Effective Integration of Core and Log Data

8/9/2019 Effective Integration of Core and Log Data

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1991 SCA CONFER ENCE PAPER NO. 9102

EFFECTIVE INTEGRATION OF CORE AND LOG DATA

Paul F Worthington

BP Research, Sunbury-on-Thames, UK

ABSTRACT

The pr imary purpose of core-to- log data integrat ion is to reduce the uncertainty associated with

forma tion evalua tion. In so doin g, we take advan tage of both the higher precision of core data an d

the larger scale of investigation of log data. It is particularly important that whe n tying logs ba ck to

core, the calibration algorithm is as well defined as possible.

This paper examines wa ys of optimising the calibration process. A basic requirem ent is the d efinition

of a com mo n reference depth scale. A seco nd requirement is to reconcile the dif ferent v ert ical

resolut ions by the dep th-averaging of core data, the s ignal-enha ncem ent of log data, or both.

Essential to this process is the adoption of "key intervals" as control zones for data integrat ion.

These procedures can result in a reduced uncertainty which transmits through to reservoir appraisal.

Important new developme nts will facilitate the integration of core and log data whe re core recovery is

incom plete. These include the ultrasonic m onitor ing of core entry into the core ba rrel, the m ult i-

sensor scanning of whole core, and the interactive display of both core and log data in interrogative

work-station mode.

Implementation of the optimal data-integration strategy, and of the new upcoming developments, will

require cross-discipline collaboration between those responsible for core acquisition, core analysis

and wireline log interpretation. A key factor will be the ability to develop interpretative algorithms for

cross-scale application.

INTRODUCTION

An important technical goal is to reduce the uncertainty asso ciated with reservoir app raisal. The

attainment of this objective will require a m ore effective integration of the data that c ontribute to the

reservoir model. A key comp onent of the integration process is the recon ciliation of data m easu red

at different scales. A funda me ntal aspect is the integration of core and log data.

The primary purpose of core-log integration is to control log evaluation using data measured under

laboratory conditions. This "tying back to core" usually involves plotting meas ured core values of a

g iven parameter against log-der ived values of the same parameter , a f ter some form of depth

merging. The resulting data distribution is then used to generate a regress ion algorithm wh ich ca n

be applied to log data over uncored intervals. It is reasoned that such an algorithm w ill comp ensa te

for depar tures in the wel l logs due to poor too l design, er roneous shop and f ie ld ca l ibrat ion,

engineer ing error, and imperfect environmental correct ions (Cooke-Yarborough, 1987). Thus we

seek to take advantage of bo th the higher p recision of core data and the larger scale of investigation

of log data.

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1991 SCA CONFERENCE PAPER NO. 9102

This approach presupposes that the core data are representative of the reservoir, in terms of both

sam pling frequen cy and sample integrity. This supposition is most appropriate whe n the form ation is

c l e a n ,

conso l idated, homogeneous, and ver t ica l ly and la tera l ly extensive wi th wel l def ined

bounda ries, and when the core data are free of environme ntal, instrumental and op erational e rrors.

These co nditions are rarely satisfied. Even if the data can be measured accurately and precisely,

there have frequently been doubts about the representativeness of core data, especial ly where

lithology is markedly variable (Marchant and W hite, 1968). A key question concerns the dispa rity in

sample size between core and log data (Enderl in et al., 1991). If this disparity can be redu ced, an

improved data correlation might be achievable.

This paper examines the mechanism of core-log integration and identifies ways in which uncertainty

might be reduced . The aim is an improved data reconciliation for better defined reservoir descriptio n.

The paper therefore focuses on speci f ic issues of reservoir characterization at the mesoscopic

(bedding) scale as discussed more generally by Worthington (1991).

The structure is first to identify prerequisites for effective core-log integration, then to list elements of

an idealized data integration strategy, to discuss briefly how this strategy might be impeded by a poor

understanding of the physics of scaling, and finally to mention some contemporary developments

wh ich would facilitate implementation of the strategy.

