Automatic Determination of
Lithology From Well Logs
Pierre Delfiner, SPE, Etudes et Productions Schlumberger
Olivier Peyret, SPE, Schlumberger Overseas S.A., Brunei
Oberto Serra, Schlumberger Technical Services
Summary. A procedure combining modern wireline measurements with a lithofacies data base has been
developed to produce an automatic lithologic description of the formations crossed by a well.
The database lithofacies are defined from petrographic knowledge and translated in terms of log responses.
The assignment of depth levels to a lithofacies is done with the data base and with a discriminant function
(Bayesian decision rule). External knowledge can be taken into account by use of artificial intelligence methods'.
A confidence factor is produced for each result.
.
Logs currently in the data base are the density, neutron, sonic transit time, gamma ray, photoelectric cross
section, and concentrations of thorium, potassium, and uranium. Major lithofacies groups represented in the data
base include sandstones, limestones, dolomites, shales, coals, and evaporites. These are subdivided by
introducing cement and special minerals and by considering porosity ranges.
The construction of the data base is a critical step. It is largely empirical and requires careful calibration
against intervals with well-known lithologies (e.g., from cores). The data base can be tuned to local conditions.
The procedure has been tested in several environments and compared with cores and mud log descriptions. A
detailed lithologic column can be produced at the wellsite and used in decision making. The results can also
serve as input for further geologic studies of facies and sequences or for quantitative evaluation of formations.
Introduction
Subsurface lithology is traditionally determined from core
or cutting analysis. Cores are generally not continuous
and consequently do not provide a complete description
of formations crossed by a' well. It can be difficult to restore the components and the thickness of the lithologic
column from cuttings. This is because of mud swirling,
influence of caving (fall-down of wall fragments), loss
of some constituents (salts and silts), or even the total loss
of circulation. As a result, the lithology column based on
those data is not sufficiently accurate and precise for quantitative use.
Well logs give a practically continuous survey of the
formations crossed by a well. They allow measurement
of apparent thickness and of real thickness if dipmeter data
are taken into account. Burke et al. 1 and Clavier and
Rust 2 have shown that well log responses can give a good
idea of the lithology. With the increase of physical parameters recorded by modern logging tools-e.g., photoelectric cross section, natural or induced gamma ray
spectrometry (GRS), and dielectric constant-it becomes
more obvious that their combination can give a good lithologic description of the formations. This evidence was the
basis for the concept of "electrofacies" (Serra and
Abbott 3 ) defined as "the set oflog responses which characterizes a bed and permits it to be distinguished from
other beds" (Serra 4 ). Applied to openhole logs, this electrofacies is an equivalent of the lithofacies that, according to Moore,5 is the "total sum of the lithological
characteristics (including both physical and biological
Copyright 1987 Society of Petroleum Engineers
SPE Formation Evaluation, September 1987
characters) of a rock." It is not always obvious, however, to translate this electrofacies in terms of geologically meaningful rocks. A procedure combining modern
wireline measurements with a lithofacies data base created
from logs (strictly speaking, an electrolithofacies data
base) has proved to be effective in this translation.
The lithofacies are previously defined from petrographic
knowledge and translated in terms oflog responses. This
forms a data base of rocks (sandstone, limestone, etc.)
as opposed to minerals (quartz, calcite, etc.). Levels of
log data are assigned to a given lithofacies by use of the
data base and a discriminant function.
The objective in this approach is to define the lithology by use of a set of log responses applied at the wellsite
and at the computing center.
Approach
The basic idea is to represent a set of n log readings at
a given depth level as a point in n-dimensional space, the
log space. An electrofacies is then materialized as a cluster
in this space-i.e., an area with a relatively high concentration of points. The points are close because the log
responses are similar. Our task is to attach to each level
of the well the cluster, or group, to which it belongs.
Statistical theory provides two types of approach. 6,
One approach is to determine the clusters from the data
in each well. This "clustering" has the advantage ofletting the data "speak for themselves" and reveals their
subtle differences. However, the geologic interpretation
of the clusters must be ~epeated each time. A procedure
known as FAcloLoa™ has been developed to analyze
electrofacies by clustering. 3,7
303
RHOB~
,
·
:
··
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.
