eni Ss.p.aA. upstream & technical services · eni s.p.a. upstream & technical services Introduced...

eni Ss.p.aA.upstream & technical services

2013-2014 Master in Petroleum Engineering and Operations

Facies Characterization of a Carbonate Reservoir integrating Rock Type, Rock Physics and Log

Facies Models

Author: Federica Colombo

San Donato Milanese, 14 TH October 2014

eni s.p.a. upstream & technical

services 2

Facies Characterization of a Carbonate Reservoir integrating Rock Type, Rock Physics and

Log Facies Models

Author

Federica Colombo

Division eni S.p.A.

Upstream & Technical Services

Dept. GICA/IPET

Company Tutors

Cristiano Tarchiani

Piero Balossino

Antonio Valdisturlo

Alessandro Amato Del Monte

University Tutor

Prof. Francesca Verga

MASTER IN PETR.ENGINEERING & OPERATIONS 2013-2014

Stage Subject


services

Introduction

Conclusions

3

LIST OF CONTENT

Methodology

Application


Log Facies Models


services

Introduction

Methodology

Application

Conclusions

4

LIST OF CONTENT


Log Facies Models


services 5

INTRODUCTION: SCOPE OF WORK

� Review of the existing workflow for characterization of a carbonatic reservoir adding data of a new well to an existing dataset

� Integrated workflow linking core data, log data and elastic properties to provide input for seismic classification to facies probabilities

� Application to a real field in offshore Venezuela


services 6

INTRODUCTION

� Reservoir petrophysical characterization through a Facies model

• Lithologically and petrophysically homogeneous facies

• Qualitative and quantitative analysis

• Integration of data from different sources and scale

(plugs, core descriptions, RCA, SCAL, logs, CPI, …)

� Common approaches in E&P industry

• Cut-off analysis

• Core plug-based Rock Type classification

• Log-based facies classification (by cluster analysis)


services

� Reservoir petrophysical characterization through a Facies model

• Lithologically and petrophysically homogeneous facies

• Qualitative and quantitative analysis

• Integration of data from different sources and scale

(plugs, core descriptions, RCA, SCAL, logs, CPI, …)

� Common approaches in E&P industry

• Cut-off analysis

• Core plug-based Rock Type classification

• Log-based facies classification (by cluster analysis)

7

INTRODUCTION


services

Introduction

Methodology

Application

8

LIST OF CONTENT


Log Facies Models

Conclusions


services

DepositionalFACIES

9

RESERVOIR INTEGRATED PETROPHYSICAL CHARACTERIZATION: WORKFLOW

RRTclassification

RCA

MICP k,Φ

DESCRIPTION DESCRIPTION

PORE TYPES

CLUSTER ANALYSIS

PetroelasticLOG FACIES

00

SEISMICFACIES

scale up

INVERSION

Plugs Thinsections

Cores Logs Seismic

SCAL

scale up


services

METHODOLOGY: ROCK TYPE IDENTIFICATION

10

� Rock Type definition (*)

•“...unit of rock deposited under similar conditions which experienced similar diagenetic processes resulting in a unique porosity-permeability

relationship, capillary pressure profile and water saturation for a given height above the free water level”

� Analysis of core data in order to find an univocal k/φ relationship for each RRT (Reservoir Rock Type)

1. Hydraulic Flow Unit Method: Flow-zone indicator (Fzi)

2. Mercury Injection Capillary Pressure (MICP) analysis: Winland’s method (r35)

3. Power Regression method: based on existing RRTclassifications

(*) Archie, 1950


services

� Introduced by Amaefule et al., 1993

� Based on a modified Kozeny-Carmen equation (1958), relating permeability to porosity and properties of the pore

network

Amaefule suggests to divide both sides of the equation by porosity and take the square root:

11

METHODOLOGY: HYDRAULIC FLOW UNIT METHOD (FZI)

Fzi: Flow Zone Indicator

RQI: Rock Quality Index

φz: Porosity group

RQI Fziφz

φe: Effective Porosity

Sgv: Pore surface area per unit grain volume

Fs: Pore shape factor

τ: Tortuosity


services 12

METHODOLOGY: HYDRAULIC FLOW UNIT METHOD (FZI)

