digital rock analysisc203624.r24.cf1.rackcdn.com/numericalrocks.pdf · digital rock analysis...

Alex Mock and Håkon Ruelåtten Numerical Rocks

Digital Rock Analysis

Integrating drill cuttings analysis with subsurface data

April 2011

www.numericalrocks.com

Introduction to the e‐Core technology and pore‐scale modeling

Using drill cuttings as source material

Case studies

Outline


The e-Core Technology


Pore‐scale Modelling ‐ Summary

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0,0 0,2 0,4 0,6 0,8 1,0Sw

Rel

ativ

e Pe

rmea

bilit

y kr

Thin Section 3D Model Core Scale Properties Network Model

Relative Permeability Curve

Capillary Pressure Curve

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1

2

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Sw

J-fu

nctio

n

Micro-CT

Thin sections and micro‐CT prepared from core plugs, drill cuttings or sidewall

samples.

Calculation of two phase parameters such as capillary pressure, relative permeability & resistivity index. Plus

wettability sensitivity tests.

Calculation of petrophysical/single phase parameters such as porosity, absolute permeability, formation factor, NMR, elastic properties, etc.


Thin Section Analysis

porosity cement & clays grain size distribution

Grain Size: binarize image distance tranform stereological correction grain size distribution

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0 100 200 300 400 500

Grain Size (microns)

Cum

. fre

quen

cy

146μm

Qtz

Fsp Clay

Pore


(1) Sedimentation and Compaction: (4) Diagenesis:

Process Based Modeling

(2) Realistic Grain Shapes:

(3) Carbonate Grainstones:


Thin Section Reservoir Rock

2D section in a reconstructed 3D model

Process Based Modeling


MicroCT Imaging

X-ray source

Detector

object

Projection images

CT slices

3D models


MicroCT Imaging


MicroCT Imaging

Bit size: 0.5mm to 10mm

Cutting

Drilling

Impregnation

Sample Preparation


Formation Factor

NMR Relaxation Elasticity

Absolute Permeability

Mercury Injection Pc

Rock Model and Computed Petrophysical Properties


0 0,1 0,2 0,3Porosity

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250

Form

atio

n Fa

ctor

ExperimentalPredictedMCT

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Sw

J-fu

nctio

n

Sim.dataExp1 (AI=0.14)Exp2 (AI=0.57)Exp3 (AI=0.8)Sim.Avg (AI=0.5)

Stress-Strain Vp & Vs Permeability Form. factor NMR relaxation Pc Rel. perm

Solid Matrix Pore Space Pore Network

0 0,1 0,2 0,3Porosity

1E-1

1E+0

1E+1

1E+2

1E+3

1E+4

Perm

eabi

lity

(mD

)

ExperimentalPredictedMCT

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Time (ms)

Nor

mal

ized

am

plitu

deExp. (0.08)Sim. (0.08)Exp. (0.18)Sim. (0.18)Exp. (0.22)Sim. (0.22)

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4.5

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5.5

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0 0.05 0.1 0.15 0.2 0.25Porosity

SimulatedHan (1986)

Overview and Summary

MICP and NMR pore size distributions


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0 0.05 0.1 0.15 0.2 0.25 0.3

Porosity

Perm

eabi

lity

(mD

)

ModelsSub1 1Sub 2Sub 3Sub 4Exp

Heterogeneous at all scales – including the pore‐scale Replace single data points (non‐stationary) with ”stationary” transforms (i.e. cross‐correlations)

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100

1000

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Porosity

FRF

ExpModelsSub1 1Sub 2Sub 3Sub 4

Property Correlations from few samples


0 0.048 0.0625 G

Single Pore

pore sizes pore volumes

intergranular & clay

cross‐sectional shape G = A/P2

Pore Network Extraction for Multi‐Phase Flow


Primary Drainage

kr, Pc, RI, Swi

Waterflooding

kr, Pc, RI, Sorw

wettability effects

Secondary Drainage

kr, Pc, RI, WI

3-phase flow

kr, Pc, RI, Sor

Hysteresis modelling

Multi‐Phase Flow Simulations


w

water film

oil

Primary Drainage

w oil

water wet

oil wetP Cc ow≥ +Πmax σ

Mixed Wet Pore

Aging

w O water

Waterflood

Oil film thickness:Pc,max (i.e. Swi)Pore GeometryAdvancing contact angle

Mixed Wettability Model


Using Drill Cuttings


Use drill cuttings during well drilling to obtain the basic reservoir properties; e.g., absolute and relative permeabilities, capillary pressures, residual saturations, petrophysical characteristics, formation factor, sonic velocities, NMR relaxation.

To start with, we need a representative 3D digital model of the rock, that

has sufficiently high resolution in order to see the connectivity of the pore system, and

contains a ”Representative Elementary Volume” (REV) of the rock.

We have two approaches to obtain such a model:

Reservoir characterization while drilling – a vision


Use of e‐Core

Numerical Rocks Methodologies Two Approaches

Method 1:

Method 2:

PBM*

Image segmentation (Thresholding)

Building 3D rock models

Petrophysical Properties

Pore network extraction

Calculation of fluid flow properties

*Process Based Modelling

Micro CT Micro CT imaging

Thin Section & BSEM


We can use drill cuttings to construct representative rock models:



Drill Cuttings Cleaned and dried cuttings with chip sizes up to several mm original rock texture is well preserved


Cleaned and dried cuttings with chip sizes up to several mm original rock texture is well preserved

Sandstone Cuttings


Carbonate Cuttings


Integrate available data into the modeling process: Logs

Geological Models

Electromagnetic ?

Seismic ?