. .
:
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.,
.
..
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:.
..............•
LIMESTONE 25·35 PU
FELDSPAR
N ...
";".00
QUARTZ
.
•
....,.\>
'
..
""""".""'''''.''''''''.,.''~''''''''''''''''''''''''''''''''''.''''
.
,
,
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,
,
.
15.0
25.0
35.0
ijS.Q·
NPHI
a
ROCK
FRAGMENTS
PEF ~ ,
ACCESSORY MINERALS
HEAVY MINERALS (pyrite, hematite)
HEAVY RADIOACTIVE MINERALS (z:ircon, monaz:lte, mica)
LIGNITE FRAGMENTS
CEMENT
~
CALCITE
,.
:LITHIC SANDSTONE
is
c.:t-.:::"oo----:r::-------".-:t,5-:-,.0-----::!25i:"":,.0--~:r.:---:::-:iij •. o
NPHI
b
DOLOMITE
ANHYDRITE
HALITE
CLAY
Fig.1-Projections of a lithic sandstone and a 25- to
35-p.u. limestone electrofacies on (a) density/neutron
crossplot and (b) P e/denslW crossplot.
The second approach, used here, is "classification"
(also called "discriminant analysis"). The grolJ.ps are
specified in a lithofacies data base and' then each depth
level is assigned to the correct group by use of an appropriate discriminant function. The procedure has the advantage of being fully automatic because the interpretation
work is done only once; however, acorrect definition of
the 4ata base is critical to obtain good results.
There are various classification methods that differ
mainly by the criterion, or distance measure, used for assigning a sample to a group., The distance measure can
be uniform for all rocks or can vary with the rock. Busch
et al. 8 have proposed a method in which the discriminant
functions are calibrated on core data for each field, but
the same distance measure is used for all rocks. In our
approach, the distance measure varies with the rock,
reflecting the internal dispersion of log readings for each
rock.
Each rock is represented in the data base as a volume
in the n-dimensional space of logs. A volume.is used
rather than a point to allow for variations resulting from
both geology and data acquisition. By definition, a rock
has mineral' proportions that vary within certain ranges.
For example, a quartzose sandstone is composed of 90
to 100% quartz, and 0 to 10% other material (feldspar,
mica, clay, or calcite). Rocks also vary in porosity, texture' and fluid content. Data acquisition introduces variations of its own, independent of lithology, by way of pad
contact, borehole rugosity, invasion profile, mud effect,
and shoulder effects. In the case of nuclear tools, there
are also statistical fluctuations in count rates.
304
Fig. 2-Classification of sandstones· (adapted from
Pettijohn 10,11).
The size of e~ch volume reflects the amplitude of all
these variations. For example, gypsum is a lithofacies with
a well-fixed composition and is represented by a small
volume in the data base. By contrast, the volume
representing shaly sands is quite large. In practice, this
volume is divided into subcategories, keeping in mind the
tradeoff between robustness and resolution: a large electrofacies volume "absorbs" more variations due to acquisition statistics but may also hide real lithologic
differences.
Each electrofaciesvolume is usually represented as an
ellipsoid in n-dimensional space by reference to the multivariate Gaussian distribution. The model is simple and
yet flexible enough to capture the main features of an
electrofacies-range on eacn log and directions of main
elongation. Fig. 1 shows the representation of two electrofacies, a lithic sandstone (quartz, feldspar, rock fragments, and chlorite) and a 25- to 35-p.u. limestone, in
the three-dimensional (3D) space defined by the density,
neutron, and photoelectric cross-sectional (P e) lithodensity logs. From the neutron/density crossplot alone, it is
not possible to distinguish between the two rocks; however, they are well separated by the P e' Generally, the
more dimensions, the better the discrimination.