PREREQUISITES FOR CORE-LOG INTEGRATION

Matched Depths

Core data are identified by dril led depths, logs by wireline depths. The primary veh icles for depth

merg ing are natural gamm a ray mea surem ents. Core plug locations should be identified on core

gamm a records. The various logging runs should be depth-matched to a comm on wirel ine depth

scale. Thereafter, core and log data should be merge d to a com mon reference de pth scale. The

determination of a reference depth scale is one of the most difficult tasks facing the data integration

specialist. Since the log data are continuou s, and can benefit from excellent dep th control unde r

favourable conditions (Theys, 1991), a possible approach might be to use a wireline depth scale that

has been reconciled with the dri l lers' depth. This merging procedure might not be feasible w here

core recovery is incomp lete and the natural gam ma data are featu reless . How ever, the latter

qual i f ication impl ies a degree of homog eneity that might render the procedure less cr i t ical . An

alternative procedure has been to match core to electrical images of the wellbore obtained using the

form ation microscanner (Ekstrom et al., 1987): this has the additional benefit of core orient ation.

The growing interest in sl imhole continuous coring might lead to opportunities for evaluating and

comparing these various approaches.

Representative Scales of Measurement

Core plugs should be sufficiently large to provide a meaningful average of the pore structure of each

lithological unit or bed that is sam pled. The logs should effectively resolve the beds from which core

has been taken . The beds are presume d to be laterally consistent in terms of physica l properties, at

least to the depth of investigation of the logging too ls. Where they are not laterally ho mo gen eou s,

core-log reconciliation will show more pronounced residual departures (Enderlin et al., 1991) and, in

extreme cases, core and log data might be poorly correlated (Marchant and White, 1968).

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1991 SCA CO NFERENCE PAPER N O. 9102

Equivalent Textures

Core data should relate to samples that have been undamaged by the acquisition, handling and

cleaning proces ses. In particular, routine core analysis practice can induce damage w here interface-

sensitive clay minerals are present (Heaviside et al., Pallatt et al., 1984; 1983; Worthington et al.,

1988 ). Log data should describe zones that have not been dam aged by the dri l l ing proc ess ;

formation damage is, of course, most likely to occur close to the we ll bore.

Comparable Measurements

The sam e parameter should be measured both downhole and in the laboratory if the data are to be

effectively integ rated. For example, electric and induction logs measure horizonta l resistivity at given

frequencies; it is important that the corresponding laboratory measurements on horizontal core plugs

are recorded at similar frequencies to avoid problems arising from the frequency-dependence of

electrical data . Furtherm ore, the correlated data should be reported in the same param etric system .

In the case of porosity, one might use total interconnected porosity or free-fluid porosity, but not one

of each.

ELEMEN TS OF N IDE LIZED D T INTEGR TION STR TEG Y

Key Intervals

Of the various prerequisi tes described above, the ones over which we have no control are the

relative bed thicknes s and lateral homogeneity. We have a choice of core plug sizes and can select

logging tools to sense different volumes around the near-wellbore zone, but we cannot alter the

vertical resolution of logging tools without departing from the standard logging su ite. Even

then,

it

may not be possible to measure the same parameter(s) with a sharper resolution, simply because of

too l ava ilabil i ty. An available option is to restrict the core -log inte gration to the centra l parts of

relat ively th ick beds in the f irst instance . This wo uld al low a "cal ibration" of the log data to be

effected to the exclusion of wirel ine data close to bed bou nda ries. The cal ibra tion sh ould be

restricted to selected key intervals.

A key interval is one which comprises beds whose thicknesses are significantly greater than the

coarsest vertical resolution within the logging suite and which has been fully co red. Algorithms for

tying logs back to core should be establ ished only for key interv als. No other da ta should be

conside red. The criteria for selecting key intervals can be based on Table 1. Th ere, the tool intrinsic

resolution

is an engineering design characteristic (e.g. source-detector spacing),

log sensitivity

is the

minimum bed thickness for signi f icant manifestation in the logging curve where there are non-

extreme contrasts between beds, and log param etric resolution is the minimum bed thickn ess

needed for full manifestation in the logging curve, i.e. the log faithfully records the parametric value

for the bed in question (W orthington, 1990). Bed thicknesses should be several times greater than

the min imum needed for the logg ing too l to ach ieve parametr ic reso lu t ion, i .e . to make a

measurement that is as close to the truth as other environmental factors will allow, subject to perfect

tool operation. Obviously some com promise wil l be needed for the induction log in resistive be ds,

which is justified by, for example, the success of Olson (1986) and Dyos et al. (1988) in reconciling

core-derived water saturations with those determined from deep induction logs.