Use purpose built drill bits for accurate sampling and good samples

e.g. PLATYPUS drill cuttings sampler



Case Studies


Micro‐CT image

2 well consolidated and heterogeneous sample Sample Type

North African oil reservoir Operating field area

Micro‐CT and process based modelling /Anchoring to SCAL data Methodology used

North African Oil Company Client

Two-point Correlation Function - SSWO1

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TPC

TS 1 (avg)TS 2 (avg)TS 3 (avg)TS 4 (avg)M1 (avg)M2 (avg)M3 (avg)MCT (avg)

Examples – Case Study I

SEM image


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krw_LETkro_LETkrw_simkro_simkrw_expkro_exp

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Per

m krw_LETkro_LETkrw_simkro_simkrw_expkro_exp

Relative Permeability Sec. Drainage

Sample SSWO1 Data set Model MCT lab k hor. 1305 1301k vert. 946 966k avg 1185 1189

1508 1442*

FRF 24.6 29.4 ---

1442*

*measured on stacked cores

Relative Permeability Imbibition

– Simulation results match experimental data confirming mixed wettability.

– This physical description of wettability was applied to 12 additional samples without SCAL data.

Examples – Case Study I


Thin section SEM image Models for 3 different micro‐facies

2 consolidated and very heterogeneous clastic sample with several micro‐facies on thin section scale

Sample Type

Middle East oil reservoir Operating field area

Process based modelling for different micro‐facies Methodology used

Middle Eastern Oil Company Client

Heterogeneous Clastic Rocks

Examples – Case Study II


Porosity and permeability of different micro‐facies were averaged according to observed area fractions in thin section

– In spite of sample heterogeneity, petrophysical properties were predicted accurately.

Lab Model

749

0.237

n/a

762 Abs. Permeability [mD]

0.247 Total (”He”) Porosity [frac.]

0.227 Effective Porosity [frac.]

1 Sample

Examples – Case Study II


MCT image (left) and PBM image

Poorly consolidated and heterogeneous sample Sample Type

North Sea Operating field area

Micro‐CT and process based modelling (PBM) Methodology used

Norwegian giant Client

Correlation Function

Porosity Distribution

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PBMMCT

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0 0.1 0.2 0.3 0.4 0.5Porosity

PBMMCT

Comparisons

Examples – Case Study III


12.415.5F_avg

12.613.6Fz

12.416.0Fy

12.217.5FxPBMMCT

12.415.5F_avg

12.613.6Fz

12.416.0Fy

12.217.5FxPBMMCT

PBMMCT PBMMCT828736kx (mD)

731630ky (mD)

778852kz (mD)

779739k_avg

828736kx (mD)

731630ky (mD)

778852kz (mD)

779739k_avg

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Sw

J-fu

nctio

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Sim.dataExp1 (AI=0.14)Exp2 (AI=0.57)Exp3 (AI=0.8)Sim.Avg (AI=0.5)

Capillary Pressure

Relative Permeability

– Simulation results propose AI of ~0.5.

– Relative permeabilities match lab results.

Examples – Case Study III


Examples – Case Study IV

1.E-02

1.E-01

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1.E+01

1.E+02

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1.E+05

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45Porosity [fraction]

Perm

eabi

lity

[mD

]

Sigrun LabSigrun e-CoreNorne LabNorne e-CoreHeidrun LabHeidrun e-CoreGudrun LabGudrun e-CoreStatfjord LabStatfjord e-CoreErmintrude LabErmintrude e-Core

Field 1 exp. Field 1 sim. Field 2 exp. Field 2 sim. Field 3 exp. Field 3 sim. Field 4 exp. Field 4 sim. Field 5 exp. Field 5 sim. Field 6 exp. Field 6 sim.


Examples – Case Study IV

Endpoints from Relative Permeability Simulations – All Fields

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Res

idua

l Oil

Satu

ratio

n, S

orw

(fra

c.)

eCore

SCAL data

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k rw@

S orw

eCore

SCAL data

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Res

idua

l Oil

Satu

ratio

n, S

orw

(fra

c.)

eCore

SCAL data

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Absolute Permeability, k (mD)

k rw@

S orw

eCore

SCAL data


Single sample ‐‐‐ Multiple data sets ‐‐‐ Better solutions

Obtain early reservoir evaluation of a well (e.g. directly from drill

cuttings)

Re‐use of models for multiple fluid flow simulations and for future use

(extremely time and cost efficient).

Sensitivity tests can be easily performed for different wettability

preferences and different irreducible water saturations.

Compare, complement, explain and extend core plug SCAL data

Benefits of Using e‐Core


Facies (mm cm)

Facies (mm cm)

Geo model (cm m) Pore (μm)

Field (km) 1,E-06

1,E-05

1,E-04

1,E-03

1,E-02

1,E-01

1,E+00

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1,E-05

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1,E-01

1,E+00

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1,E-06

1,E-05

1,E-04

1,E-03

1,E-02

1,E-01

1,E+00

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up-scaling # 1

up-scaling # 2

up-scaling 1

e-Core®

Pore‐to‐Field Upscaling


The e‐Core technology is a powerful tool for rock and reservoir characterization. The analysis can provide input data throughout the lifetime of the field; from exploration wells to reservoir characterisation; evaluation of EOR potential and Tail‐end IOR.

Input to the analyses must be rock samples that contain ’a representative elementary volume (REV)’. Drill cuttings may represent REV.

e‐Core analyses based on drill cuttings may provide advanced petrophysical and reservoir parameters for formation characterization ’while drilling’, and thereby

Improve the decision making regarding well completion and production strategy.

Conclusions

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