� When the FZI values have been identified, the k/φ equations of each Rock Type are drawn on the k-φ cross-plot

Porosity (fraction)

Perm

eab

ilit

y (

mD

)

1000

0.001

0.40.0


services 13

METHODOLOGY: MICP DATA

p0 = 0

grains

pore throat

p1 > p0

p2 > p1

p3 > p2

Mercury Injection Capillary Pressure analysis

� Mercury is injected into an evacuated sample at increasing pressure steps and incremental injected volume is recorded

� It represents the distribution of connected pore volume accessible by throats of a given size


services

0.001 0.01 0.1 1 10 100 1000

14

METHODOLOGY: MICP DATA

� Results are provided with cross-plots of pore throat diameter vs. normalized pore throat distribution/Hg saturation

� Black curve: % of rock pore volume saturated by mercury at each step

� Red curve: distribution of pore volume accessible by throats of a given size

Pore throat diameter (µm)

No

rm

alized

dis

trib

uti

on

/H

g s

atu

rati

on


1.0

0.001 10001

0.0

1.0

0.001 10001

0.0


services


15

METHODOLOGY: MICP DATA - WINLAND’S EQUATION

� An empirical relationship among porosity, uncorrected air permeability and the pore aperture corresponding to a Hg saturation of 35% (r35) has been developed by Winland (*):

Log r35 = 0.732 + 0.588 log k - 0.864 log φ

(*) Pittman, 1992

d35=0.8, r35=d35/2

35%

No

rm

alized

po

re

thro

at

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 1000

0.0


services 16

METHODOLOGY: MICP DATA - WINLAND’S EQUATION

� When the relationship among r35, k, φ has been established for MICP samples, it is extended to all RCA samples and data are displayed on a k-φ cross-plot

Porosity (fraction)

Perm

eab

ilit

y (

mD

)

1000

0.001

0.40.0


services 17

METHODOLOGY: POWER REGRESSION METHOD

� Based on existing RRT classifications (FZI and/or MICP, …)

� Function that represents the k- φ relationship of each RRT

1000

0.001

0.40.0 Porosity (fraction)

Perm

eab

ilit

y (

mD

)


services 18

METHODOLOGY: CLUSTER ANALYSIS

� Non-supervised, multi step, multivariate statistical classification method for core/log facies identification

� Input data: RCA/SCAL measures (k, φ, Pc, …) - logs -interpreted curves

• training data � reference set definition on cored interval• propagation data � remaining data set classification• contingency analysis � validation of logs classification vs. core

Porosity (fraction)

Vp

Vs

rati

o

2.7

1.7

0.40.0


services 19

LIST OF CONTENT

Introduction

Methodology

Application


Log Facies Models

Conclusions


services 20

APPLICATION: GEOGRAPHICAL SETTING

WELL 4

WELL 3WELL 2

WELL 1 +

-


services

WELL 1WELL 2 WELL 3

W3

W1

W2

21

� Early and Middle Miocene carbonates

� Distally steepened ramp

TOP CARBONATES

TOP SILICICLASTICS

TOP BASEMENT

APPLICATION: GEOLOGICAL SETTING

SW SE


services 22

APPLICATION: GEOLOGICAL SETTING

� Main components:

1. Rhodoliths (RDST)

2. Branching Red Algae (GRST & PKST)

3. Foraminifers (GRST)

4. Corals

macroforaminifers

corals

laminarrhodolith laminar

rhodolithbranchingrhodolith

branchingred algae

1

32macroforams

4

corals

rhodoliths

branchingred algae


services 23

APPLICATION: AVAILABLE DATA

� Pre-existing database: Well 2 & Well 3

• RRT classification based on integration of MICP analysis

(Winland), Flow Unit method (FZI) and Power Regression method

• Pore Type classification from core description

• Depositional facies classification from core description

• Log facies model based on Vp, Vs, density, GR, PHIE and the

previous core classifications

� New well data (Well 4):