Construction of the Lithofacies Data Base
A data base of about 150 rocks was designed to cover
the most·abundant sedimentary rocks. Some igneous rocks
have been included because they are encountered in
sedimentary series as basements or as intrusions of lava
flow (weathered granite reservoirs would be counted as
SPE Formation Evaluation, September 1987
OPEN HOLE
LOGS
CORE DESCRIPTION
LITHOLOGICAL t-L--T-H-0-rF.-~-C-IE-S--_f
COLUMN
SUB
MAJOR
FROM LOGS
definition
definition
_J.ftJL_
46
p.u.
RHOS
-15
150
:::I::t':~J~~':t:.:~
1"~'f..
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,eckstone, .... WICke
"onl wnh common
'"hr'titln"ula.
CALCAREOUS 0-10 PU
PURE ANHYDRITE
CALCAREOUS 0·10 PU
TIGHT DOLOMITIC
CALCAR.EOUS 0-10 PU
ANHYDRITIC
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EVAP
DOLO
LIME
DOLO
PURE ANHYDRITE
EVAP
LIME
Chicklnwi,••n,'Ydrita
TIGHT OOLOMITIC
LI'htohw••' .... tom.d w
'..""r.ye.le.r
. CALCAREOUS 0·10 PU
.
....llIoun•••O,.
••c....ton••. ,.,fllnn.
tn' ,'I'nt,onl .. n....
'hin,I,.'ll'y'OIO""1
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:;~~i:::::~:: In·
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'om'd~
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~r
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EVAP
ANHYDRITIC
LJME
PURE ANHYDRITE
EVAP
MEDIUM POROUS10·25P
ionll.lckt"onl
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PURE- ANHYDRITE
ANHYDRITIC
,rain,.tnetnlll,.I.....
,.c.....n•. wi.hocc•• •
.,It.
LIME
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il"
LIME
TIGHT 0-10 PU
I
,"h.. ·
L'ltttOliv••, • .,.Iome'-
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ANHYDRITIC
EVAP
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DOLOMITIC 25-35 PU
LIME
I'URE ANHYDRITE
EVAP
ANHYDRITIC
DOLOMITIC 10-25 PU
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LIME
PURE ANHYDRITE
EVAP
ANHYDRITIC
DOLO
TIGHT 0·10 I'U
LIME.
=.T:~r:~:~~r.:c:;,
....1'Inde.ton, In'
"C"'lonewllh,"h... •
dri'l nMMI•••nd I• .,.,
:~itC.hiCII.nWI,• •
n"".
-r---::::_
DIrk ".,,..,,•. w• .,.,
In' ,.,a"lllamlne,••
hm.m......en•••
,....m.reu.w.c....
l'I"""',ICIl.'I
.
Wlth,.""''''',,11
"'f.'tcar,."
Fig. 3-Comparison of lithofacies description from logs and cores in a carbonate and evaporite series.
arkoses). Metamorphic rocks have not been introduced
because of lack of control and lack of logs recorded in
such formations.
The logs currently introduced in the data base are natural gamma ray; thorium, uranium, and potassium contents
as computed from natural GRS; density; neutron porosity
(or apparent hydrogen index); sonic transit time; and P e'
Other possible additions are sonic waveforms, following
the work of Hoard, 9 and results from Gamma Ray Spectrometry Tool (GST™) measurements, which provide
the concentrations in elements (iron, calcium, silicon, sulfur, oxygen, carbon, hydrogen, chloride, etc.).
Three approaches can be considered for the construction of the data base.
Theoretical Approach. This is based on petrographic
classifications found in the literature and on tool response
SPE Formation Evaluation, September 1987
equations. For example, detrital rocks have been classified by Pettijohn 10 into quartzose sandstones, arkoses,
graywackes, and shales, as illustrated in Fig. 2. For each
type, the ranges of the mineralogical and elemental compositions result from analyses of typical rock samples
reported by Pettijohn et ai. 11 These compositions can be
converted to well log responses by use of tool response
equations and the well logging parameters of minerals established by Edmundson and Raymer. 12 The influence
of porosity, of accessory miner'.lls, and of data acquisition statistics is also considered.
