pore carbonate properties

7/21/2019 Pore Carbonate Properties

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SPWLA 52nd

Annual Logging Symposium, May 14-19, 2011

BASIC LOGS UNLOCK COMPLEX CARBONATE PORE PROPERTIES

Stefan Calvert,BG Group PLCand Gene Ballay,Independent Consultant

Copyright 2011, held jointly by the Society of Petrophysicists and Well Log

Analysts (SPWLA) and the submitting authors.

This paper was prepared for presentation at the SPWLA 52 ndAnnual LoggingSymposium held in Colorado Springs, Colorado, United States, May 14-19,

2011.

ABSTRACT

Many carbonate fields exhibit a high degree of

heterogeneity and structural complexity leading to

challenges in understanding the production performance.

In many cases only triple combo data is available due to

cost and/or operational considerations.

It is generally recognised that an understanding of the

complex micritic pore structure properties of carbonates

is essential to the development of an understanding of

formation properties that impact on production; such as

the effective (flowing) porosity, water saturation either

from resistivity logs and/or saturation height functions,

permeability, relative permeability, wettability and

recovery factors.

Carbonate rock typing from logging data is generally

considered to lie within the realms of NMR and image

log analysis that is calibrated to core data. The

characterisation of the different porosity types present in

carbonates are driven by pore size variation (micro,

meso and macro) which have wide variations in poro-

perm characteristics for samples with the same total

porosity. Similarly, the complex and heterogeneous pore

structures can result in associated problems quantifying

hydrocarbon saturations due to non-Archie resistivity

water saturation relationships.

The application of the Thomeer technique has provided

insights and better production estimates. Integration of

core derived Thomeer parameters with basic logs has

delivered a robust and readily implementable reservoir

property framework. The density-neutron logs canprovide a direct measure of threshold entry pressure thus

allowing simple and reliable rock typing using the basic

logging suite. The resultant rock typing significantly

improved the formation evaluation description for the

full field reservoir modelling.

INTRODUCTION

In many cases the data available to examine field

heterogeneity is limited to only triple combo data due to

cost and/or operational considerations. Core data

therefore is especially valuable to investigate the

possible explanations.

An understanding of the complex micritic pore structureproperties of carbonates was found to be key to the

describing the formation properties that impacted on

production. The study field undertook a petrophysical

re-evaluation based on several factors:

Discrepancies between the fluid distributions based

on the reservoir model predictions and production

volumes implying that the in place volumes and

production characteristics were not understood.

Realisation that textural variations and fractures

within the carbonate formations were prolific and

should be captured in the petrophysical model.

Poor agreement between log and core data.

Geology

The study field consist of three main units: Alternates,

that rapidly alternate from limestone to shale. The A

zone, a tight limestone and shale unit forms ephemeral

local seals for the B zone. The B zone is 50m thick with

excellent continuity and quality. The top 10-15m of B

unit contains the best reservoir rock. The A zone is

~50m thick and comprises 10m zone of tight limestone

with thin marl/shale beds, overlain by moderate quality

limestone reservoir. The tight zone acts as a local

baffle between the gas cap primarily in A zone, and the

oil column which largely resides in the B zone.

The oil column is approximately 20m thick (xx37-

xx57m TVDSS) with a gas cap and a 40-60m water leg.

The gas column is typically 50m thick, but locally

exceeds 130m. The free water level is at xx62m

TVDSS.


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THOMEER MODEL

Thomeer (1960) introduced a pore geometrical factor to

assist the description of saturation-height as defined by

capillary pressure curves. The premise being that

reservoir quality is driven by rock fabric and thereforesaturation-height function must reflect the rock fabric.

Thomeers method (1960) is based on fitting hyperbola

to the logarithm of capillary pressure (Pc) and bulk

volume occupied (BV = !T* Sw, !T= total porosity andSw= water saturation):

[log(BV/BV")] * [log(Pc/Pd)] = log(e-G)

(BV/BV") = e-G*[log(Pc/Pd)]

where Pd is the threshold capillary pressure, BV" is the

bulk volume at infinite pressure (close to !T) and G is

the Thomeer pore geometrical factor.