• 3 cores � 456.4 ft (thin sections, core descriptions)

• 242 plugs � RCA (k, φ)

• 19/242 plugs � SCAL (MICP)

• Logs (Vp, Vs, density, GR), PHIE


services

APPLICATION: WORKFLOW

24

� MICP analysis of r35 on new data � hard to fit all measures: difficult to include new data in the old classification

NEW RRT CLASSIFICATION

1. Cut-off analysis to identify non-reservoir Rock Types2. FZI method � cut-off � classes of MICP distribution � identified 5

new RRT3. Extension of new RRT classification to all wells

4. Pore Type classification5. Depositional facies classification

6. Comparison with existing log facies classification guided by old RRT (Winland, W2+W3 cores)

Associated with cluster analysis


services

DepositionalFACIES

25


RRTclassification

RCA

MICP k,Φ


PORE TYPES

CLUSTER ANALYSIS


00

SEISMICFACIES

scale up

INVERSION

Plugs Thinsections

Cores Logs Seismic

SCAL

scale up


services 26

APPLICATION: RRT CLASSIFICATION

RRT 1

Porosity (fraction)

Perm

eab

ilit

y(m

D)

1000

0.001

0.450.0

231 - W2

338 - W3

340 - W3

1 - W4

8 - W4

9 - W4

14 - W4

20 - W4

RRT1 - all wells

231 - W2

338 - W3

340 - W3

1 - W4

8 - W4

9 - W4

14 - W4

20 - W4

RRT1 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services 27


RRT 1

RQ

I(R

ock Q

uality

In

dex)

Porosity (%)

10.0

WELL 4

0.0 0.35

0

8

231 - W2

338 - W3

340 - W3

1 - W4

8 - W4

9 - W4

14 - W4

20 - W4

RRT1 - all wells

231 - W2

338 - W3

340 - W3

1 - W4

8 - W4

9 - W4

14 - W4

20 - W4

RRT1 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services 28


WELL 4: FREQUENCY HISTOGRAMFreq

uen

cy

FZI classes

2.7

1

0.40.05

� Flow Unit Method: • Computation of frequency histogram of FZI for each well


services 29


� Flow Unit Method: • Computation of frequency histogram of FZI for each well• Common cut-off definition• Analysis extended to Well 2 and Well 3

WELL 4: FREQUENCY HISTOGRAM

0.1

0.21

0.32

0.51.25

Freq

uen

cy

FZI classesRRT2 RRT3 RRT4 RRT5 RRT6


services 30


� Cut off of FZI used as reference to classify pore throat distribution curves from MICP analysis

� Results: 5 RRT identified

� Classification extended to W2 & W3

R

R

T

2

R

R

T

3

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n

Pore throat diameter, µµµµm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


R

R

T

5

R

R

T

4

R

R

T

6

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rma

lized

Po

re th

roat

dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n /

Hg

sat

urat

ion


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atu

ratio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rma

lized

Po

re th

roat

dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n



services

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rma

lized

Po

re th

roat

dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n /

Hg

sat

urat

ion


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roa

t dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000N

orm

aliz

ed P

ore

thro

at d

istr

ibut

ion

/ Hg

sat

urat

ion


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


31


RRT 4RRT 4

104 - P2

195 - P2

304 - P2

109 - P3

258 - P3

89 - P4

98 - P4

136 - P4

221 - P4

RRT4 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


104 - P2

195 - P2

304 - P2

109 - P3

258 - P3

89 - P4

98 - P4

136 - P4

221 - P4

RRT4 - all wells

Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services 32


RRT 4

Porosity (fraction)P

erm

eab

ilit

y(m

D)

