Method and apparatus for determining geological facies

In a method and apparatus for obtaining a recording of facies of geological formations, logging instruments are moved in a borehole to produce log measurements at successive levels of the borehole. With each level is associated a reference sample whose coordinates are constituted by the respective measurements in a multidimensional space defined by the different logs. The scatter of samples thus obtained is analyzed to distinguish modes characteristic of clusters in which the concentration of samples is the highest. The samples may then be associated with selected modes to form classes each characteristic of a respective facie, and the facies then displayed as a function of the depth of the respective associated sample.

BACKGROUND OF THE INVENTION 
This invention relates to the geological study of subsoils notably for the 
location and exploitation of mineral deposits. 
Mineral and petroleum prospecting is based upon the geological study and 
observation of formations of the earth's crust. Correlations have long 
been established between geological phenomena and the formation of mineral 
deposits which are sufficiently dense to make their exploitation 
economically profitable. 
In this endeavor, the study of the facies of the rocks encountered takes on 
particular importance. By facies, notably of a sedimentary rock, is meant 
a set of characteristics and properties of a rock which result from the 
physical, chemical and biological conditions involved in the formation of 
the sediment and which have given it its distinctive appearance with 
respect to other sediments. This set of characteristics provides 
information on the origin of the deposits, their distribution channels and 
the environment within which they were produced. For example, sedimentary 
deposits can be classified according to their location (continental, 
shoreline or marine), according to their origin (fluviatile, lacustrine, 
eolian) and according to the environment within which they occurred 
(estuaries, deltas, marshes, etc.). This information in turn makes it 
possible to detect, for example, zones in which the probability of 
hydrocarbon accumulation is high. 
There are various sources of information on the facies of formations. It 
may be provided by surface or subsoil observations, and notably by the 
study of core samples taken from rock studies, for example during the 
drilling of a borehole for an oil well. 
The geological characteristics used for recognizing a facie include, in 
addition to the fossil fauna and flora: 
The mineralogy, i.e. the mineral composition of the rock; silicate, 
carbonate, evaporite, etc.; 
The texture: grain size, sorting and morphology, degree of compaction, of 
cementation, etc.; these parameters can be of decisive importance as 
concerns the permeability of rocks also exhibiting porosity values and 
other similar ones; they are related to the microscopic appearance of the 
rocks; 
The structure: thickness of beds, their alternation, presence of stones, 
lenses, fractures, degree of parallelism of laminations, thickness of 
strata, etc.: all of which are parameters related to the macroscopic 
appearance of the rocks. 
The petrophysical and petrographic characteristics of a rock, excluding the 
paleontological data, constitute the litho-facies of the rock. This 
consequently includes the descriptive characteristics of the rock 
independent of the genetics of formation and notably of deposition. 
Other types of information coming from the subsoil can be used by the 
geologist for the investigation of facies. Such information can be 
provided by drill cuttings sent up to the surface from the bottom of a 
well by means of a fluid (generally mud) injected near the drilling tool. 
It has already been noted that certain measurements of the physical 
characteristics of the formations traversed by a borehole made it possible 
to obtain valuable information for the interpretation of the facies. 
Such measurements for determining the physical characteristics of the 
geological formations traversed by boreholes are presently carried out on 
a very large scale, notably in oil wells. They are carried out by means of 
sondes moved in the borehole, and the signals transmitted by the sonde 
give a recording (log) as a function of depth. They may involve highly 
varied characteristics resulting either from natural phenomena, such as 
the spontaneous potential or the natural emission of gamma rays, or from a 
prior stimulation of the formation by the sonde by the emission of 
electric current or acoustic waves, electromagnetic waves, nuclear 
particles, etc. 
In the case of petroleum prospecting, the logs are useful in determining 
accurately the hydrocarbon-bearing strata and at investigating, in 
addition to the nature and quantity of such strata, the possibility of 
extracting the hydrocarbons from the rocks in which they are contained. 
A substantial part of the log interpretation efforts up to the present time 
has tended toward the evaluation of the porosity of the reservoir rocks or 
matrices and their permeability, as well as the fraction of the pore 
volume occupied by these hydrocarbons. These so-called formation 
evaluation techniques generally also bring out other parameters such as 
the average matrix rock grain density and clay content. 
These interpretation studes have also demonstrated and used correlations 
between the measurements furnished by logging tools and certain 
compositional characteristics of the rocks traversed by boreholes. For 
example, it is common to plot certain information on the readily 
identifiable lithology of the formations encountered as a function of 
borehole depth, a prime example being the proportion of limestone and 
dolomite of a rock at a given level of the well. 
More recent studies have shown that very clear relations could in fact 
exist between the appearance or the evolution of certain characteristics 
in logging measurements and certain parameters of the litho-facies. 
This raised the question as to whether it would not be possible to 
establish a correspondence between the different facies or litho-facies 
encountered within a formation interval and all the logging data 
obtainable in this interval so as to establish an "Electro-facies" or a 
"para-facies" constituting an image of the facies or of the litho-facies 
of the rock as seen through the logs. 
This idea is based upon the notion according to which each log represents a 
response spectrum characteristic of the facies of the different zones 
along which it has been established, and that all these logs represent a 
respective "signature" of these facies or litho-facies. 