The advantages of Thomeers formulation are:

Independent from permeability, K

Can be superimposed for multiple pore volumes

Texturally representative

Facies are separable

Permeability, K and irreducible water saturation,

Swircan be predicted as follows

Swir= [eG*log(Pd_macro)

] / !T

where Pd_macro is the threshold capillary pressure of themacro pores. Permeability, K can be calculated using the

Thomeer (1983) or Clerke (2008) methods respectively:

KThomeer= 3.8068 * (BV"_macro/Pd_macro)2* G-1.3334

KEd_Clerke= 10(a + b * log(214/P

d_macro) + c *

!

T)

where a, b and c are the appropriate fitting constants.

Micritic Carbonates

Micritic carbonates typically consist of two or three

separable pore volumes related to the pore size; micro

(5!m). The

Thomeer model is most effective when hyperbolae are

superimposed therefore each pore size can be fitted and

summed to describe the whole pore system:

BVT= BVmacro+ BVmeso+ BVmicroBVi= 10^{-Gi*(log(BV"_i)+[log(Pc)-log(Pd_i)]}

where i is micro, meso and macro respectively.

POROSITY AND SHALE VOLUME

The density-neutron crossplot was used to compute

shale volume, Vsh, assuming linear response equations:

( )( )

( )( )

( )( )

( )( )!

!"

#

$$%

&

'

''

'

'

!!"

#

$$%

&'

'''

'

=

LimeWater

LimeKao

LimeNWaterN

LimeNKaoN

LimeWater

Lime

LimeNWaterN

LimeNN

shV

((

((

))

))

((

((

))

))

__

__

__

_

where !N= neutron porosity, !N_Lime= limestone neutron

porosity, !N_Water = water neutron porosity, !N_Kao =

kaolinite neutron porosity, " = bulk density, "Lime =

limestone bulk density, "Water= water bulk density, "kao

= kaolinite bulk density. Note that XRD data shows the

shale is almost entirely kaolintie in the form of dickite.The fluid parameters were fixed to that of the formation

water ("water=!N_water=1) that provided an excellent match

with XRD data even within the hydrocarbon intervals

and provided a stable solution within the inversion.

The volumes of limestone, Vlime, dolomite, Vdolo, and,

effective porosity, !e were calculated using a material

balance matrix inversion method:

!!!

"

#

$$$

%

&

'

('

('

=

!!!

"

#

$$$

%

&

(((

(((

sh

kaoNshN

kaosh

doloee

doloNdoloeNeflNe

dolodoloeefle

V

V

V

VV

VV

VV

1

_

lim

_lim_lim_

limlim

))

**

)

))))

***)

( )( )

( )( ) !

!"

#

$$%

&

'

'+

'

'(=

waterNkaoN

claydryNkaoN

waterkao

claydrykao

Sh

__

____2

3

1

))

))

**

**)

ShSheT V !!! "+=

where limestone, dolomite, kaolinite and dry clay

density are "lime, "dolo, "kao and "dry_clay respectively

likewise limestone, dolomite, clay and dry clay neutron

porosity are !N_lime, !N_dolo, !N_kao and !N_dry_clay

respectively. Hydrocarbon density, "hc was chosen

appropriately for the oil and gas legs then solved

iteratively for the unknowns. Grain density, "matrix was

calculated by:

( ) ( ) ( )( )

She

hcxoWaterxoekaoSh

Matrix

V

SSV

!!

"!+""!"!

=

#

$$#$$$

1

1


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where Sxo is calculated using the Archie equation

assuming Rmf=Rw,

n

mTxo

mf

xo

R

aRS

1

!

!

"

#

$

$

%

&=

'

where a = 1, m = 2, n = 2. The effective porosity, !e

calculated here is not used in anyway apart from to

calculate total porosity, !T. Effective porosity was

calculated from the rock typing and capillary pressure

model detailed below.

WATER SATURATION

Formation and flushed zone water saturations were

calculated using the dual porosity method, Petricola andWatfa (1995). Rw, m and n values were based on Rw

analysis and SCAL results from two wells.

Effective and total formation and invaded water

saturations were calculated using equation 3, Petricola

and Watfa (1995):

( )

( ) ( )

( )micro

micro

micro

macro

micro

marcom

macro

mircom

micro

T

w

n

w

R

R

S

!

!!

!

!

!

