Enhanced Reservoir Description: Using Core and Log

Society–of Petroleum Htgkteere
SPE 26436
Enhanced Reservoir Description: Using Core and Log Data
to Identify Hydraulic (Flow) Units and Predict Permeability in
Uncored Intervals/Wells
Jude O. Amaefule” and Mehmet Altunbay*, Core Laboratories; C)jebbarTiab*, U. of Oklahoma; David G Kersey
and Dare K. l(eelan”, Core Laboratories
“SPE Member
n
ik
Copyright 1993, Soctely of Petroleum Engineers. Inc.
Thie paper wae prepared for preaenlaoon al the 681hAnnual Techmcal Conference and ExhlbOion of the Soc,ety of Petroleum Engineers held in Houston. Texas, 3-6 October 1993.
Tms papar was aelecled for pree8ntaIion by m SPE Program Commmee foltowing review of Inlornratmn comamed in an abstract submmed by the author(s). Conlanta of the paper,
as preaanted, hava not been reviewed by tha &clely of Petroleum Engmeere and are subject to correction by tha author(s). The ma!ercel,ae presenled. does not necassarlly reflect
any position of the Society of Petroleum Engineere, tte off[cere, or membere. Papere presented at SPE meetinge are subject to publication review by Edltonal Commnteea of the Society
of Petroleum Englnaers, Permswon to copy is restricted to an abetracl of no! more than 300 worde. Illustrabonamay not ba copied. The abstract should contain conspicuous acknowledgment
of where and by whom the paper la presented. Write Llbranan, !3PE. P.O. Box 833836, Rlchardaon, TX 75083.3S36, U.S.A. Telex. 163245 SPEUT.
Abstract
Understanding complex variations in pare geomet~ within
different Iithofacies is the key to improved reservoir description
and exploitation. Core data provide in~ornration on various
depositional and diagenetic controls on pore geometry.
Variations in pore geometrical attributes in rum, de$ne the
existenceof distinct zones(hydraulic units) with similar f?uid-jlow
characteristics. Classic discrimination of mck types has been
based on subjective geological observations and on empirical
relationships between the log of permeability versus porosity.
Howevec for any porosity within a given mck type,permeability
can vary by several orders of nragnitnde, which indicates the
existenceof severalflow units.
In this papec a new, practical and theoretically correct
methodology is proposedfor identi$cation and characterization
of hydraulic units widtin mappable geological units (facies). The
technique is based on a modified Kozeny-Carmen equation and
the conceptof mean hydraulic raditis. The equation indicatesIhat
for any hydraulic unit, a log-log p!ot of a “Reservoir Quality
versus a
index,” (RQI), which is equal to 0.0314 ~.
“Normalized PorosityIndex” (+=) which is equal to WI-W should
yield a straight line with a unit slope. 7he intercept of the unit
slope line with +Z = 1, designated as the “FIow Zme Indicator”
(M), is a unique parameter for each hydraulic unit. RQI, 4, and
FZI are based on stressed potvsity and permeability data
measuredon core samples.
—
FZf is then correlated to certain combinations of logging tool
responses to develop regression models for permeability
predictions in coredandiotcored intervals or wells. ?’?te
proposed
techniquehas been successfullytested in c/astic rocksfrom East
Texas,SouthAmerica, WestAfrica, South East Asia and Far East
Asia, as well as carbonate sequences frvm West Texas and
Canada. This paper documents the theoretical development,
validates and characterizes the hydruulic mrits, and presents
predicted versus actual permeability data to demonstrate the
eficacy of the proposedtechnique.
Introduction
One of the most important c.xisting and emerging challenges of
geoscientists and cnginccrs is 10 improve rcsmvoir description
techniques. It is well nxognized that improvements in reservoir
description will reduce the amount of hydrocarbon left behind
pipe. Accurate determination of porwbody/lhroat attributes and
fluid distribution arc central elements in improved reservoir
Many reservoir description programs. though
description.
detailed. have not included descriptions at the po~-throat scale,
Yet. porn-throat attributes control initiallresidual hydrocarbon
distribution and fluid flow. Because they are readily available,
continuous sources of data, Iogging tool responses arc often used
to draw inferences about Iithology. depositional and diagenetic
sequences. and fluid content. These inferences are based on
empirical models utilizing correlations among tool responses,
reek and fluid propmics. in many insumces. unfortunately. the
correlation models can not b used globally kxcauw of the
influences of factors not fully considered by the models. Factors
include (a) the presence of potassium-feldspar, zircon, etc.
causing crrotwously high calculated Vsh from the gamma ray; (b)
microporosity in kaolinite. chert, etc. leading to high apparent
water saturation calculations: and (c) sidentc, pyrite. barite, and
smec[ite influencing the rcsistivity. density and neutron log
20!5
2
ENHANCED RESERVOIR DESCRIPTION: USING CORE AND LOG DATA TO
IDENTIFY
HYDRAULIC
(FLOW)
UNITS
AND PREDICT PERMEABILITY IN UNCORED lNTERVALSAVELLS
calculations, The key to enhanced rc.servesdetermination and
improved productivity is not based on the use of empirical
It is based on the establishment of causal
cordations,
relationships among core-derived. microscopic pore-throat
parameters and log-derived macroscopic attrihutcs.
TIwse
theoretically correct rck+tionships can then be used as input
variables to cafibratc logs for improved reservoir description.
In this paper, a new, practical and theoretically sound
methodology is introduced to identify and characterize hydraulic
units within mappable geologic units (facies). This methodology
uses core data to develop an understanding of the complex
variations in pore geometry wi[hin different Iithofacics. Core data
provide information for various depositional and diagcnctic
Variations in pom geometrical
controls on pore geometry.
attributes, in turn, define the existence of distinct zones (hydraulic
units) with similar fluid-flow characteristics. A hydraulic (pore
geometrical) unit is defined as the rcprwmative
elementary
volume* (REV) “of total reservoir rock within which geolc]gical
and pctrophysical properties that affect f!uid flow are intcmidl y
consistent and predictably different from properties of other rmk
volumes.”2
Hydraulic units arc related to geologic facies distribution. but do
not necessarily coincide. with facies boundaries.3 Tbcrcfom.
hydraulic units may not be vertically contiguous. Hydraulic units
are often defined by (a) geological attributes of tcxtu~.
mineralogy, sedimentary structures, bedding contacts and nature
of permeahllity barriers and by (b) pctrrrphysical properties of
porosity, permeability and capillary pressure.
Statement of the Problem
Classical Permeability-Porosity Relationships
Knowledge of permeability and pcrmetrhility distribution is
critical to effective reservoir description. Several authors4”5’6
have noted the importance of these parameters for planning and
implementation
of completion
strategies for successful
watcrflooding programs and for construction of rcprcsurtativc
reservoir management.
simulation
models for effcctivc
Permeability and permeability disrrihmirm arc usually determined
from core data. However. most wells arc often not cored. As a
result, permeability is estimated in uncorvd sections/wells from
permeability
versus porosiiy relationships that am nftcn
developed from statistically insignificant data sets. In uncrrrcd
wells or zones, empirical pmrcability
is estimated from logderived porosity using Eq. 1.