Interpretive Approach. This method is based on interpretation of crossplots validated with core or cutting data.
Ellipsoids are defined from their two-dimensional projections on crossplots, which are ellipses. Each ellipse is
specified by the minimum and maximum on each log and
305
GLOBAL
RESULTS
OPEN
HOLE
LOGS
c(
~
IX
a..
LITHOLOGIC
COLUMN
LITHOFACIES
SUB
MAJOR
definition definitio
I(AOLINITIC
;i ..~_·-I+-----t=''-''1-oI-:::1-~r--f''''''-'''';;~~~~~'''''';''~-ICOMPACTEDKAOLINITI
KAOLINITIC
WITH HEAYY MINERALS
KAOLINITIC
POROUS RADIOACTIYE
UNCOMPACTED
KAOLINITIC
PEAT
UNCOMPACTED
KAOLINITIC
LO.OSE HEAVY SILTY
SAND
SHAL
COAL
SHAL
CLEAN 30-45 PU
SAND
CLEAN. 20·30 PU
CLEAN 30·45 PU
POROUS RAOIOACTIYE
LOOSE HEAYY SILTY
SILTY
IlRAYWACKE 35'45 PU
UNCOMPACTED
KAOLINITIC
SHAL
SAND
SHAL
KAOLINITIC
L1GNITIC DR BITUMIN.
COAL
PEAT
L1GNITIC OR BITUMIN.
SHAL
PEAT
COAL
Fig. 4-Lithofacies description from logs in a sand,shale, and coal series.
a correlation coefficient. The lithofacies are defined manually by a geologist who interprets the crossplots in mineralogical and petrographic terms. Simple statistical
techniques, such as histograms and Z-plots, help to determine ellipsis boundaries. Experimental control is obtained by analysis of all possible log data crossplots in
wells where the composition is known either from core
or cutting analyses or from regional geologic knowledge.
Empirical Approach. This approach is based on fitting
a log model on cores. If lithologies are picked from cores,
it is possible to build a log model by analyzing the distribution of log responses in front of the conis; however,
this requires that cores. and complete logging suites for
all rocks be entered into the data base. This is usually not
possible except in field studies. Alternatively, a clustering analysis (e.g., by FACIOLOG) may be used to define
the electrofacies .
306
The procedures actually used to construct the lithofacies
data base is a mixture of the three approaches, making
use of cores whenever available, but also of tool response
equations and of crossplots to cover the other cases. Thus
a general data base was builtto integrate data from different wells representing various types of lithologies. The
data base includes 30 sandstones, 25 shales, 30 limestones,
25 dolomites, 25 evaporites, 3 coals, 10 igneous rocks,
and 4 miscellaneous rocks.
.
To account for the influence of porosity on log
responses, porous rocks have been subdivided into several
porosity ranges .. Porosity is assumed to be filled with
water. The effect of gas or light hydrocarbon on the logs
has to be detected and corrected beforehand.
Sandstones have been defined following Pettijohn's 10
classification. Cement (calcareous, dolomitic, or halitic)
and accessory minerals have also been introduced-e.g.,
heavy minerals (pyrite and hematite), heavy radioactive
SPE Formation Evaluation, September 1987
SH
5S
5S
~~
SH
SS,;SH
7ZS0
55
SS,;SH
7250
SH
COAL
SH
COAL
"'Ot~ II.
co.o4
6 ~~ .
10 CJ»(. J ~""', ........~,
7300
CI1"" .
~iii!lilrr-r---------------1
SH
'"!!!----
7300
59
"1
Go
el' - ~",
la, "fo.'
):) 50..1... I ~1I..&.'" - "'/~··
.
o -.Q.,l».\>; 2l,"~"J ..... Q.W.JI'"t+; eua.QQ. • .) ... "-
5S
SH
S5,;5H
.+.
11DC~
,~~~~ c':S~
flO . .
J.f'c...<~
7350 - - - - t-ti;IOO~S.:T'-al:;:.:-,~.-(:'t~
...