+

+

+

+

"##$

%&&'

(

=

1

)(

1

)(1

1

1

)T

microwe

wT

SS

!

!! +"=

where m(micro) and m(macro) were taken to be the

minimum and maximum measured Archie m values

respectively.

IRREDUCIBLE WATER SATURATION AND

RESIDUAL OIL SATURATION

Irreducible water saturation, Swir, and residual oilsaturation, Sor, were fitted to total porosity derived from

SCAL data.

Swir=1*e-a

!

T

Sor=1*e-b

!

T

Maximum recoverable oil = (1- Swir- Sor)

where a and b are the appropriate fitting constants.

CAPILLARY PRESSURE AND ROCK TYPING

The hyperbolae for each plug of nearly 90 plugs were

plotted on the bulk volume occupied against capillary

pressure plot (Figure 1). A histogram of the threshold

entry pressures for the macro pores, Pd_macro, (Figure 2)provided the classification of the rock types (Table 1).

It is interesting to know that at the basic level, the Lucia

(1995) classes are also based upon variations in

displacement pressure, which Lucia then relates to

crystal size (and poro-perm crossplot position). Thomeer

(1983), however, has the enhanced capability of being

able to simultaneously address multiple pore systems.

Gaussian fits to each of the rock types enabled P10, P50

and P90 estimates to be developed (Figure 1). The

average and standard deviation of Pd, BV", G parameters

for each pore space were analysed to produce the BVP10, P50, P90 data fits.

Rock TypeCapillary Pressure

Range (psia)

0 275

Table 1.Rock typing pressure classes

An observation was made that the P10-90 range of eachPd, BV", G parameter was ~0.5 times the parameter

average. This was utilised to generate the P10 and P90

from the P50 parameters.

The Pd, BV", G fitting parameters were analysed to

develop relationships that allowed the prediction of the

parameters from known/measured inputs !Tand Pd_macro

were taken forward (Figure 3).

(macro and micro) Pd= a * !T-b

(meso) Pd= a * Pd_macro-b

(macro) BV#= c * Pd_macrod(meso and micro) BV#_i= c * !T

d

G_i= e

where i is micro, meso and macro respectively and a to e

are the appropriate fitting constants.


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P10 parameters were calculated as = 1.5 * parameter and

P90 parameters were calculated as = 0.5 * parameter.

Effective porosity, !e is defined as the macro pore space

as this is the only pore space considered to contribute to

flow in this reservoir.

!e= (BV#_macro/BVT) * !T

SATURATION HEIGHT IMPLEMENTATION

Implementation of the saturation height model required

linking Pd_macroto log data allowing log based rock types

calibrated to the capillary pressure data thus allowing

delivering textural representative K, !eand Swestimates.

Most log datasets contained only LWD gamma ray,

resistivity, density and neutron logs. Advanced logswere restricted to a small number of vertical wells.

Density-neutron log separation was been observed to

indicate rock quality irrespective of pore fluid thus

establishing a relationship between density-neutron

porosity separation and Pd_macro(Figure 4):

!density= (2.71 - "b) / 1.71

!dif= !density- !neutron

Pd_macro= a*10-b.

!dif

where a and b are the appropriate fitting constants. Thevariations in mineralogy (calcite dolomite, kaolinite)

were linked to the rock quality variations. High kaolinite

and/or dolomite found in the poorer facies but due to the

close values of the photoelectric factor for example both

~3B/e, a photoelectric factor method was unsuitable for

implementation.

In combination with !T from density-neutron logs and

height above free water level the capillary pressure, Sw

(BV/!T), !e, Swirand K could be calculated.

Wireline implementation and comparison with SCAL

data was performed on a number of wells with therelevant core data sets (Figure 5). Note the log based

rock typing (curves) are most well fitted to SCAL data

(stars) and that the log based rock typing is never more

that one rock type incorrect.

Permeability

The relationship between !T and K is compared with

Pd_macro and permeability, K. Note that the stronger

correlation is between Pd_macro and K (Figure 6). An

improved relationship is established by combining !Tand Pd_macro(green) compared with Pd_macroalone (black)

to predict K (Figure 7). P10 and P90 estimates were also

given (Table 2).