Iogk =U$+b
SPE 26436
normally distrihukxf pcwunctcrs dots not necessarily establish
On [he classical plot, the relationship bctwccn
causaiit y.
pcrmcahility and porosity is not causal. Whereas porosity is
generally indcptmdcnt of griiin si?x, permeability is strongly
dcpcndcnt on grain sire. For example. in a reservoir. porosily and
permeability may. in general, be directly propotiional. Yet, in [he
same reservoir. thcrw may be tiolh high and low permeability
zones with equal porosity values (F~g. 1). Therefore this
traditional plot can not be u.scd rcliatrly to estimate accura[c
permeability from porosity data. Several invcstigators5”7s8’9”10
have noted the inadequacy of this classical approach and have
proposed altcmativc models for relating porosity to permeability.
From the classical approach it can be concluded that for any given
rock type, the different .porosity/permeabil ity relationships are
evidence of the existence of different hydraulic units. ApparcnUy
~s ] I had ~olnc to ~ similar conclusion about
several invcstigatms -0
porosi[y/pcrmcai. ility rclinionships.
In this paper, a theoretically corrcc[, and fundirmcntally derived
relationship tw[wecn porosity and pcrrmmhility is proposed. The
inffucncc of various geological (dcpositiomd and diagcnelic)
vanablcs that control Iluid flow arc rcflcctcd in [hc proposed
mcthodrdogy.
Fundamental Theory
The hydrwlic quality of a rock is comrollcd by pore gcomelry.
This, in turn, is a fun,:tkrn of mineralogy (i.e.. type. abundance,
morphology and krcatio~ relative to pow throa[s) and texture (i.e.,
Various
grain size. grain shape, sorting. and packing).
permutations of these gcolo.gicitl attributes often indicate the
cxistencc of ~lslincl rock uni~s wilh similar pore throa[ attribute:.
Determination of [hesc pore Ihroat,attributcs is ccrrmd to accurate
zoning of rc!crvoirs in[o units with similar hydraulic propcr(ics.
The mean hydraulic unit radius (rmh) conccpt12 is the kcy 10
unraveling the hydraulic units and rulating porosity, pcrmcahility
and capillary rrcssurc.
rtl,lt =
Cross Scc[ional Area = Volume Open to Flow
(2)
Wcucd Surface Area ‘ “
Wcl[cd Pcrimclcr
For a circular. cylindrical capillary [ubc.
rm/1 =
:’”””””’””””””””””””””””’’”””
‘3)
““’
By invoking the concept of the mean hydraulic rodius. Kozcny13
and Carrncn14 considcrtd the rc.scrvoir rock m be compo.scd of a
hundlc of capillary tubes. They then applied Pois..cuille’s and
Darcy’s Laws to dmivc a rclutionship (Eq. 4) between porosity
and pcrrncahili[y. The primary assumpliuns in their derivation arc
tha! “the I,avcl iimu of a Iluid chxncn[ in a capillary tuhc is equal
to [ha[ in a llEV.”l 5 wrd [hat porosity is cffcclive.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1)
here is apparently no theoretical basis lo support the traditional
crossplot of the Iogarilhm of pcrmcahility versus prmsity.
Permeability is plotted as a log functicm only bccausc it trpfxxrrs
to be log-normally distributed. However. correlation of two
206
SPE 26436
1.0. AMAEFULE,
M. ALTUNBAY,
D. TIAB,
. . . . . . . . . . . . . . (4)
D.G. KERSEY
AND
FZI (pm), designated m [hc Flow Zone Indicator. is given by
The mean hydraulic radius (rmh) can bc related to the surface area
pcr unit grain volume (Sgv) and effcctivc porosity (Qc)as fOllOWS:
Substituting Eq. 5 for rmh in Eq. 4, Kozeny and Carmen ohlaincd
the following relationship
k=
$:
1
(1--$C)2
[1
2t%:r
(6)
““””’”””””””o”””””””””””
where k is in ymz and $Cis a fraction.
The gcncralixcd form of the Kozeny-Carmen rclfl[irmship is given
hy Eq. 7.
k=
$:
1“
(1-0=12
~,+?
[1
(7)
..”
..”.”...””.””,””””
gv
RQI
‘zI=
‘g=~(i%)=i(
+)(s)
where F~is the shape faclor (2 is fora circukrrcylindcr). The icrm
FS# has classically been refwrcd to as [hc Kenny constant. For
ideal, uniform. and unconsolidated rocks, Carmen’4 and
Lcvcrctt16 computed the value of this term to be abou! 5.
However. Row and Brucc17 showed that this term (F,T2) could
vary from 510 100 in ma} reservoir rocks.
Many invcstigalorsz- }1 have attempted to calcuknc pcrmcahilily
from porosity by using Eq. 7. These attempts gcncrall y have not
been successful duc to the uscof a constant value (typically 5) for
F,# and the lack of consideration of Sg,2 in [hew compilations.
In reality, the Kozeny constant is a variable “constant.” which
varies bctwccn hydraulic units, but is constant within a given unit.
‘$,
““’’’’”””””””””””””””(1’)
logRQI = k)g$z-! .logFz
l . . . . . . . . . . . . . . . . . . . (12)
On a log-log plot of RQ1 versus @t,uII samples with similar FZl
values will lie on a straight line n’ith unit slope. %mplcs with
diffcrcnl FZ1 values will lic on other pmdlcl Iincs. The value of
the FZ1constant can hc determined from the intcrccpt of the unitslopc straight line at @z=1. Sun?p/esrhat lie on the same straight
line have similar pore throat attributes and, thereby, constitutea
hydraulic unit.
Alternative relationships thirt yield FZ1 wrlucs similar to those
derived from Eq. 12 hiwc also been dcvclopcd from Eq. 7 as
follows.
If k (red), FZI (!..rm)and @c(fraction). then
3
k =1014
(Fzf)~
e
(LO-)
,
. . . . . . . . . . . . . . . . .. (13)
c
If ORis dc[incd m
~;
‘~= (l-oe)~”
““”’””
. . . . . . . . . . . . . . . . . . . . . . . (14)
[hen
k=1014(FZ12)
@~ . . . . . . . . . . . . . . . . . . . . . . . . .. (15)
Taking the logarithm of bo[h sides of Eq. 15 then results in
~=[&-J~] ....................
Iogk = M (@R) +Iog I I014(FZ12)]
(8)
. . . . . . . . . . . . (16)
A log-log P1OIof k versus ‘$Rrcsuhs in a straight line with a unit
slope and an intcrccpt (at $R = 1) of 1014 (FZI)2.
where k is in ~mz.