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Data Characteristics

Table 2 com pares the characteristics of core and log data and indicates which are superior in terms

of continui ty of sampl ing, sample volume, accuracy and precision, measurement condit ions, and

evaluation philosophy. Ideally, we shou ld try to merge the data to derive m aximum benefit from the

collated datab ase . Thu s, we take advantage of the greater accuracy and precision of core data , and

the fact that these data can be interpreted directly whereas log data cannot. On the other h and, by

merging the data, we link these benefits to the continuous nature and larger sampling volume of log

data , and to the ad vantag e that log data relate to in situ con ditions . We shall now con sider th e

integration of core and log data in terms of ea ch of these data ch aracteristics.

Evaluation Procedure

Logs are records of physico-chem ical characteristics which can be measured dow nho le: they are not

records of the reservoir properties we requ ire to know. The data may need to be corre cted for shale

or l ight-hydrocarbon effects. An algorithm is then used to l ink the corrected physical measurem ents

to poros i ty, wate r saturation or possibly perm eabi l i ty, a l l in approp riate param etr ic uni ts. The

algorithm can be conc eptual, empirical or theoretical. It can also be universal, reservoir-specific, or

even variable within a reservoir. The form of the algorithm m ust be clearly es tablished a nd verified

through core control. This is the first level of core calibration of log data.

As an example, let us consider the case of a quartz sand from a North Sea f ie ld with isolated

micropores within the grains. Bulk density is 2.3 g cm "

3

. The mineral density is 2.65 g c m "

3

but the

grain density is only 2.55 g cm"

3

. The conceptual relationship linking porosity

0

to bulk density p

b

is, for the particular case of the density log,

P

m a

~

P

b

0

n

=

(1)

D

p

-

p

ma f

where p

m a

, pj are the matrix and fluid dens ities, respectively. The question is "Which m atrix dens ity

do we us e?" If we follow s tandard practice and use 2.65 g c m "

3

, ^

D

is calculated as an absolute

porosity of 21 per cent. This figure includes the isolated micropores and wou ld not correc tly indicate

the reservoir porosity for practical purpos es. But if we are enlightene d, we will use the grain den sity

of 2.55 g cm"

3

for which #

D

is calculated as a total interconnected porosity of 16 percen t. This figure

excludes the isolated micropores which are represented within the correctly measured grain density,

the use of which allows good correlation between #

D

and porosity determined in the laboratory from

helium expansion measurem ents. Thus algorithm (1) is correctly characte rised.

This example em phasises the need to determine precisely those characterising param eters, such as

pj and p

m a

, which govern the form of the (conceptual) algorithms relating log measurements to

reservoir prope rt ies. Ano ther exam ple, th is t ime of an em pir ical core -cal ibr ated alg ori thm for

permeability prediction from nuclear magnetism and density logs in Western Canada, is that of Logan

(1989).

Measurement Conditions

Core-log integration is more effective where the core data themselves relate to simulated reservoir

conditions. Ideally, data should be acquired through pressure coring with rapid preservation, with a

compatibi l i ty of measurement directions where appropriate, and using simulated reservoir fluids,

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1991 SCA CONFERENC E PAPER NO. 9102

tec ton ic s t resses and rese rvo i r tem pera tu res . A l thou gh th i s gene ra l requ i rem en t s t r i c t l y

encompasses pressure and temperature, i t is general industry practice to consider only effective

overbu rden stresses. At the very least, a reservoir should be studied to establish the influence of

stress upon porosity and permea bility. If there is no significant depen den ce, the problem falls awa y.

But if there is a stress effect, some form of correction to core data is needed before the se data can

be used to cal ibrate log data. The spec i f ication of experim ental con dit ions is a crucia l step in

ensuring the accuracy and representativeness of core data.

Accuracy and Precision

I t is the practice to assume correctly functioning systems both in the laboratory and in

situ.

Uncertainties in log data are caused by the remoteness and the environment of tool measurement.

Corrections for borehole effects, invasion effects, and bed thickness all have inherent errors. The

error associated with the bed-thickness correction is greater than that for borehole co rrection . To

overcom e this problem , log readings should be chosen on plateaux where a pprop riate. Other errors

are par tly accom mod ated in ty ing back to core. Log data can be great ly im proved by s igna l

enhance ment or decon volution processing (D yos, 1986). This is an area whic h wi l l continue to

advance rapidly. In contrast, core data are seen as relatively accurate and precise (H amilton and

Stewart, 1983).

