d
i c t e d
T r a i n
e d
1
4-5
3
2
Black – Core PermPurple – Coates type permRed – Kozeny-CarmenGreen – High resolution Rt
What about Core?
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Kozeny-Carmen Permeability
• General shape and range is comparable
– Permeability computed for the wells is similar to coremeasurements
• As one would expect the textural mapping from images andclustering is much more defined.
– Sorting and textural map allows a range to be assigned to flowunits.
Image Petrophysics Core
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High Resolution Results
GR Core BHI Texture SortingImage
VolumetricsNMR
VolumetricsConventionalVolumetrics
ImagePerm
Fine grain size on left
Coarse grain size on right
2.5 m
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A wide separation between the curves TEXTBIN20 –TEXTBIN80 at 3070ft – 3992 ft, that indicates mixture of grain size andthe TEXTHOMO curve shows poor sorting index, below 3992ft there is a more homogeneity behavior in the grain siz edistribution
Perforation Interval Identification
Wide separationindicate a mixture of
grains Poor sorting index
Weatheringsection
Homogeneity grain sizedistribution with a mediumsorting index
Wide separationindicates a mixture of
grains Poor sorting index
Weatheringsection
Homogeneity grain sizedistribution with a mediumsorting index
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As an Integration Tool
Predicted Facies
ASCII Loadto DataBase
MDT & PLTDecisions
Model: Core RockType/Flow Units
FractureConnectivity
3D FaciesDistribution
Perm Distribution
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Observations
• Image petrophysics appears to provide textural mean andhomogeneity values that can be related to or calibratedwith grainsize.
• Being able to use core derived sorting, porosity andheterogeneity to predict a mean grain size from boreholeimages and have it match is a useful tool.
• Producing flow units that incorporate sorting andgrainsize permits use of algorithm's that accommodatethese measurements such as Kozeny-Carmen equations.
• The resultant computed permeabilities from image datashow variation that is consistent with what is seen in core.
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Thanks and Questions
I would like to thank the following people for their help and advice:
Paul Kalathingal, Region Manager, Weatherford PetroleumConsulting, Malaysia
Dr Manfred Frass, Region Manager, Weatherford PetroleumConsulting, Latin America
Dr Peter Elkington, Development Manager, WeatherfordGeoscience Development, East Leake, UK
Ruben Martinez, Geologist, Weatherford Petroleum Consulting,Malaysia
And finally, thank you for listeningand any questions