However, if permeability is prcscntcd in millidarcics.
following parameter can hc defined:
RQ/(pnl)
&,=
Substituting lhcs: vanablcs into Eq. X and taking the logarithm of
both sides results in
The ismrc of [hc variability of the Kmcny constant is addressed in
the following manner. Dividing both sides of Eq. 7 by porosity
($J and taking the squirre mm of bolh sides results in
3
D.K. KEELAN
then the
= Rcsevoir Quality Index
=0.0314
;
[
tozone the reservoir into
Some invcstiga[ors LIB have ~[[cmptcd
different layers hy using the paramck!r (k/$). Dividing both sides
of Eq. 13 by $ rcsuh$ in
. . . . . . . . . . . . . . . . . . . . . . (9)
e
Taking the logarithm of both sides of Eq. 17 yields
$? is defined as the pore volume-to-grain volume ratio.
$,=
log(;)
& ............................... 10)
()
= 2h@+10g[l
014(FZ02].,,
e
(
e
207
. . . . . .. (18)
*
4
ENHANCED RESERVOIR DESCRJIWJON: USING CORE AND LOG DATATO
IDENTIFY
HYDRAULIC
(FLOW)
UNITS
AND PREDICT
A log-log plot of (k/@) versus I$Z results in a straight line with a
slope of 2 and an intercept of $2 = 1 of 1014 (FZ!)2. As noted
earlier, all samples with similar FZI values &long to the same
hydrauiic unit and will, therefore, lie on the same straight line.
Geological Significance of FZI
Statistical tech~ques, based only on variations in permeability
have been used by previous investigators]9 to zone the reservoir
into layers. The problem is that these approaches ignored
geokrgical attributes that control reservoir zonation.20
The Flow Zone Indicator (FZI) is a unique parameter that
incorporates the geological attributes of texture and mineralogy in
the discrimination of distinct pore geometrical facies (hydraulic
units).
Data*’ displayed on Figs. 2 and 3 dcmonswatc the exccllcrrt
correlation Lwtwcen SW (from capillary pressure). surface area
(from NMR). weight perecnt of grains with sizes less than 30
microns (from wet sieve), and FZX. As expected, SW dwreased
with increasing FZI (Fig. 2). The relationship between SW,and
FZI cart be represented mathematically by the following
expression.
PERMEABILITY
SPE 26436
IN UNCOIWD INTERVALWWELLS
any given homogeneous population are approximmcly normally
distributed. A probability plot of a unimodally and normally
distributed variable often results in a straight line. In contrast, the
existence of multiple. homogeneous subgroups within a given
population often give rise to muhirnodrd distributions and thus
results in multiple straight lines on a probability plot.22
Determination of the specific number of hydraulic units is
constrained by random errors in the porosity and permeability
data used to compute FZI. The magnitude of the random errors
can be estimated by the root mean square technique.23 Relative
random emors in FZI computed from &I. 21 were used to
establish the uncertainty envclo~s around FZI trend Iincs. All
samples with the coefficient of variance (2W2WFZI > 0.5) were
conside~d unreliable and therefore were not used in the hydraulic
unit zonation process. The following parameters were used to
compute (AFZUFZ1)4n Eq. 21: (Ak./k = 0.2; A@.= 0.S% (if 0[ is
trwd instead of $., then A@= l%)).
0.5
2
‘Fy,’)
(—
= *0.5
[
(:)
●
3-@
(rQ)
2
+ (;)
1
. .. (21)
Validation and Characterization of Hydraulic
Units: Case Histories
Confirmation of [he wdidi!y of !he proposed FZI Iechniquc for
identification of dislinct pore geometrical (hydraulic) units is
provided hy using a combination of the following technologicx
where a = 1.12. b = 0.5634. c = 1.44. and ? = 0.998. The
relationship between !$v W (weight percent grains less than 30
microns) and FZI can be represented by the following equation:
●
x.
Y=
Y,,jin+ (Ym=~-yfl,in)
#
●
. . . . . . . . . . . . . . . . . . (20)
●
Mineralogical and textural characteristics of each unit as
dctcrmincd from petrographic data (XRD, FTfR mineralogy,
thin-section photography and SEM)
RQI stress sensitivity
POX throat charac[cnziltion as determined from mercury
injection arrd/or centrifuge capillary pressure.
Values of Ymin. Ymw. and Xmin are summarized in Table 1.
in gene~l, rocks containing authigcnic po~ lining. p~fe fi!iing,
and pore bridging clays as well as fine grained. poorly sorted
sands tend to exhibit high surface area and high tormosity. hence
low FZI (Table 3). In contrast, clean, coarse grained and well
sorted sands exhibit Iower surface areas. lower shape factor. lower
tortuosity, and higher FZI values. In a related fashion. different
depositional envirorrmems and diagenetic processes control pore
geometry and FZI in carbonate rocks.
The si?~ distribution of pore throat radii, dctcrmincd from
capillary pressure data. provides an independent m~’hod to
corroborate the hydraulic unit zonation. The ra[io of macro (r >
1.5 pm) plus mcso (0.5Km c r c 1.5Hm) to micro (r <0.5 pm) is
an excellent dciincator of hydraulic units.
Manipulation of Eqs. 3-7 yields the fundamcnud relationship
between FZI and the mean hydraulic radius (r~h):
log
Hydraulic Unit Zonation Process
A detailed flowchart of the hydraulic unitiwuion prosess is shown
in Fig. 4. l%is process involves the application of climsical
statistical techniques including histogram/frequency diagrams,
normality tests. cluster analysis. and emor analysis for
(r,,,),) = log(FZ/)+log(@$J
. . . . . . . . . .. (22)
Thus,
discriminating the hydraulic units.
A one-dimensional FzI
frequency histogram coupled with a chtssical test for normal
distribution were used to discriminate the distinct family of
As documented by standard statistical
hydraulic units.
textbooks.22 intrinsic and unimodally distributed variables within
Y=mX
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (23)
where X = FZ1. Y = ~’.
+,
208
and m = S1OPC
=&%.
.
9
.
SPE26436
J.O. AMAEFULE, M. ALTUNBAY, D, TIAB, D,G. KERSEY AND D.K. KEELAN
Fig. 5 provides the corroborative cvidcncc of the soundness of the
fundamental theory as depicted by the straight line in [he ccntcr of
theplot ofrm~QZvcrsus FZL l%eslopc isthe productof ~$z.
which is equal to 6.74.
Four typical examples from clastic sequences irr Texas, South
America, South East Asia, and West Africa as well as two
carbonate rock examples from West Texas and Canada are
presented to demonstrate the worldwide applicability of this
proposed technique.
Fieid Examples of Clastic Reservoirs
‘Ikavis Peak Formation, East Texas
The first step in the verification of this hydraulic unit zonatirm
mclhod is to compare the. crossplot of the Iog of permeability
versus porosity (Fig. 1). The log of pcrmeabili[y/porosity
crossplot shows wide dispersion around the regression Iinc (e.g..
permeability varies up to four orders of magnitude for a given
porosity).