As an ex amp le, Fig. 1 illustrates the tying back to core of a density log. This log was run in a shallow

cored hole dri l led for coal exploration through thin beds with excellent depth con trol. There w as a

negligible mud cake and therefore only the long-spaced density data are show n. Fig. 1a contains

unproc essed log data which show departures in terms of accuracy (gradient * 1) and prec ision

(degree of data scatter). The signal-enhanced density log (Fig. 1b) shows a ma rked increase in both

accurac y and precision . In this example, the excellent depth control al lows the benefits of signal

enhancem ent to be strongly evident. The agreement wo uld be improv ed sti l l further if the vertical

resolutions of log and core data were comp atible.

Continuity and Sampling Volume

These two data characteristics are considered separately. Logs hold the advantage in both cases

being continuous and sensing a larger volume. We need to manipulate core data to br ing them

closer to the logs.

The continui ty of core data is l imited by the sampl ing interval chosen for routine core analysis

althoug h special core analysis studies have more flexibility. The sam pling interval for plugs must no

longer be considered in isolation from the logs. Key intervals should be chose n by considering log

data in conju nction with core natural gam ma reco rds. Identified key intervals shou ld have a plug

samp ling interval of 6 inches. Plugs should be taken away from bed boundaries. This strategy wou ld

provide the same continuity in core data as in conventional log data.

Plug sampling volume can vary from one to ten percent of the volume sense d by logs. The locations

of plugs for special core analysis should be guided by X-ray CT scanning where possible to avoid

local ised heterogeneit ies which the log wou ld suppress. Plugs should be taken h orizontal ly for

resistivity and permeabil ity measurements and vertically for sonic measurem ents. This would allow

directional compa tibility with the correspon ding well logs or forma tion tests. The core d ata should be

averaged over a distance that corresponds to the vertical resolution of the appropriate logging

tool.

If

the tool has been subjected to signal enhancement, so that the vertical resolution is processed to be

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sharper, it is the latter distance over which plug averages should be determ ined. This proced ure will

take place over selected key intervals for which plugs have been sampled at six-inch intervals.

The simplest approach would be a five-point running mean which would bring the resolution of the

core data closer to that of conventiona l log data. It can be shown that if the core p lugs are from the

same bed, this procedure carried out with equal weightings will reduce uncertainty by more than half.

The averaging of core data can be designed to reflect the response curve of the corresponding

logging tool (Barlai , 1979) by using appropriate weightings. This refinement might increase the

physical significance of the resulting mean.

Fig.

2 depicts a simulated densi ty log over a model interval that contains six porous beds, wi th

porosities 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, and with shale strata between the m . Each bed is 8 ft

thick and each has been sam pled at 6-inch intervals starting 3 inches into the b ed. Thu s, plugg ed

intervals are 0.25, 0.75, 1.25 ..., ft, mea sured from a bed bound ary. The core data are spe cified to

show a cyc l ic i ty o f bu lk densi ty . It is pres um ed that each core p lug sam ples a ho r izon ta l

hom ogeneou s layer of thickness six inches. The simulated density log averages the core data, a

feature w hich is clearly evident in Fig. 2.

In a real situation, we would be confronted w ith the density log and would crossplot this against the

core data , as shown in Fig. 3a. The inputs to this figure are the core data me asured at the s pecified

levels and digitised log data sa mpled at those same levels. The net result is a scatter of data about

the line of unit gradient with outliers due to shoulder-bed effects.

In contrast, Fig. 3b shows the same plot but with the core data averaged by a five-point running

mean over a vertical distance of 2 ft, which is approaching the vertical resolution of the density tool

(cf. Table 1). Now the scatter has virtually disappeare d. Thu s, while it is not possible to accou nt for

lateral near-wellbore heterogeneities that the tool might see but the core would not, it is possible to

account for heterogeneit ies vert ical ly wi th both log and core sampl ing at di f ferent scales and

therefore w ith different resolutions.