KClerke= 10[d + e * log(Pd_macro) + f * log(

!T)]

Parameter P10 P50 P90

d 0 -0.6 -1.2

e -0.59 -0.59 -0.59

f 7.3 7.3 7.3

Table 2.Permeability fitting parameters

Lucia (1995) also notes the strong correlation between

Pd_macro and K specifically that pore throat size, the

largest of which is reflected in the displacement

pressure, is more important than is the porosity. The

Thomeer analyses approach presented here is an

improvement on the usual Lucia model in that it

recognizes simultaneous multiple pore size modes.

Absolute fluid permeabilities were measured as part of

the relative permeability tests at residual oil saturation

and irreducible water saturation. Crossplotting fluid

permeabilities with core Klinikenberg permeability

demonstrated that only simple power trends wererequired (Figure 8). Note that the magnitude of the

Klinkenberg correction is smaller, as permeability

becomes larger.

Relative Permeability

The objective of relative permeability analysis is to

describe the fractional flow of the fluids produced at any

given water saturation observed. In combination with

the rock typing it was possible to estimate fluid fractions

based on fitted parameters by extending Clerkes (2007)

fitting method for oil curves in a water/oil system to all

fluids in the three phase system present in this case.

In addition the concave relative permeability

behaviour observed could be fitted and is due to the

micritic nature of the rock. Fitting concave curves was

possible using exponents


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By normalising water/oil saturation between the residual

oil/trapped gas to irreducible water/residual oil, fitting

parameters were obtain for each of the sample-fluid pair

curves and fractional flow curves (Figure 9). These

fitting parameters were analysed to obtain predictive

trends with absolute permeability (Figure 10).

Permeability thickness and fractional flow could be

derived to assist with production volume and rates

estimates.

Oil/Gas

( )( )( )

( ) ( )[ ]

( ) ( )[ ]gasoilgasoilgasoil

gasoil

Sdoilgas

b

gggasoil

orgt

orwgg

KrKrFF

cecKr

SaKr

SS

SSS

gg

!!+=

"!+=

!=

""

""

=

!

__

_

_

_

1

1

1

1

1

Water/Oil

( )( )

( ) ( )[ ]

( ) ( )[ ]oilwateroilwateroilwater

oilwater

Shwateroil

f

soilwater

orwir

wirws

KrKrFF

gegKr

SeKr

SS

SSS

s

!!+=

"!+=

!=

""

"

=

!

__

_

_

_

1

1

1

1

where SSand Sggare the normalised saturation, Sgtis the

trapped gas saturation, Kris the relative permeability to

the respective fluids, FF is the fractional flow to the

respective fluids and ! are the respective fluid

viscosities and, a to h are the appropriate fitting

functions of the form:

a=x*Ky

a = x*log(K)+y

CONCLUSIONS

The use of an appropriate capillary pressure model

enabled the petrophysical characterisation of a micritic

carbonate field. It was observed that the difference in

density and neutron porosity values was related tothreshold entry pressure of the macro pore system. In

combination it was possibly to extend the water

saturation and permeability calculations to all wells with

triple combo logs in the field.

The Thomeer analyses approach presented here is an

improvement on the usual Lucia model in that it

recognizes simultaneous multiple pore size modes. The

approach also extends Clerkes methods.

The revised approach assisted the understanding of the

hydrocarbon distribution within the field and thus well

planning and recoverable volumes. As a consequence of

the improved description of the matrix behaviour, there

was recognition of the significance of fracture flow to

production.

Production and well test permeability values were an

order of magnitude greater than the maximum

permeability values calculated from the matrix. Fracture

mapping and image analysis was therefore required and

gave rise to the re-processing of the seismic volume for

fracture attributes and an integrated study of fracture

from core and image logs.

ACKNOWLEDGMENTS

The authors wish to thank BG Group for permission to

publish this work. Dr. Tim Pritchard, Head of

Petrophysics (BG Group) is gratefully acknowledged for

reviewing the text and discussing the results.

REFERENCES

Ballay, G., 2008, Monte Carlo modelling with Excel,

GeoNeurale. ([email protected])

Bateman, R.M., and Konen, C.E., 1977, "The Log Analystand the Programmable Pocket Calculator, Part II -

Crossplot Porosity and Water Saturation", The Log

Analyst, November-December 1977.