The dispersion is decrwscd when depositional
environments am supaimposcd onto the plot, Samples deposited
in the fluvial environment (lower interval) have higher
permeability values forgiven porositics than samples deposited in
the delta plain environment (Fig. 1). Replacement of the general
regression line with regression lines for the upper and lower
intervals reduces the dispersion.
Plotting Ihc distribution of the major diagcnctic clays highlights
[he impac[ of diagcncsis on reservoir properties. Regardless of
the depositional cnvironmcm, the highest permeability values arc
associa:cd with kaolinite, intcrrnediatc pcrmcabilily
values
correlate with chlorite, and the Iowcst permeability values
correlate with illite.
South East Asia
Porosity-permeability data from a ckslic sequence from South
East Mb were suhjectcd to the hydraulic unitization process.
Fig. 10 shows the cxi.wcnce of distinc[ hydraulic units within the
cored interval as dctcrmincd from a log-log plot of RQI versus @z
The different curves depicted on this figure indicate the FZI
average for each of the seven hydraulic units.
Thin section pholomicrographs 24 of samplesfrom the various
units shown on Figs. 11 to 15 substantiate the effectiveness of the
proposed zonation process. Notice the effect of texture and
mineralogy on the pore geometrical quality of the different units,
For instance. comparison of Figs, 11A and 11B show the textural
and mineralogical similarity of hydraulic unit I taken from both
shallow and deep play samples, In contrast, hydraulic unit 2 (Fig.
12) is typified by a very fine to frnc graincd texture and a much
higher clay corncnt ( If)%) than hydraulic unit 1 (5%). Hydraulic
unit 3 (Fig. 13) from a deep play is characterized by a coarse
sihstonc to very fine graincd sandstone wilh a to[rrl clay conlcnt of
21%. while hydraulic uni! 4 (Fig. 14) is a medium grained
siltstone wi!h a highcrc)ay comcnl of 25%. The scvcmh (Fig. 15)
and worst unit is a argillaceous siltstonc with a total clay content
of 34T0.
West Africa
Fig. 16 shows the classical log k versus 1$plot for a typical data set
from the Niger Delta.
seven distinct hydraulic units were
cstablistrcd within Ihc cored intcrwtl by utilizing the proposed
tcchniqr.w, As prtwiously ohscrvcd. the permeability-porosity
rcla(ionship in the Niger Delta is also nonlinear and predictable
(Eq. 13). FZI ranged from 0.3 to WCII over 11 in these
dcpositionalldiagcnctic scqucnccs. The variability of FZI in the
Niger Delta rocks appears to he bolh texturally and
mincmlogically controlled.
A log-log plot of RQl versus $Z for this data .sct(Fig. 6) shows [he
cxislcncc of six distincl hydraulic units within the cored interval.
Each hydraulic unit is charactcriztd by a diffcrm average FZI
value. Tbc influence of diagtmcsis has modified the original
dcpositiorral parameters to give these multiple hydraulic units.
Fig. 17 documcnw the intrinsic pore geometrical chwactcristics
and reflects lhc effect of geological irttlibutcs (Figs. 18A to 18C)
on hydraulic quolity. For cxampic. hydraulic unit 4 (FZI = 4.83),
which is a fine-graincd. moderately well-rounded and well-sorlcd
sand with a low clay con!crrt ( 19’okaolinite) (Fig. 18A). bird the
foilowing distribution of pore throat sizcx macro = 83%, meso =
270, and micro = 1570. Irr contras~ a sample from hydraulic unit
5 (FZI = 3.7). with a port.! throat sixfi distribution of macro = 68%.
mcso = 5%. and micro= 2770. is comprised of laminated. coarse
and fine graincd scqucnccs wi!h angukw, poorly sorted grains and
an intcrmcdiatc clay content (570 kaolinite) (Fig. 18B). The worst
quality hydraulic tmit (!ZZI = 0.4), which is comprised of
laminated, tine graincd. WCII rounded, moderately sorted
sands[onc scqucnccs. had a high clay content (12% kaolinite and
chlorite) (Fig. 18C) and a pore throat size distribution of macro =
22, mcso = 29. and micro= 49%.
South America
Porosity and permeability data gcncratcd on a typical Sou[h
American clastic reservoir rock were u.scd to compute RQI. Oz.
and FZI. A loglog plot of RQI versus @z(Fig. 7) shows five
distinct hydraulic units within the corvd interval. These units
were discriminated by the previously discu.sscd statistical
techniques with the theoretical unit slope consmaint.
Additionally. Fig. 8 shows the classical log k versus $ plot after
the zormtion prmxs. Tbc permeability response equations were
derived from Eq. 13. It is cviden[ from this plot that permeability
is a nonlinear function of porosity. texnrrc and mincridogy. The
differences between the hydraulic UniLS was further vcriticd by
water-oil capillary pressure data and catirm exchwrgc capacity
(CEC) per unit pore volume (Qv). It is evident on Fig. 9 tha[ Qv
decreased with increasing FZI. thus manifcstirrg the effect of clay
mincrak on the rock’s hydraulic qualily.
5’
Porosi[y and pcrme~bility dora determined at muitiplc net
overburden pressures were used to chirractcrii’~ the RQ1 smess
sensitivity of each hydritulic unit. RQI was correlated to net
ovcrhurdcn swcss(rr) by the following rckttionship::s
209
*
ENHANCED RESERVOIR DESCRIPTION: USING CORE AND LOG DATA TO
6
1DENTIF%
HYDRAULIC
(FLOW)
UNITS
AND PREDICT
-b~*
1- EXP(-(
[[
where b~= stress sensitively
faCIOt’. oi =
—))]]
~
IN UNCORED
INTERVALWWELLS
successfully applied to rocks from the UK North Sca and South
East Asia.
fs-cri
=EXP
Y =!%!!
RQli
PERMEABILITY
SPE 26436
. ..(24)
Field Examples of Carbonate Reservoirs
ini[ial stress used for RQ1i,
and c = stress constant (2000-4000 psi).
Canada
The stress sensitivity factor (b,) was further coflelated tO FzL
which was computed from porosity and permeability at inilial
stressconditions, to arrive at the following predictive relationship:
Fig. 20 show: Ihc diswihtstion of FZI wrlucs versus class intervals
for a Canadia~ carhonatc rock. The cxiskmcc of three distinct,
uniformly distributed families of k versus @(Fig. 2 I ) art quite
apparcm on the classical crossplot with mcdiarr FZI vislucs of 1.5.
6.8, and 12.5.
This example clearly dcmonstraks the
applicability of [his tcchniquc to all rock types.
nl
[1
B, (:-l)
0
Y=b$=Al-
Cl(;
. . . . . . . . . . . . . (25)
-l)nh
West Texas
o
The crossplo! of log pcrmcuhility versus porosity shows the
scatter common in mmry carbonate reservoirs (Fig. 22). Zoning
the interval into hydraulic units shows six distinct FZI groups
(Table 3).