This approac h wil l be less effective in heterog eneou s and/or thinly bedded form atio ns: for such

cases a strip-sampling approac h has been proposed (Dahlberg and Fitz, 1988). This involves the

strip-sampling of core over a distance that approximates the spatial resolution of the logging

tool.

This technique involves destructive testing. It al lows the sam ple to be mixed over the length of the

strip. However, it is not standard practice.

CORE-DERIVED ALGORITHMS FOR LOG INTERPRETATION

There have been numerous examples in the l i te ra ture o f core data be ing compared wi th the

interpretations of log data. These exa mples draw upon the strategic elemen ts of the previous section

to widely varying degree s. Invariably, the form of the interpretative algorithm is determ ined at the

core scale and then applied at the log scale. Although this approach has often wo rked wel l , there

have been occasions where data reconcil iation has not been possible, even where the formation

seems laterally homogen eous. These m is-matches have been responsible for the view that core and

log data might have a residual incompatibi l i ty which cannot be reconciled even under favourable

conditions. In the case of core validation of log interpretation, a possible contributive explana tion

might be the sensitivity to upscaling of the empirical algorithms used in petrophysics.

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To illustrate this argument, let us consider a unit block of porous material that is divided into halves,

each of wh ich is homogeneous and isotropic (Fig. 4). The two halves are charac terised by po rosities

and formation resistivity factors


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SCA CONFERENCE PAPERNO 9102

The second approach is to use equation (3) in conjunction with the density mixing-law for porous

media, i.e.

p = 4> P + d - 0 ) p

m a

( 7 )

D

r ma

where p

f

, p

m a

are, again, the densities of the interstitial fluid and the rock matrix, respectively.

Equation (7) can be written:

_ V a _ ^ b

ma f

Equation (8) has the same form as equation (1). We can use equation (8) to substi tute for

j>

in

equation (3) so that:

(

P

ma

P

l

[

P

ma

f\

1

+

2

V -

P

2

P

ma

P

f\

Equa tion (9) reduces to equation (6). This means that both approa ches lead to the sam e pred iction

of p

b

. Th us, the system of equation s is robust in term s of cross -scale a pplic ation , i.e. the sam e

estimates of p

b

are obta ined by taking routes 1 and 2 (Fig. 5). It is impo rtant to note that thes e

equations are conceptual, in contrast to the previous example where the system was partly fo unded

on empiricism.

As petrophysics continues to move strongly towards cross-discipl ine integration in pursui t of

improved reservoir description, the question of the scale-compatibility of petrophysical algorithms will

be subject to further scrutiny. Even if core analysis practised in accorda nce with a sound tec hnic al

strategy does not prove to be fully reconcilable with log data, it does provide so me ground -truthing

which , i f properly used, can aid in the quantification of uncertainty and the management of risk.

Further progress will no doubt be forthcoming from the tech nical advances that are in hand.

CONTEMPOR RY DEVELOPMENTS

The strongest technical drive to integrate core and log data at the present time is to be found in the

technology and engineering development function of the Ocean Dril l ing Program (O DP ). The ODP

was initiated in 1983 and it has, to date, been responsible for some 40 scientific expeditions (legs) to

explore the structure and history of the Earth as revealed beneath the ocean s. The exploration

strategy has b een to dri l l ful ly-cored boreho les at target sites and then to run a ful l suite of wireline

logs at each site. The database of core and log data is therefore volum inous and , if not m anage d

effectively, i t might become difficult to handle, even during the course of a single leg. The present

objective is to provide an integrated database of core and log data for each drilled site in as close to

real time as possible. The following ongoing technical developme nts are directed at achieving this

goal.

Sonic Core Monitor

In cases of incomplete core recovery it has been the practice to "hang " the rec overed core fro m the

top of the core barrel as though recovery had been continuous over the uppermost portion of the

interval and zero over the rest. The sonic core monitor is desig ned to record the rate of core

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1991 SCA CONFERENC E PAPER NO. 9102

recovery so that, by comparing this rate with the rate of bit penetration, the correct location of each

core piece within the core barrel can be determined.

The system comprises an ul trasonic source/sensor at the top of the core barrel and a reflector

positioned on the top of the advancing core (Fig. 6). The length of the core column is continuously

recorded by mea suring the travel time of the reflected pulse. An example of data output is show n in

Fig. 7. The zones of poor core recovery are imm ediately recog nisable . Now that the conce pt is

proven,

the prototype design is being upgraded through the development of a second tool for routine

field use in consolidated rocks.

