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 in

sand-shale sequences

• Identification of sand-shale facies and sand thickness

counts

• 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)


Technology Driver

• To generate high-resolution petrophysical properties from Bore-hole Images

• To quantify textural heterogeneity and improve core-

facies predictability and petrophysical facies grouping

• To implement this methodology on all standard industry

image tools

• To extend the discrete single well analysis to a full field

approach

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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 which incorporates 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

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Methodology

• The Bore-hole Image Tool acquires multiple resistivity/conductivity samples per depth interval; high depth resolution

• A distribution of the resistivity/conductivity can be constructed for each interval. Porosity distribution is binned

• The distribution bears remarkable similarity to grain size distribution and can be calibrated to give a mean grain size and grain size distribution map

Matrix Secondary

f

Porosity Distribution0% 100%

Porosity distribution from Archie (relates conductivity to porosity!)

Porosity bin


Visualizing The Conductivity Spectrum

HomogenousMildly

HeterogeneousVery

Heterogeneous

Conductivity Histograms

Well Sorted Poorly Sorted Bi-modal

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Image Tool Resolution

AMZ/AND/ALD = 6”

1”

Acoustic Images = 0.4”

Electrical Images = 0.2”

Analysis & Certainties Are Tool Resolution Dependent

Generated Vertical

Resolution


Image Petrophysics

• Captures radial variability in porosity (Note the core plug distribution – grey dots)

• PHIT from image (calibrated against PHIT from logs)

• Computes high resolution volumes

• HR Volumes and Porosity distribution 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 the same bin indicate well sorted grains. Sort index is higher

Porosities falling in multiple bins indicate poorly sorted grains. Sort index is lower


Permeability

• Permeability Equation:

• KAφeff2

• KA adjusted to match core permeability

• Alternatively:

• KA*φeff2 * 10^(KBφSimg/φPimg)

φSimg: secondary porosity from image

φPimg: primary porosity from image

Ref: Doyen, 1988

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• Predict core facies

• Evaluate lateral and

vertical heterogeneity

variability

• Define core based

stacking cyclicty and

surfaces

• 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 conductivity spectrum from BH Images

Sorting & Grain size Index & Histograms

Grain Sorting Index Log

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 Ray Low High

Sandstone Argillaceous Sst Siltstone Mudstone

X-bedded

Laminated

Massive

Massive-Cemented

Rippled

Deformed

Hetrolithic mudstone

Massive (mottled)

� Higher GR values

my be related to K-

feldspars –need

Spectral GR

� More conductive –

bedded sst higher

permeability

Distribution of conductivity spectrum

Conventional Approach ???

Co

nv

en

tio

na

lA

pp

roa

ch

??

?


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 Well

Sorted

Well Sorted Moderately

SortedPoorly Sorted

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Grain Size Analysis

SandSilty

Sand

Sandy

Silt

SiltClayey

Silt

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 up of 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 – Carman Equation can be applied.

=150V0µ µ µ µ (1 – φφφφ)2

εεεεs Dp φφφφ32 2

∆∆∆∆p

L

Where: ∆p is the pressure drop

L is the total height of the bed

V0 is the superficial velocity

µ is the fluid viscosity

φ is the porosity

ε is the sphericity of the particles in the bed

Dp is the diameter of the spherical particle

Kozeny – Carman Equation:

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Translating Images into Texture

Increasing

Grain Size


Translating Images into Texture

Increasing

Grain Size

Better

Sorting

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The Result So Far

BHI Data Texture SortingImage

VolumetricsImage

Permeability

ImageProcessing

PetrophysicalEvaluation

ImagePetrophysics


Core vs. Image

Comparison

of core vs.

imagelog for

approximately

1.1 M of core.

Core and image

show similar

features.

Lamination seen

across the core

1.1

M o

f core

and im

ag

e

Appro

x 20cm

of c

ore

and im

age

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GR Core Image Texture Sorting

Image

Volumetrics

NMR/Conventional

Volumetrics

Image

Perm

•The image analysis

allows:

•Texture or grain

size index to be

estimated

•Better estimation of

volumetrics

•Sorting index

measure

•Quantification of

distribution of sands

and location of

sands

•Coarsening and

fining cycles to be

located.

Core points

2.5

M o

f imag

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

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GrainsizeUses10 divisions fromClay to cobble

Pre

dic

ted

Tra

ined

1

4-5

3

2

Black – Core Perm

Purple – Coates type perm

Red – Kozeny-Carmen

Green – High resolution Rt

What about Core?


Kozeny-Carmen Permeability

• General shape and range is comparable

– Permeability computed for the wells is similar to core measurements

• As one would expect the textural mapping from images and clustering is much more defined.

– Sorting and textural map allows a range to be assigned to flow units.

Image Petrophysics Core

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High Resolution Results

GR Core BHI Texture SortingImage

VolumetricsNMR

Volumetrics

ConventionalVolumetrics

Image Perm

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 and the TEXTHOMO curve shows poor sorting index, below 3992ft there is a more homogeneity behavior in the grain size distribution

Perforation Interval Identification

Wide separation indicate a mixture of

grains Poor sorting index

Weathering section

Homogeneity grain size distribution with a medium sorting index

Wide separation indicates a mixture of

grains Poor sorting index

Weathering section

Homogeneity grain size distribution with a medium sorting index

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© 2009 Weatherford. All rights reserved.28

As an Integration Tool

Predicted Facies

ASCII Load to Data Base

MDT & PLT Decisions

Model: Core Rock

Type/Flow Units

Fracture

Connectivity

3D Facies

Distribution

Perm Distribution


Observations

• Image petrophysics appears to provide textural mean and homogeneity values that can be related to or calibrated with grainsize.

• Being able to use core derived sorting, porosity and heterogeneity to predict a mean grain size from borehole images and have it match is a useful tool.

• Producing flow units that incorporate sorting and grainsize permits use of algorithm's that accommodate these measurements such as Kozeny-Carmen equations.

• The resultant computed permeabilities from image data show 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 Petroleum Consulting, Malaysia

Dr Manfred Frass, Region Manager, Weatherford Petroleum Consulting, Latin America

Dr Peter Elkington, Development Manager, Weatherford Geoscience Development, East Leake, UK

Ruben Martinez, Geologist, Weatherford Petroleum Consulting, Malaysia

And finally, thank you for listening and any questions

fesm image petrophysics final

Documents

borehole image tool

borehole images

image tool resolutionamzandald

image logsrichard holland

idea of porosity variation

mean grain size

shale facies

petrophysical facies