The grouping into six units is supported by
independent geological and cngirwcring data. In this example,
petrology and mercury-injection
capillary
pressure were
performed on sclcc[cd samples from cuch hydraulic unit.
For the Niger Delta clastic rocks. the RQI stress sensitivity (h,)
was correlated to =1 with the following parameters Al = 1.39.
X = FZl, X. = FZlmin, B, = 1.31. Cl = 0.96, D, = 5.2. nl = 2J~.
with a coefficient of determination (R2 = .9999). In general. it was
established rJat rocks from the same hydraulic unit (similar FZI)
exhibited similar RQI stress sensitivities (b~). b~decreased with
increasing FZ1. Additionally, rocks with abundant macroporc
throats typically showed lower RQI stresssensitivities (b, < 0.05).
in contrast to microporosity-dominated rocks that showed higher
b~vahses(b. > 0.05).
The dfccts of the dcposiliorml environment on pore throat
attributes were examined, As cxpcctcd in Ptilcozoic carbonate
reservoirs, the overprint of diagcncsis (Tahlc 4) has significantly
ahercd the original dcposi!ional pore/grfiin relationships. The
slight correlation of mottling (i.e.. tioturbation) with FZI shows
[he residual influcncc of the depositional environment on
reservoir quality.
The classical J function is not adcqualc for differentiating [hc
vmious hydraulic units. This is because the J function only
normalizes capillary
pressure with nxqrcct IO porosity.
permeability and tomrosity, but does not include the effect of
surface area, Eq. 26 conlirms this observation.
P,
J—=
[
;
=2k
CJCOS8
>e[
-=—
RQf =
r,,,,,
1
—
JFj
In compitrison. diagcnctic effects arc directly rclalcd to FZ1. An
with FZ1
inverse corrclalirm (F = ().99Y9) of dolomitkdci[c
rcsuhs from the ini!ial diagwrctic development and subscqucm
partial dissolution of dolornitc.
Pore-throat size distributions were calculated from the capillary
pressure dirts. The ristio of macroporc to microporc throat sires is
dirccdy proportional to FZI.
. . . . . . . . . .. (26)
It is for this reason that no global J function appears plausihlc for
norrrdization of capillary pressure.
Therefore. a more gcneralimd water saturation versus capillary
pressu~ model was developed based on the concept of [hc
hydraulic unit.
The proposed relationship (Eq. 27) is WCI1
behaved.
-’Fz’-F2’mJn)Fw,’nEll
Sw = Swr+ (1 -Sw,)e [
. (27)
As FZI tends to FZtmio,SWapproaches 1 as expcctcd. Similarly.
as Pc = Pd, Sw also approaches 1. However. as FZI or p,
approaches large values. SWapproaches [he imeduciblc saturation
s Wr 13g. 19 shows a typical Niger Delta example on the
application of the proposed model. This model has also been
210
Hydraulic Units and Permeability Prediction
from Core and Log Data
A typical Far East Asia example was used to demonstrate the
integration of core and log dala to identify hydraulic units and to
predict Pcrmcahility. A tlowchan of the process is shown on Fig.
23. Initially. the core depth was mirtchcd lo rhc wireline log depth
by comparing [hc totaVspcctrid gamma derived from log and core
data. Next the proposed zonation process was dcvclopcd from
stressed core (porosity and Klinkenberg permeability) data from
(he cored interval. Based on this process. six distinct hydraulic
units were idcmtificd within [hc corvd interval. The result of this
zonation process is shown on the log k versus @plot in Fig. 24. As
cxpcctcd. each pore gcrrmctrical family had similar average r~h
and FZ1 values.
.
SPE26436
J.0, AMAEFULE, M. ALTUNBAY, D. TIAB, D.G. KERSEY AND D.K. KEELAN
Selection of the Wireline Data for the Derivation of
I’Yansform Equations
Environmentally corrcctcd wireline log data were sclcctcd Iwscd
on their ability to reflect the pore space attributes such a.. clays
(tyfx, abundance, location, morphology), porv-throat geometry
and cffcclive porosity. Individual wireline tool responses can not
be directly correlated with the pore-space/throal aurihutcs.
Thcrvfore. a statistical evaluation of the correlation between
wireline data and pore-space/ihroa[ a[trihutcs is pcrfonncd for
each case. Sciccred log data arc then usd as variables in a
wansform equation to generate the permeability protilcs.
A set of environmentally-corrcctcd
wireline logging tool
rc.sponscs(~. @$@t_j.@l),At, Rxo, RI) were rank-correlated to FZI
using Spcarman’s Rho Statistical Tcchniquc.2s
Derivation of Regression Models for FZI and
Permeability
A matrix of known core (FZL RQI id
k) ValUCS and
corresponding wireline log responses is crcatcd for each hydmulic
unit. A logarithmic frltcring of the data is performed 10norrnalii’c
Ihc dala distribution by calculating tfw logarithm of each varhhlc.
Then, a matrix of the logarithms of norrrmlind indcpcndurt
variables and logarithm of the normali?cd dcpendcm variable
(FZI. RQI or k) arc gcncratcd. Quadralic. multilincar, or iincar
modcls26 with modifications are then sclcctcd to form the basis
Tbc transformation matrix is
for [hc transform equations.
standardized by subtracting the mean of each column from the
row variable it.self and dividing the rcsuh by the standard
deviation of that column.
Transform equations for each hydmulic unit wwc dcriw.1 from
the models and assigned to !he cwrcsponding dcplh poinls hascd
on [hc hydraulic unit prolilc.
Qualitative Predictions of Hydraulic Units for the
Uncored Sections
Qualitative predictions for the uncored sections of cauh WC}]arc
performed hy using”a prohfibilistic method with a deterministic
tool. The probabilistic method uxs rclatimrships implicii in the
data to derive rcsulw. It does not require any assumption or
Results urc obtained by statistical
prcdclcrmincd equations.
The deterministic [od Ihtrt complcmcms this
infcrcnccs.
methodology is the hydraulic u “izoticm process that has hem
performed individually
and indcpcndcntly
on each WCII.
Thcrcforc. although it .sccms like a wrwtrdiction in wrrns. the
deterministic approach (i.e.. lydrarrlic uniw protilc t)f the c[wcd
.scctions) cnhmrccs the rcliafMity of the prixliclions of a
probahllistic
mol. bccwr.sc it provides a rcliahic
and
Prohabililics arc
mathematically sound infermrcc dwubaw.
7
The discrimination matrix crcawd from the control WCII(S) is used
as a rcfcrencc SCI (a set of histograms t’or every log for each
hydraulic unit) for the ncighhoring uncorcd WCII(S)to dcucrminc
the probability of having the same hydrirulic unit in a given
prediction window. This then forms the basis for the qualita[ivc
predictions of hydraulic uniL$ in an uncorcd WCI1.