::---~,\'1:-)""':' ' '::-:'f'o(:-,-occ.-.-ftA""':'""':'-;.-ss--~r-II-' -~
.:~-- -..:
fI\d.&a.( \I. cas4 6r1i-.
~ GO"", .I\:>, ......a 0.... , ...""\. , f'i~ , _~ - "'to·
c\....
COAL
7350
v- - -
SH
,cit
~ .......", ~
M", -
.
~ ~)
c:l..- 1 , .
-nL "', elf'- .... ,'(J II\", '"1~.
""'. J
N" , dMA,I. -\ll,\-,
Fig. 5-Comparison of lithofacies description from logs with a mud-log description from cuttings.
minerals (zircon, monazite, and micas), and lignite
fragments.
For shale lithofacies, four grades of compaction have
been considered (tight, compacted, average, and uncompacted). When natural GRS data are available, the clay
mineral type is deduced from the thorium and potassium
values and their ratios. The introduction of induced GRS
measurements will provide even finer clay typing.
Special shale lithofacies have been included .(lignitic or
bituminous shales, and shales with heavy minerals, reflecting the influence of limonite, siderite, or pyrite). Carbonates have been subdivided into limestones (less than
25 % dolomite), dolomitic limestones (25 to 50 % dolomite), calcareous dolomites (50 to 75 % dolomite), and
dolomites (more than 75 % dolomite). Other lithofacies
have also been introduced to account for the presence of
silica (especially chert), anhydrite, heavy minerals, clay
(argillaceous), or radioactive minerals (apatite). Rarer carbonates, such as siderite and ankerite, have also been introduced for completeness.
Evaporites are well known and e"sy to recognize from
log responses, especially if they are present in beds thick
enough to make the influence of adjacent beds negligible.
Pure evaporites have very well-defined log responses and
in principle could be represented as points in the electroSPE Formation Evaluation, September 1987
facies data base. They have been represented as volumes,
however, to account for minor changes in composition
and statistical variations of the log responses related to
borehole conditions or acquisition statistics.
Organic rocks have been differentiated in coal, lignite,
and peat according to the small changes observed on log
values. Some igneous rocks have been introduced into the
data base. Their mineralogical composition is reflected
mainly by density and sonic transit time, as well as thorium, potassium, and uranium content. These rocks have
a high resistivity when they are pure and are not weathered
or fractured.
Statistical Analysis
A statistical procedure, known as a Bayesian decision rule,
uses the data base to classify depth levels. The principle
is to attach a probability distribution of log values to each
lithofacies and then to identify from which level the set
of log readings most likely originated. The possibility that
a depth level does not belong to any of the specified
lithofacies is also considered.
Within each lithofacies, the vector X of log responses,
suitably transformed, is assumed to have an n-variate
Gaussian distribution whose density is denoted by
P(XIF i ). This corresponds with the ellipsoids of the data
307
BIOTURBATED?
EXAMPLE OF COMBINATION OF
LITHO
GEODIP
CLUSTER
FOR LITHOLOGY
FOR TEXTURE
FOR STRATIGRAPHY
DRAPE
LAMINATED
LEGEND
MASSIVE
_______I~
Fig. 6-Combination of lithofacies description from logs with
base that are interpreted as the volumes containing 95 %
of the points. These distributions allow calculation of the
probability of the log readings by use of the lithofacies.
What is needed, however, is the probability of the lithofacies P(F j IX) given the log readings, called the posterior
probability ofF j • It can be obtained from Bayes' formula:
pjP(XIF j)
EjpjP(X\Fj ) ,
where Pi =the prior probabilities of the lithofacies before
any logging data were collected. They 'represent the information that may have been available on the lithology.
For example, if there are only carbonates in the well, P j
may be set to zero for all sandstones. When no information is available, all lithofacies are given an equal chance
and the p;' s are all equal. Values other than these may
be derived from external geologic knowledge of the area
of interest.