1000

0.001

0.450.0

104 - P2

195 - P2

304 - P2

109 - P3

258 - P3

89 - P4

98 - P4

136 - P4

221 - P4

RRT4 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


104 - P2

195 - P2

304 - P2

109 - P3

258 - P3

89 - P4

98 - P4

136 - P4

221 - P4

RRT4 - all wells

Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rma

lized

Po

re th

roat

dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n /

Hg

sat

urat

ion


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roa

t dis

trib

utio

n / H

g s

atu

rati

on


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000N

orm

aliz

ed P

ore

thro

at d

istr

ibut

ion

/ Hg

sat

urat

ion


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.001 0.01 0.1 1 10 100 1000

No

rmal

ized

Po

re th

roat

dis

trib

utio

n / H

g s

atur

atio

n


33


RRT 6

0.001 0.01 0.1 1 10 100 1000Pore throat diameter, µµµµm

10 - P2

43 - P2

47 - P2

66 - P2

73 - P2

9 - P3

11 - P3

12 - P3

20 - P3

27 - P3

31 - P3

37 - P3

40 - P3

52 - P3

57 - P3

62 - P3

65 - P3

339 - P3

RRT6 - all wells


10 - P2

43 - P2

47 - P2

66 - P2

73 - P2

9 - P3

11 - P3

12 - P3

20 - P3

27 - P3

31 - P3

37 - P3

40 - P3

52 - P3

57 - P3

62 - P3

65 - P3

339 - P3

RRT6 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 1000

0.0


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services 34


RRT 6

Porosity (fraction)P

erm

eab

ilit

y(m

D)

1000

0.001

0.450.0


10 - P2

43 - P2

47 - P2

66 - P2

73 - P2

9 - P3

11 - P3

12 - P3

20 - P3

27 - P3

31 - P3

37 - P3

40 - P3

52 - P3

57 - P3

62 - P3

65 - P3

339 - P3

RRT6 - all wells


10 - P2

43 - P2

47 - P2

66 - P2

73 - P2

9 - P3

11 - P3

12 - P3

20 - P3

27 - P3

31 - P3

37 - P3

40 - P3

52 - P3

57 - P3

62 - P3

65 - P3

339 - P3

RRT6 - all wells


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 1000

0.0


Norm

ali

zed

dis

trib

uti

on

/H

g s

atu

rati

on

1.0

0.001 10000.0


services

DepositionalFACIES

35


RRTclassification

RCA

MICP k,Φ


PORE TYPES

CLUSTER ANALYSIS


00

SEISMICFACIES

scale up

INVERSION

Plugs Thinsections

Cores Logs Seismic

SCAL

scale up


services 36

APPLICATION: PORE TYPES CLASSIFICATION

FR

WPP

BIOMOP

PBP

� Approach:

• Thin section description (W4)

• Core description (W2, W3 & W4)

� Results:

• Pore Type family definition▪ Interparticle porosity PBP

▪ Intraparticle porosity WPP

▪ Biomoldic porosity BIOMOP

▪ Microporosity GEN_MICROP

▪ Vuggy porosity VUGP

▪ Fracture related porosity FR

• Logs of Pore Type


services 37

APPLICATION: PORE TYPES CLASSIFICATION

� Approach:

• Thin section description (W4)


� Results:

• Pore Type family definition▪ Interparticle porosity PBP

▪ Intraparticle porosity WPP

▪ Biomoldic porosity BIOMOP

▪ Microporosity GEN_MICROP

▪ Vuggy porosity VUGP

▪ Fracture related porosity FR

• Logs of Pore Type


services 38

DepositionalFACIES


RRTclassification

RCA

MICP k,Φ


PORE TYPES

CLUSTER ANALYSIS


00

SEISMICFACIES

scale up

INVERSION

Plugs Thinsections

Cores Logs Seismic

SCAL

scale up


services

� Approach: • Core description (W2, W3 & W4)

� Results: • Depositional facies classification

▪ 8 facies defined in W2 & W3▪ W4 has a new facies: FACIES 9

39

APPLICATION: DEPOSITIONAL FACIES CLASSIFICATION

�


services

� Approach:


� Results:

• Depositional facies classification

▪ 8 facies defined in W2 & W3▪ W4 has a new facies: FACIES 9▪ FACIES 1 (rhodolithic RDST)