Multi-Sensor Track

The oil industry traditionally records core natural gamma counts by running the recovered core on a

belt through a gamm a-ray detector. The ODP has extended this concept to include other con tinuous

measurements such as magnetic susceptibi l i ty, gamma-ray attenuation and P-wavevelocity. This

so-called "mu lti-sensor track" is a screening facility: it provides a reference scale to which other co re

data can be related, e.g. physical properties measurements on core plugs.

Interactive Data Display

The aim is to integrate laboratory data and downhole measurements w ithin a com mo n user-friendly

system. The system is required to be interrogatable and it must have the capabil i ty to select a nd

display in interactive mode all those data that are relevant to a particular interpretation process.

There are four essential stages in this process :

(i) Integrate core data

(ii) Integrate log data

(iii) Merge core and log data

(iv) Display core and log data

At the present time item (i) is receiving the highest priority. Data are being store d as com put erize d

barrel sheets for which the display can be varied . An example is show n in Fig. 8. The inten tion is to

merge the log data so that they, too, can be displayed on call in the available right-ha nd tracks . This

wou ld mean that any log or core data could be called up and displayed with a com mo n depth scale

subject only to track availabil i ty. This facil i ty wo uld provide the opp ortun ity for intera ctive d ata

interpretation using the core-log database actually during the course of an ODP leg.

CONCLUSIONS

Core -log correlation is an integral com ponen t of reservoir chara cterisatio n prac tice. I t can be

enhanc ed by the judicious selection of key intervals com prising relatively thick, fully cored bed s. Key

intervals should be targeted on the basis of seismic and geological zonation and the chararcter and

qual i ty of log data. Plug selection shou ld be driven by the need for comp atibi l i ty b etw een the

di fferent scales of measurem ent, pr incipal ly the core and log scales. Plug locations should be

chosen on the basis o f log responses and X-ray CT scanning, the la t ter to invest igate any

heterogeneities in selected core pieces. For maximum effectiveness the plugs should be selected by

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1991 SCA CONFER ENCE PAPER NO. 9102

Ham ilton, J.M. and Stewart, J.M. 1983. Thin bed resolution and other problems in matching log and

core data. Tran s. SPWLA 24th Ann. Logging Symp., 11-13.

Heaviside, J., Langley, G.O. and Pallatt, N. 1983. Permeabili ty characteristics of Magnus reservoir

rock. Trans SPWLA 8th Eur. Form.Eval.Sym p., A1-29.

Logan, W.D . 1989 . Bridging the gap betwee n core permeab i l ity and log-derive d pe rme abi l i ty.

Trans. SPWLA 30th Ann. Logging Symp., V1-23.

Marc hant, L.C. and White, E.J. 1968. Com parison of log and core analysis results for an extrem ely

heterogeneous carbonate reservoir. Trans. SPW LA 9th Ann . Logging Symp., L1-16.

Olson, D.M. 1986. Calibration of log and core saturation data : case history from the San Ardo Field.

Trans. SPWLA 27th Ann. Logging Symp., Z1-12.

Pa l l a t t , N. , W i l son , J . and McH ardy , B. 1984 . The re la t i onsh ip be tween pe rm eab i l i t y and

morphology of diagenetic illite in reservoir rocks. J. Petr. Tech . 36, 222 5-2227.

The ys, P.R. 199 1. Log data acquisition and quality control. Editions Tec hnip, Paris, 330p p.

W orthington , P.F. 1990. Sediment cycl ic i ty from wel l logs. Geo l. Soc. London Spec. Publ. 48 ,

123-132.

W orth ington, P.F. 19 91 . Reservo i r character iza t ion a t the mesosco pic sca le . In : Res ervo i r

Characterization II (Lake, L.W., Carroll, H.B. Jr. and Wesson, T.C.,: Eds.), Academic Press, NY,

123-165.

W orthing ton, P.F., Tou ssaint-Ja ckson , J.E. and Pallatt, N. 1988. Effect of sam ple preparation up on

saturation exponent in the Magnus Field, UK North Sea. The Log Analyst, 29(1), 48-53.

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1991

SCA

CONFERENCE PAPER NO .