Batzel, M., and Wang, Z., 1992, Seismic properties of pore

fluids, Geophysics., 57, 1396-1408.

Clerke, E.A., Mueller III, H.W., Phillips, E.C.,

Eyvazzadeh, R.Y., Jones, D.H., Ramamoorthy, R., and


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Srivastava, A., 2008, Application of Thomeer Hyperbolas

to decode the pore systems, facies and reservoir properties

of the Upper Jurassic Arab D Limestone, Ghawar field,

Saudi Arabia: A Rosetta Stone approach, GeoArabia,

Vol. 13, No. 4, p.113-160

Clerke, E.A., 2007, Permeability and microscopic

displacement efficiency of M_1 Bimodal pore systems in

Arab-D Limestone, SPE 105259

Crain, E.R., 1986, "The Log Analysis Handbook, Volume

1: Quantitative Log Analysis Methods", PennWell, ISBN

0-87814-298-3 (v.1).

Kewen, L. and Horne, R.N., 2002, Experimental

verification of methods to calculate relative permeability

using capillary pressure data, SPE 76757

Lucia, F.J., 1995, Rock-fabric/petrophysical classification

of carbonate pore space for reservoir characterization,

AAPG Buletin no.79 v.9, pp1275-1300.

Petricola, M.J.C., and, Wafta, M., 1995, Effect of micro

porosity in carbonates: Introduction of a versatile saturation

equation, SPE 29841

Schlumberger Chartbook, 2009, Schlumberger.

Thomeer, J.H.M., 1983, Air permeability as a function of

three pore-network parameters, Journal of Petroleum

Technology, April, p. 809-814.

Thomeer, J.H.M., 1960, Introduction of a pore

geometrical factor defined by a capillary pressure curve,

Petroleum Transactions, AIME, v. 219, T.N. 2057, p. 354-

358.

ABOUT THE AUTHORS

Stefan Calvert is a Principal Petrophysicist currently

working in Brisbane and for the last seven years withBG Group Plc. Prior to BG he spent four years working

as a Research Petrophysicist for Reeves Wireline

Technologies Ltd (now Weatherford). His interests

include unconventional reservoirs, horizontal well log

interpretation, induction logging, cased hole nuclear

logging, carbonates and thin bed evaluation. Stefanholds a BSc in Physics, an MSc in Geophysics and a

PhD in Petrophysics from UK universities. He has also

published papers with the SPWLA, SPE and EAGE.

Stefan is current President of the FESQ and member of

SPWLA, SPE, IOP and PESGB. Stefan jointly holds a

patent for cased hole density log hydrocarbonevaluation. Outside of work Stefan enjoys swimming,

scuba diving, hiking, snowboarding and travelling.

Gene Ballay served in the U.S. Army as a microwave

repairman and in the U.S. Navy as an electronics

technician, and he is a USPA parachutist and a PADI

dive master. He holds a PhD in theoretical physics with

double minors in electrical engineering/mathematics,

has taught physics in two universities and heldpetrophysical engineering assignments in Houston,

Texas; Anchorage, Alaska; Dallas, Texas; Jakarta,

Indonesia; Bakersfield, California; and Dhahran, Saudi

Arabia.


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Figure 1. Bulk volume occupied against capillary pressure.

Figure 2. Pore throat radius distribution.


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Figure 3. The Pd, BV", G fitting parameters relationships with known inputs !Tand Pd_macro.


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Figure 4. Core threshold entry pressure to wireline density neutron porosity difference calibration crossplot.

Capillary pressure data was available for four wells each represented in a different colour.


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Figure 5. Example log with wireline predicted threshold entry pressure (blue curve) and rock type (magenta curve)

compared to core threshold entry pressure (blue star) and rock type (magenta star).


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Figure 6. Core total porosity (green) and core threshold entry pressure (blue) crossplot with core permeability.

Figure 7. Permeability prediction comparing Clerke (2007) {green} and Thomeer (1983) {black} methods.


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Figure 8.Crossplot of fluid permeabilities with core Klinikenberg permeability.

Figure 9.Fitting parameters to fractional flow curves, gas-oil example. Note that one sample has a concave shape.


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Figure 10.Relative permeability fitting parameters predictive trends with absolute permeability.

pore carbonate properties

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