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




Evolution of Bore Hole Image Tools

Baker AtlasStar (1995)

HalliburtonEMI (1994)

SchlumbergerFMI (1991)

PrecisionHMI 2001

WeatherfordCMI (2006)


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




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.


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




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


Visualizing The Conductivity Spectrum

Homogenous MildlyHeterogeneous

VeryHeterogeneous

Conductivity Histograms

Well Sorted Poorly Sorted Bi-modal




Image Tool Resolution

AMZ/AND/ALD = 6”

1”

AcousticImages = 0.4”

ElectricalImages = 0.2”

Analysis & Certainties Are Tool Resolution Dependent

GeneratedVertical

Resolution


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




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


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




• 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


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




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 ? ? ?


Grain Size Analysis

Very Good Good SomeFair Low Very Low

Permeability

1000 mD 100 mD 10 mD 1 mD 0.1 mD

Resistivity


Porosity

30 pu 20 pu 10 pu 5 pu 0.5 pu

Very WellSorted

Well Sorted ModeratelySorted

Poorly Sorted




Grain Size Analysis

SandSiltySand

SandySilt

Silt ClayeySilt

Clay

Sand Silt Clay


Permeability

1000 100 10 1 0.1

Resistivity

We are essentially mapping the grain size distribution onto the resistivity spectrum.


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:




Translating Images into Texture

IncreasingGrain Size


Translating Images into Texture

IncreasingGrain Size

BetterSorting




The Result So Far

BHI Data Texture SortingImage

VolumetricsImage

Permeability

ImageProcessing

PetrophysicalEvaluation

ImagePetrophysics


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




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


Approach

• Neural Network

– Self organized map

– Multiple realizations

• NNT clusters (Flowunits)

– Used to control perm

• Porosity

• Sorting• Kozeny Carmen Equation used

REALIZATIONS




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?


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




High Resolution Results

GR Core BHI Texture SortingImage

VolumetricsNMR

VolumetricsConventionalVolumetrics

ImagePerm

Fine grain size on left

Coarse grain size on right

2.5 m


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



© 2009 Weatherford. All rights reserved.28

As an Integration Tool

Predicted Facies

ASCII Loadto DataBase

MDT & PLTDecisions

Model: Core RockType/Flow Units

FractureConnectivity

3D FaciesDistribution

Perm Distribution


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.




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

fesm image petrophysics final

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