Fig. 25 shows the output of a ciIse study using lhc proposed
technique on a Far East Asia WC1lin a typical logging trace wi[h
eight [racks. The hydraulic unit numbers smrting from Lhebest to
the worst are a..signcd 10the extreme righl [rack (track 8). In the
first WCII, the interval from X420 to X5(H) feet was cored. Data
from X420-X472 were used to develop the hydraulic unit tracks
and regression models from wireiinc logs. Hydraulic units were
subsequently prcdictcd for the seclion X472 to X500 and
permeability dctcrmhrcd from the regression models for [hose
units. Wiihout hydraulic unitization, pcrmcahiii[y predicted from
wireline logging tool response through the classical muhilincar
correlation tcchniquc shows significant deviations from actual
measured vahres with;=
.22 (Fig. 26). In contrast. pcrmcabilily
prcdictcd from the mme logging tool responses after hydraulic
zonalion (Fig. 27) ex~ibits cxuclh!nt correlations (~> .999) with
the actual measured values in the cored intcrvcd. Fig. 28 comptrms
the pcrrrwability prcdic[cd from the classical log permcahiiity vs.
porosity crossplot 10 pcrnmbility
derived from logging 1001
responses after hydraulic unitization using actual mcmurcd
permeability values in another interval. As is evident on this PIOL
the clmsical log Pcrmcabili(y versus porosity com?kition
undcrcs[imatcs permeability for k > 50 md in contrast to the
propo.wd hydraulic unit controlled Iog-derived Pcrrrwahilily.
In a .secmrd uncrnd ‘wcII. the proposed prc>ccsswirs uxd to
predict lhc hydraulic unit prolih!s and to assign the regression
cquutions for Pcrnwmbility dwcrrninistion based on logging
piiramcwrs (Fig. 29). This hydrmlic unit process also hw been
successfully applied in the Norwrgiiin Notih Sea for pcrmcahility
prediction.zs More extensive lhcowticid dwwlopmcnts dealing
with cla..lic, corhmratc wrd naturally frticturvd mcrvoirs can be
found in liitb.z’)
Conclusions
A ntw. practical imd lhcorclicislly-ha.wd rechniquc hts hcwr
dcvelopt!d to identify md chwaclw’i?.c uniL$ with similar pore
throat gcomctriuisi isiwihutcs (hydfiiulk uniw). This hxhniquc has
a wide variety of practicirl Iicld applications for both cored and
uncomf wells. ‘tksc include:
* Improvctl prediction of pcrmcahilily ml pcrmc~hilily
distrihu[ions from wirclirw logs in partially cored/uncorcd
in[ervals and adjactml wells
computed from cstimalcd distrihu!irms of each hydrmdic unit by
application of the Baycs Theorem.27 Level hy Ievcl, transition
matrices were crcatcd and solved to eslahlish a vertical
association Iretwccn hydraulic units. Details of the prohabilislic
techniques am WCIIdodumcnlcd.27
211
●
Imprmwd well-to-well rock propcriics corrckitions for
rcfmemcn! of pclrophysicid models
●
ForccwL$ reservoir rock quality (and forrmion damw?c
pottmtial) in partially cordluncored WC1lSfor improv;d
complctirms and cnhwrccd recovery decisions
8
ENHANCED RESERVOIR DESCRIPTION: USING CORE AND LOG DATA TO
SPE 26436
IDENTIFY HYDRAULIC (FLOW) UNITS AND PREDICT PERMEABILITY IN UNCORED INTERVALWWELLS
●
Provides a unique parameter, the Flow Zone Indicator (FZI)
for delineating the number of Iaycrs (hydraulic units)
required for assignment of geological and pewophysical
parameters in numerical simulators.
5.
Sli!cs, J.H., Jr. and HuItllz, J.M.: “’TIM usc of Routine and
Special Core Analysis in Characterizing Brent Group
Reservoirs. U.K. North Sea.” SPE 18388 (1988).
6.
Chopra, A. K.. Swin. M.H. and Adur, J.C.: “Dcvclopmcnt of
Reservoir Dcscripticm to Aid in Design of EOR Project;’
SPE 16370,prmemcd at the SPE California Regional
meeting, Wn[ura. Californiir (1987),
7.
Dorfman, M. H.. N~wcy. J.J.. and Coa[cs, G. R.: “NCW
Techniques in Lithofacics Dctclminution and Pcrmrwbility
Prediction in Carbona[cs using Well Logs” (1990) 113-120,
in Geological Applications of Wireline Logs, A. Hurst, M .A.
Lovell and A,C. ~orton, Eds., Geological Society Special
Publication No, 48.
8.
Dubrule. 0. and Haldorsen. H. H.:
“Geostatistics for
Pcrmcabilily Estimation,’” Reservoir Charac/erizatiorr, L.W.
Lake and H,B. Canwll, Jr. (tds.). Academic Press (1986)
223.247.
9,
‘llmur, A.: “An $nvcstigation of Permeability. Porosity and
Residual Water Saturation Rclationships;” Trans. SPWLA
Ninth Annual Logging Symposium (1968) Paper K.
The proposed hydraulic unit process has been successfully
applied worldwide for both clastic (East Texas. South America,
West Africa, South East Asia and Far East Asia) and carbonate
(Canada and West Texas) rocks.
Nomenclature
stress sensitivity factor
critical stress constant (2000-4000 psi)
shape factor
Kozeny constant
permeability (pm*)
pore throat radius (~m)
mean hydraulic radius Qtm)
Reservoir Quality Index (Km)
overburden stress (psi)
initial overburden stress (psi)
surface area per unit grain volume (~m’1 )
water saturation (fractional pore VOhUtICJ
irreducible water saturation (fractional pore volume)
effective porosity (fraction bulk volume)
reduced porosity index
pore vohrme-to.grain volume ratio
tortuosity
10.Wcndt, W.A.. Sakurai. S. and Nelson, P.H.: “Pcrmeubility
P[cdiction from Well Logs Using Multiple Regression,”
Reservoir Charccterizarion, L.W. Lake and H.B.Carroll, Jr.
(tds.), Academic Press (1986) 181-221.
11.Slat[s, R.M. and Hopkins, G. L.: Scaling Geologic Rcsu-voir
Description to Engineering
(Fdwuary 1990) 202-211.
Acknowledgments
The authors thank Core Laboratories Division of Westcm Atlas
International for permission grarrted for the publication of this
manuscript. Additional thanks are due to Cynthia Philipson for
technical editing and assistance in figure pmparatirm and to
ShclIey Barnetl for dcdicatcd efforts in the preparation of this
manuscript.
iWcds,” JPT. v. 42, No. 2
12. Bird, R.B.. Stcwwc, W.E. and Lightfoot.
Phenotnena.Ncw York, Wiley ( 1960).