The lithofacies selected as the answer at a given depth
level is simply that which maximizes the posterior probability: The computed posterior probability provides an
index of confidence in the result. A point falling outside
of all 95 % ellipsoids is classified as "unidentified."
The procedure described (which can be shown to
minimize the risk of misclassification) is applied successively to all levels of the studied interval in combination
with some special logics reviewed here.
Examples
Fig. 3 shows an example of the lithologic column obtained
in carbonates. The interval is typical of the Upper Jurassic
308
GEODIP
and
CLUSTER
CI.USTER
I
results.
in the Middle East. There are two levels of description:
one indicates the major lithology (e.g., limestone, dolomite, or evaporite), and the other qualifies it (e.g., anhydritic dolomite). A posterior probability curve is shown
for the major and secondary descriptions indicating, on
a scale of 0 to 1, the confidence in each of the results.
For example, all anhydrite'beds are detected witb a confidence of 1, except the fourth one from the bottom where
the confidence is only 0.7, because the bed is not pure
anhydrite as evidenced by the density, which is too low
for pure anhydrite. A core description is shown on the
side for comparison and indicates that the interval is partially dolomitized with nodules of anhydrite. Thus, the
lithology has been correctly identified by the logs used
in this example (density, neutron, gamma ray, and sonic).
A more complete description from the cores requires
texture- and structure-sensitive tools, such as the dipmeter.
Fig. 4 shows results from a complex shale/sand lithology, Coal (more precisely, peat) is detected and clay minerals are identified as kaolinite by use of the thorium and
potassium logs; this has been confirmed by core analysis.
Fig. 5 shows the match between a lithologic description from logs and a wellsite litholog established from drill
cuttings. The agreement is quite good in most of the well,
with a correct identification of shales, sandstones, and coal
beds. In the upper section, between 7,250 and 7,255 ft
[2210 and 2211 m], however, the mud log indicates coal,
whereas log-derived lithology indicates sandstone. Conversely, coal beds appear on the logs between 7,232 and
7,242 ft [2204 and 2207 m], while the mud log indicates
sandstone. This difference occurs because the coal beds
are positioned at drilling breaks on the mud log; the high
SPE Formation Evaluation, September 1987
penetration rate of the drill bit in sandstone has led to ~r­
roneous location of the coal at 7,250 ft [2210 m], whIle
it actually came from above. Therefore, the disagreement
is not really about lithology but about depth, and the answer from logs is more reliable because logs have better
depth control.
Special Logics
A straightforward level-by-level application of the Bay~s '
decision rule is not sufficient to solve some of the practI.
cal problems that may arise (see Serra et al. 13).
In principle, logs processed for lithology determinati9n
have been corrected for borehole effects (temperature, rugosity, etc.). But if the effect is too serious (caving, baryte
or potassium salt in the mud, or gas), the affected logs
either will define wrong electrofacies or will result in
unidentified levels. In such cases, the affected logs are
skipped for the determination of the lithofacies and a flag
appears to indicate that the answer is not as reliable as
in other zones.
Light hydrocarbons, and especially gas, may.have an
overwhelming effect on the responses of the densIty, neutron, and sonic logs, and may completely overshadow
lithologic differences. Thus, light hydrocarbons are detected beforehand and their effect on density and neutron
is corrected. Correction for the sonic log is more difficult,
so this log is simply skipped in gas-bearing intervals.
To avoid holes in the data base, which result in many
unidentified levels, the ellipsoids have been defined with
partial overlap. This may create instability when points
are at the boundary between two lithofacies. To overcome
this problem, a vertical-continuity logic has been introduced.
Because mineralogical or chemical composition is not
the only parameter used in the classif~cation, tw.o .rocks
with similar compositions but very dIfferent ongIns or
textures may be represented approximately by the same
volume in the n-dimensional space. For example, some
graywackes and volcanic agglomerates both have th~ c?mposition of a trachy-andesite. In such a case, the dIstInCtion can be made only by introducing external knowledge.