▪ Best quality in W2 & W3 ▪ Rare, thin and with poorer properties in W4

• Definition of a continuous sedimentologicalfacies log in W4 for comparison with W2 & W3

40

APPLICATION: DEPOSITIONAL FACIES CLASSIFICATION


services 41

DepositionalFACIES


RRTclassification

RCA

MICP k,Φ


PORE TYPES

CLUSTER ANALYSIS


00

SEISMICFACIES

scale up

INVERSION

Plugs Thinsections

Cores Logs Seismic

SCAL

scale up


services

� Log facies calibrated on W2 and W3 andpropagated to W4 (RCA & facies not yet available)

� Log facies model from cluster analysis

• TRAINING SET � VPVS, AIMP, DTCO, RHOZ, NPHI, GR, PHIE

• ASSOCIATED LOGS � DEPOSITIONAL FACIES, PORE TYPES,

PRE-EXISTING RRT (W2, W3) CLASSIFICATIONS

• Starting from 25 classes

� Results:

• Log facies definition � 5 log facies

APPLICATION: PETROELASTIC LOG FACIES CHARACTERIZATION

0

0.001

45

1000

Porosity (%)

Perm

eab

ilit

y(m

D)

42

2.1

2.9

12040 DTCO (µs/ft)

Den

sit

y

(g

/cc)

2.6

1.6

IP (FRMB)

Vp

Vs

(FR

MB

)

160007000


services 43

� Both sets have been re-grouped to optimize the correlation

� Anyway, the correlation is poor, especially for the best RRT (5&6): the highest frequencies are not restricted to the matrix diagonal

Log facies


Log facies

Rock Types

Porosity (fraction)

Perm

eab

ilit

y(m

D)

Rock Types

1000

0.001

0.400.0


services 44

� Log facies well defined considering acoustic impedance (PIMP)

� Facies have a wide range of VpVs ratio � can be optimized


Vp

Vs

(FR

MB

)

IP (FRMB)

2.5

1.7

160006000


services 45

� GAMMAK (*) � frame flexibility factor

� Describes the flexibility of the rock frame due to the pore structures

� Computed from a Rock Physics model (EBT, Extended Biot Theory)

� Different values of GammaK in the same log facies


High GAMMAK ��

weak rock frame

Low GAMMAK ��

stiff rock frame

Vp

Vs

(FR

MB

)

IP (FRMB)

2.5

1.7

160006000

200

(*) Sun, 2000-2004-2011


services 46

� FK (*) � frame stiffness

� Describes the stiffness of the rock frame due to porosity and pore network

� Computed from a Rock Physics model (EBT, Extended Biot Theory)

� Different values of FK in the same log facies


Low FK ��

low rock strenghtHigh FK ��

high rock strenght

Vp

Vs

(FR

MB

)

IP (FRMB)

2.5

1.7

160006000

200

(*) Sun, 2000-2004-2011


services 47

LIST OF CONTENT

Introduction

Methodology

Application


Log Facies Models

Conclusions


services 48

CONCLUSIONS

� New RRT classification to include W4 showing different petrophysical characteristics and facies

� Pore Types and Depositional facies classifications extended to W4

� Analysis of existing Log facies classification versus new RRT:

• Poor correlation between log facies and new RRT• (checked using contigency analysis)

• VpVs ratio not well discriminated

• Variations in GammaK and FK values within the same log facies


services 49

CONCLUSIONS: WAY FORWARD

� Requirements for a new log classification:

• Changing some input logs in the training data

• Taking more into account VpVs ratio

• Accounting for new RRT classification

• Considering elastic parameters (GammaK, FK)


services 50

ACKNOWLEDGEMENTS

I would thank Eni Upstream & Technical Services

Management for permission to present this work and

related results and colleagues of GICA dept.

Special thanks to EACH ONE OF MY TUTORS

for the technical support.

THANK YOU FOR YOUR ATTENTION !

eni Ss.p.aA. upstream & technical services · eni s.p.a. upstream & technical services Introduced...

Documents

Transcript of eni Ss.p.aA. upstream & technical services · eni s.p.a. upstream & technical services Introduced...