E.N.:
Trunspm?
13. Koizcny. J.: “Uhcr Kapillarc Lcitung dcs Wasscrs im Bodcn.
Sitr.ungsberichtc.” Royid Academy of Science, Vienna, Proe.
Chss 1 (1927) V. 136,271-306.
14. Carmen, P.C.: “Fluid F1OWthrough Granular Beds.” Trans.
AIChE (1937) V. 15, 150-166.
Iteferences
1.
Bear. J.: Dynamics of Fluids in PorousMedia, New York.
Elsevicr ( 1972).
Errhancrd Oil Recovery. Prcnticc
15. Lake, L. W.:
Englcworrd Cliffs, Ncw Jersey ( 1989).
2.
“Jntcgratcd Approach [o Reservoir
Ebanks W.J.. Jr.:
Description for Engineering Projec~V (abstract only) AAM
Abstract Flow Unit Concept.
16. Levcrctl. M. C.: ‘“Cispillary Behavior in Porous Solids;
Petroleum Tcchnrrlogy. T. P. 1223(1940) 152-169.
3.
Heam, C. L.. Ebanks. W.J., Tyr!, R.S. and Ranganathan. V.:
“Geological Factors Influencing Reservoir Performance of
the Haflzog Draw Field. Wyoming,” JPT v. 36 No. 9 (August
1984) 1335-1344.
4.
Hall.
‘“Evaluation of Capillary
17. Rose. W. and Bruce, W.A.:
Character in Pclrolcurn Reservoir Rock.” Pcwolcum
Transactions. AIME (1949) 127-142.
18. Chor)ra, A. K.: “Reservoir Dcscriplions Via Pulse.” SPE
175;8 Int’1 Meeting on Pctrolcurn En.ginccring. Tlanjin,
China (1988) 171-87.
Shirer, J.A., Lrmgston. E.P., and Strong. R.B.: “Application
of Field-Wide Conventional Coring in the Jay-Lhtle
Escambia Creek Unit; lPT(December 1978) 1774-1780.
194 Twerman. J.D.: “A Slalistical Reservoir-Zonation
nique;’ JPT (August 1962) 889-893; Trans.. AIME.
212
Tech-
SPE26436
J.O. AMAEFULE, M. ALTUNBAY, D. TIAB, D.G. KERSEY AND D.K. KEELAN
‘Ibble 1. Relationship between Surface
Area, Weight Percent
Grains e 30K and FZ121
,
Weight %
Surface Area
Parameter
(WetSieve)
(NMR)
20. Wcbcr’, K.J,and van Gums, L.C.:
“Framework for
Constructing Clastic Reservoir Simulation Mosicls.” JPT v.
42 No. 10 (October, 1990) 1248-1253 and 1296-1297.
21. Z.emanek, J.: “Low-Rcsistivity
Hydrocarbon-Bcm’ing Sand
RescrvoirsV SPE 15713, SPE Middle East Oil Sho!V (1987)
185-97/SPEFE (~CC. 1989) 515-21.
22. Taylor. J.K.: Statistical Techniquesfor Data Analysis. Lewis
Publishers, Inc.. Michigmr (1990).
iind D.K.Keclan: “Stochastic Approach
23. Amaefulc, J.O..
I
I
to
Computation of Uncertainties in Pc(rophysical Properties;’
SCA Paper 8907 (1989) 1-28.
Y
sgv
w
Y max
153.50 m2/g
19.79%
Y~in
16.92 m~lg
13.68
X~i”
O.10pm
0.80 #im
~2
0.95
0.995
24. Barr, D.C, and Altunbay, M.: “Identifying Hydraulic Units
as im Aid 10 Qualifying
Dcposilional Environments and
Diagenetic Facies;’ Geological SW. of Malaysia Symp, on
Reservoir Evaluatiorr/Formation Damage. Kuala Lumpur
(Jldy 11, 1992).
Table 2. MYectsof Ceologicsd Attributes on
Hydraulic Unit Variables
GeologicAttribute
25. Amaefule, J.Cl.. D.G. Kcrsey. D.M. MarshalL J.D. POWCIIS
“Reservoir Dcscnpiion: A
L.E, Valenciaand D.K. Kcclim:
Practical Synergistic Engineering and Geological Approach
based on Analysis of Core Data” SPE 18167 (1988) 1-30.
26. Chatcrjce, Samprit and Price. Bertram: RegressionAnal.vsis
by Exanrp/e. New York, NY, John Wiley and Sons (1977).
D. M., Rodriguez. E. and Anderson. R.L.:
“Estimating Facies and Pctrophysical Parameters from
Integrated Well Data? Paper *. Log Analysis Software
Evaluation and Review (LASER) Symp. Trans.. SW. of Well
Log Analysts, London Chapter. London. England (Dcc. 1315, 1989).
27. Tctzlaff.
28. Cooper, R.D. and Oliveira. A.M.B.:
“Modcm
Core Analysis Manuscript;’
QualitativeEffectsOn
F,
~
S.v
FZI
SwR
r~h
Coarse graincd
L
L
L
H
L
i+
Fine grained
H
H
H
L
H
L
Texture
Coarse graincd. well soticd
L
L
LH
L
H
Coarse grained. poorly
sorted -
H
H
H!L
H
L
Fine graincd. well sorted
M
M
M
M
M
M
High clay content - smcclilc
/illite/chlorite
H
H
H
L
H
L
High clay - kaolinite
M
M
M
M
M
M
Low clay content at
pore throat
- smcctite
H
H
H
L
H
L
blincralogy
“The ~nlcgration of
Core Permeability, Log-Derived Permeability and Measured
Flow Profiles in the Study of Cut-Off Criteria;’ SPWLA
Permeability Seminar (May 18, 1992).
29. Tiab, D.:
I
Intcmal
1
u -- IU“w
M= Medium
H = High
Core Laboratories publication (May. 1993).
*
n.,,
‘z’
=*,
Table 3: Der)ositional Model for a West Texas Carbonate
HU
L?epth(ft) + (%) k (I@
Features
RQI Fzl
1
6436
7.9
21.87
0.522
6.090
faintly mottlc& small anhydrhe patches
2
6390
8.8
6.38
0.267
2.774
faintiy mottled; small to huge anhydnte patches: faintIy to moderately Iaminawd
3
6417
10.1
2.23
0.148
1.313
mottled to m~ive:
4
6491
10.4
1.43
0.116
I .005
massive to faintly laminated; bird’s eye anhydnte: fine stylolitcs
5
6454
8.3
0.37
0.066
0,732
massive to faintly mottled: small anhydritc patches: common allochem ghos~
6
6621
19.4
0.76
0.062 0.258 massive to faintly mottled; small anhydrhc patche~ faint allochem ghosts; healed fractures
small anhydrile patches: faint allochcm ghos~
213
SPE 26436
ENHANCED RESERVOIR DESCRWTION: USING CORE AND LOG DATATO
10
IDENTIFY HYDRAULIC (FLOW) UNITS AND PREDICT PERMEABILITY IN UNCORED INTERVALSAVELLS
‘thhla
.“”.=
HU
Depth
(ft)
A.
w. Slbmmoti.
u.”r-..