The allocation program is guided by specifying the main
lithologic type in a given interval to disallow some lithofacies. This information may be derived from the analysis of other logs, such as spontaneous potential, caliper,
and resistivity. Alternatively, local geologic knowledge
bases holding formation tops and general lithologic composition may be used. For example, "7,900 ft [2408 m]
Top Arab-D" indicates the presence of carbonates .below
this depth. Geologic knowledge bases can be bUllt and
consulted with artificial intelligence methods.
In thin beds, log responses are influenced by surrounding beds and the deflection is often not enough to allow
correct identification, although it may allow detection.
Thus, a minimum bed thickness can be specified with the
capability of retaining thinner beds of some very characteristic lithology (e.g., coal, shale, or even sand beds).
Applications
The first application is, of course, the display of a lithologic column, which can help the geologist at the wellsite to cross-check and depth-match mud logs; to select
tops and bottoms of zones of interest, locations of sidewall core samples, and fluid sampling or pressure measSPE Formation Evaluation. September 1987
urement depths for the Repeat Formation Tester
(RFT™); to define intervals for a drillstem test; and to
provide input to a wellsite formation evaluation "quick
look." Applications at the office include well-to-well correlation (in conjunction with other information); mapping
of isoliths-i.e., lithofacies thicknesses, after conversion
to. true vertical thickness; correlation with seismic data,
especially vertical seismic profiles; and automa~ic ~ho~ce
of the mineralogical model needed for a quantItatIve Interpretation, and hence automatic selection of the parameters.
.
The log-derived lithologic column becomes an espeCially powerful geologic tool when combi~ed. with. hi~h­
resolution dipmeter results. An example IS gIven In FIg.
6, which shows a composite display of the resistivity
curves from the High-Resolution Dipmeter Tool
(HDT™) with correlated events found by the processing .
(GEODlp™ program), the lithologic column limited. by
the thorium and potassium curves, and the computed dIpS.
The drape pattern observed in the upper section of the
interval resulting from the dipmeter could suggest several interpretations-e. g., a fluvial channel fill, a bar, or
a reef. The limestone lithology, however, rules out a fluvial or deltaic environment and indicates a carbonate
buildup in which the upper laminated layers were molded on top of the underlying massive limestone. Further,
the formation is dipping to the south, and therefore the
top of the reef is to the north. This is where the next well
should be (and was successfully) drilled.
Conversely, dipmeter information complements the
lithologic description. For example, the thick limestone
bed in the middle is seen as massive by the dipmeter (little activity and no correlations), whereas· the argillaceous
limestones on top are laminated (high activity and many
correlations)., A shale interval in the upper part of the well
illustrates the case of high activity on dipmeter curves associated with scarce correlations, indicating possible bioturbation. (In coarser clastic rocks, this behavior is typical
of conglomerates.) Thus texture, structure, and lith?logy obtained from wireline measurem~nts can be combI~ed
to define the complete geologic faCIeS and to determIne
the depositional environment.
Nomenclature
F i = lithofacies i
F j = lithofacies j
j = dummy summation index
Pi = prior probability of F i
Pj = prior probability of P,i
P = probability of
P e = photoelectric cross-section
fj.t = sonic transit time
X = vector of log response at a given depth
level
Z = Z-plot crossplot showing values of a
third variable
P b = bulk density
¢ N = neutron porosity index
Acknowledgments
We are particularly thankful to I.C. Levert, S. Luthi, C.
Dahan, and J. Harry for their contributions to this work.
Thanks are also extended to the oil companies for permission to publish the examples.
309
References
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310
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51 Metric Conversion Factors
ft x 3.048*
E-Ol
in. x 2.54*
E+OO
'Conversion factor is exact.
m
em
SPEFE
Original manuscript received in the Society of Petroleum Engineers office Sept. 16,
1984. Paper accepted for publication Sept. 20, 1985. Revised manuscript received
June 19, 1986. Paper (SPE 13290) first presented at the 1984 SPE Annual Technical
Conference and Exhibition held in Houston, Sept. 16-19.
SPE Formation Evaluation, September 1987