9102

TABLE1

VERTICAL RESOLUTIONSOFLOGGING TOOLS

LOG

TOOL INTRINSIC LOG LOGPARAMETRIC

RESOLUTION* SENSITIVITY** RESOLUTION**

(m ) (m) (m)

Gamma

Ray

Density

Induction

Neutron

Sonic

0.2-0.3

0.4

2.1-2.4

0.4

0.6

0.3-0.4

0.25-0.3

0.75-0.9

0.5-0.6

0.6-0.8

0.6-0.75

0.45-0.6

1.2-1.5

10

2.4-2.6

1.3-1.4

(Conductive Beds)

(Resistive Beds)

from Allen e ta l . (1988)

assumesnoenvironmental effects,nonoise

Notes:

TOOL INTRINSIC RESOLUTIONis atool design c haracteristic:

LOG SENSITIVITY

is the

minimum

bed

thickness that

can be

seen

as an

event

in the

logging curve:

LOG PARAMETRIC R ESOLUTION is theminimumbed thickness neededforfull manifestationof the

logged parameter(s)

in the

logging curve.

TABLE2

RELATIVE MERITSOFCOREAND LOGDATA

CHARACTERISTIC

CORE

LOG

Evaluation Procedure

Measurement Conditions

AccuracyandP recision

Continuity

Sampling Volume

Direct

Experimental

Higher

Discontinuous

Smaller

Indirect

In situ

Lower

Continuous

Larger

Arrows indicate

the

more desirable data

- 1 2 -


13/16

Example of crossplots of core vs log density for

(a) Conventional log data and (b) Signal-enhanced log data

(cour tesy Dyos. C .J . . personal communicat ion)

2.5

g

1.5

(a)

1

I

2

c

& 1.5

(b)

1.5 2 0

Core density (g cm"

3

)

Simulated density log for alternating sand-shale

model sequence

Figure 1

Crossplots of core density vs log density for (a) Lev el-by-leve l correlation and

(b) Five-point de pth-averaging of core data, for model of Figure 2

Figure 2

Negative 0 wrt nominal porosity

Positive 0 wrt nominal porosity

(a)

2.5 2.0

Core density (g cm"

3

) (b )

Mean core densi ty (gem*

3

)

Figure 3


14/16

Example of non-robust algori thm for cross -scal ing

(Archie equation)

ROUTE 1

ROUTE 2

F l F

2

4

Resistors in

parallel

1.0

s>

1.0

01 F1

02 F2

/I

0.5

I

\

\\+

Mean

porosity

algorithm

+

Formation

factor

power law

Archie

equation

|

J

Example of robust algorithm for cross-scaling

(Density - porosity equation)

Estimates

disagree

unless

m=1

Figure 4

ROUTE 1

ROUTE 2

P1 P2

>

Summation

of masses

Pb

-1.0

1.0

01 P1

0

2

p

2

/ \ .

0.5

I

0.5

i

**10

^y

0 2* Pb

y

/

[

Mean

porosity

algorithm

\

P

P2

+

h * -

Mean density

equation

. Density -

porosity

equation

J

Pb

Estimates

always

agree

Figure 5


15/16

Sonic core monitor (Phase

1)

(courtesy Huey.D.. personal communica tion)

Sonic core monitor

Penetrationvscorrected core height

(courtesy Huey D., personal communication)

XCB Core b arrel

assembly

Battery pack

Downhole

electronic unit

Sonic transducer

Time (arbitrary units)

Stainless steel target

(riding on top of core)

Fgure 7

EXAMPL E

OFA

COMPUTER ISED CORE BARREL SH EET

(courtsey Janecek. T personal communication)

Reflectance

( )

0 30 50

-.\s\

:

>^>

j * ^ ->?

"

'" s .

T -.-f -,

.(""

t p ^ ^

iii(

\

~

^^

\ )c?

\ 3

u ^

\

y

" l _^T^_^

:

i \

lj

5;

J ^ .

s>.

v3-

cT J ?

C

r

."'

jj

^>

S / - "

I '"*

I ?

j ^

i i i i

S I T E

:

:K

ir -

:

-

-

-:

j

:

:

-.

-i

^

-

:

-

-=

-

i

-

s

3 5 4 H O L E

b t h .

^-'

M "

:

$

;

::

Documents

Effective Integration of Core and Log Data