-..
fnr
m W@ct
‘llwnc
--“
. ..=.
---”-
Cmrtmnatp
-...
. . . . . .
Crystal Size and Morphology
Cements
RQI
s. M“nrhd
‘. . . . .
. .
.
Other
Pore ~pes
1
6436
0.522
scattered sparry calcite
small patches of anhydrite
tine-mcd. crystalline
avg = 0,06 mm
subhedral to euhcdral
intcrcrystalline
2
6390
0.267
large patches of anhydrite,
some possibly vug tilling
medium crystalline
avg = 0.23 mm
subhedral to euhcdrai
intercrystailinc
3
6417
0,148
patchy sparry calcite
patchy anhydritc
finely crystalline
avg = 0.04 mm
anhcdml to subhedral
intercrystallinc
micmporosily
common allochem ghosts
4
6491
0.116
bird’s eye wrhydritc
minor scattered sparry calcite
finely crystalline
avg = 0,05 mm
anhcdral to subhedral
imcrcrystallirw
possible pin-point
Vugs
allochcm molds
5
6454
0.066 minor scattcrcd calcite&
bimodal crystalline
sim very finely and medium
crystalline
avg = 0.007 and 0.014 mm
anhcdral to suhhcdral
intercrystdlirw
moldic microporosily
common dolomite-rcplaccd
allochcms
allochcm molds
very Iincly to fittcly crystalline
avg = 0.013 mm
anhcdral; minor euhedral
intcrcryslallinc
micmporosily
allochem molds
minor allochem ghosts
anhydrite
6
0.062 anhydrite-healed fractures
6621
minor sparry calcite
I
et upper Interval
01
0.6
@
IE!!!!5
0.4
●
;Int;
0.2
0.001 ~
o
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Poroalty @tfloa.rvolr NOB),frsctlon
0
Fig. 1.
Plot of log permeability versus porosity (uppr and lower intervals).
0
0.5
i
t.s
2
25
FZI
East Texas example;s
Fig. 2.
214
SW,(Capillary Prcssum) versus FZI.2’
●
*
11
J.O. AMAEFULE, M. ALTUNBAY, t). TIAB, D.G. KERSEY AND D,K. KEELAN
S?E 26436
●
t4
vi
0
.-oh
I&
Suoxlllu‘IOB
(Z)lqwluu
I
—
I
L
4----T
.—
—
“
.
.
.
@@-----
:
w
A
u
12
SPE 26436
ENHANCED RESERVOIR DESCRIPTION: USING CORE AND LOG DATA TO
IDENTIFY HYDRAULIC (FLOW) UNITS AND PREDICT PERMEABILITY IN UNCORED lNTERVALS~LLS
*
ai
i?
216
SPE26436
13
J.O. AMAEFULE, M. ALTUNBAY, D. TIAB, D.G, KERSEY AND D,K, KEELAN
!
I
I
I
plu‘Mlqeauuad
217
I
I
k
0
.=
L
I
Fig. iSA.
I
1
100
1000
T
Hydrauiic Unit 4. fine grained
Sandstont
Total Clay = 1%
(West African example)
10
1
PclPd
Fig.
19.
.—
lCWO
a
Fig. 18B.
SW versus nmmidizcd capillary
pr&me fraction (Pc/Pd)
(West African example).
Fig.
20.
l%?]
versus class interval
(Canadian example).
—
10
Hydraulic Unit 5, laminated fine and
coarw graincd sandstorw.
Total Clay =5%
(West African example)
]
—
c
z
n
g
m
u
0.<
.
Fig. 18C.
Hydraulic Unit 6. laminated fine
grained sandstone.
Total Ckty = 12%
(West African example)
am
1 0.02
0
0.2
al
P017MY,
Fig.
21.
acw
O,oa 0.10
Pm2iy.
fracuon
Crossplot of permeability versus
porosity (Canadian example).
0.04
0.!2
a94
0.14
Q*8
020
0
03
Fig.
22.
fladicm
Crossplo~ of permeability versus
porosity for various hydraulic units
(V&s Texas example).
9
.
...
*
.
I
Dqmh
m.l.
h .uw
.nJ
I
Is,&’J.I.
J
ln,m
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[
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u,, ng pr(qwml
-
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m
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,
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1
hydtaultc
cord
envm>nmcm.dly
.,mm
ICJ IW?I’W
For each hydra.ht
log~mp
uml
hydraul,c
h)df.+.l,c
wc hmquc
..csh.. ....
...
u5mg
=,th
I
unit vanah(cs
G2
fcw.w.
%) At. R,.. R,. ct~ )Wlrh ~ “Zi
wm.! SWarman”s RI{( )
.. . . .
m
mclhu 1$ cort%mmcd
dclcnn,nm!K
M
1
untl pn,hlcs
mtcrwW*ells
pr<,hahll,st,c
and ICIUI rc.p,nw%
wattwcal
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,.,
o__ ..-. _..
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nt,, h) Jf..l8t
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.
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Comp.tc
15
J.O. AMAEFULE, M. ALTUNBAY, D. TIAB, D.G. KERSEY AND D.K. KEELAN
SPE 26436
II
I’--4l
.4
UIIM m adjsccnl
wells
I
l.~. -
1
COmpuIc
Ual!mcal
(mean.
mras.!w
,,[ J,.w’, w..
mcdmn. s[d dc.,ah.,
lnggmg
W’MIIICS
r
n)d .!1
and FZI
m
I
Setup mama
mdulnun m <,,rcd WII
w.here XI] = y. ON .0,>
[
Al
4
CMc.law
L =
fut
2
Pm%abd,ly
‘#
llR4[FZ/)2-—
(1-ol~
c==
\d,daw
R... JOJ R,
and map hydniultc
untu
!1
,
;’
I
1
Fig.
23.
Prcdictio~ of hydraulic units and pcrmcahili[y from core and
log dara.
*0000
1,14
low
*OO
001
0001
0
01
02
Ct
Porowry.
;. ..,..
04
ff8cUon
i----Fig. 24.
Crossplo[ of pernlcwhilily versus
porosity for various hydraulic units
(Far Eastern example).
r(,
Fig. 25.
219
PcrmcahiMy prottic prcdiclcd from logging in a
kwwal
(Far E@crn Example).
cord
I
..— ---—
16
.—. —.
---
3P
ENHANCED RESERVOIR DESCRIPTION: USING CORE AND LOG DA 1A 1u
. . .. .
L04.KJ
IDENTIFY HYDRAULIC (FLOW) UNITS AND PREDICT PERMEABILITY IN UNCORED INTERVALWWELLS
“
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8
.
220
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