It has however not been possible up to the present time to develop a method 
which, in a relatively constant, reliable and systematic manner, makes it 
possible to establish, from only the logging measurements made in a 
borehole over a given depth interval, a recording or a respresentation 
notably in the form of a graph which furnishes, as a function of depth, an 
image of the succession of the litho-facies present within this interval. 
More specifically, it would be desirable to be able, on the basis of log 
measurements over a given depth interval in a borehole, to show within 
this borehole a set of sections or zones capable of being classified in 
correspondence (at least approximate) with the different facies or 
litho-facies present in the interval, so that all the zones corresponding 
to a similar facies or litho-facies belong to the same class. 
SUMMARY OF THE INVENTION 
It is the object of the present invention to provide a method and apparatus 
making it possible to obtain, in a reasonably reliable and essentially 
automatic manner, such a distinction and classification from log 
measurements over a depth interval of a borehole in order to produce one 
or more representations, notably graphs, characteristics of the facies 
present in this interval. 
This method includes in particular the following steps: 
Logs are made over a plurality of levels within an interval along the 
borehole in order to obtain a group of several measurements for each of 
these levels. 
With each such level of the borehole interval is associated a sample within 
a multidimensional space defined by the different logs. The sample's 
coordinates are a function of the logging values measured at this level. 
The sammples thus obtained will form a scatter diagram within this 
multidimensional space, 
The samples of this scatter diagram are investigated in order to determine 
a plurality of characteristics modes each corresponding to a zone of 
maximum density in the distribution of these samples; each is regarded as 
a characteristic of a respective cluster and all the samples of this 
cluster are related to it, and 
A recording is made, as a function of depth, for example in graphic form, 
of a geological or physical characteristic of the formations within the 
borehole interval, assigning to each level a characteristic value set as a 
function of the mode to which the representative sample of said level has 
been related within the multidimensional space considered. 
According to one feature of the invention, the exploration of the scatter 
diagram is done by analyzing the position of each of these samples in 
relation with the neighboring samples to define a respective density 
index, and the characteristic modes are determined by selecting local 
maxima of these density indices within the scatter diagram. 
According to one embodiment of the invention, a facies or litho-facies is 
designated for each of the modes thus characterized and a graphic 
respresentation is produced as a function of the depth of the succession 
of facies or litho-facies thus obtained, for example by means of suitable 
graphic codes. 
According to a preferred embodiment, the characteristic modes of each 
cluster or terminal modes are made up of samples coming from the 
measurements themselves. Each mode is thus characterized by log values 
actually obtained during the measurement. 
Advantageously the selection of said characteristic modes is effected in 
two phases. In a first phase, one determines a set of local modes on the 
basis of the analysis of the local density maxima mentioned earlier. In a 
second phase, from among these local modes is chosen a small number of 
terminal modes on the basis of their mutual distance according to a 
predetermined relationship. In particular, by a clustering technique, one 
determines the local modes which exhibit the greatest overall 
dissimilarities. These terminal modes are each considered to be 
characteristic of a super-cluster. To each of these terminal modes is 
related a sub-group of local modes previously determined. 
Thanks to this method, a limited number of classes is defined, each 
corresponding to one of the selected terminal modes which is characterized 
by a high density of representative points within this space and 
distinctive characteristics or dissimilarities which are clearly marked, 
according to the mutual distance of these modes in this space. In this way 
it is possible to enter each point of this space associated with a 
respective level in a class which can if necessary be characterized by a 
number or a set of values within the multidimensional space considered. 
These values are, of course, directly derived from the logging 
measurements and represent an "electro-facies" whose determination depends 
only on these measurements. 
As each level is related to a class, it is possible to plot a graphic 
representation of this electro-facies as a function of depth, in which 
each level is assigned an index corresponding to the class to which it is 
related. After such a procedure, it is observed that the values thus 
attached to the levels are ordered according to a stepped curve in which 
the distance of each step from the depth axis corresponds to the index of 
a respective class. Each step covers several adjacent levels which fall 
within identical classes on thickness zone of varying size, each 
corresponding to a different electro-facies, with several zones 
representing the same electro-facies being capable of being located along 
the interval considered, separated by zones of different facies. From a 
designation of the litho-facies corresponding to each class, it is 
possible to plot a pattern of litho-facies as a function of depth. p Using 
the just described method, it has been possible to reveal a remarkable 
correspondence between geological facies, as they may be determined for 
example from the analysis of core samples in a given interval, and 
electro-facies defined by respective sections in the interval and their 
respective classes. 
Thanks to this method, experimentation thus confirms the hypothesis 
formulated above whereby a determination, at least approximate, of the 
geological litho-facies can be derived from a suitable treatment of the 
logging measurements obtained within an interval of geological formations 
traversed by a borehole. 
The technique recommended here can be realized by means of the main types 
of logs now existing (e.g. resistivity, conductivity, density, neutron 
porosity sonic velocity, etc.) which will be referred to here as standard 
logs. 
This technique is moreover enhanced remarkably by the use of the results of 
dipmetering measurements which contain considerable structural 
information. This use can take on various forms. It is possible for 
example to integrate dipmetering results directly with the other logs. 
These results can then be expressed in the form of so-called synthetic 
logs which result from the extraction of special characteristics of 
dipmeter measurements. It is also possible to compare them with the depth 
of the transitions between electro-facies after a first processing in 
order to refine, according to an iterative process, the criteria making it 
possible to distribute the measurement samples in relatively uniform 
classes. 
In general, the technique just described can be applied on the level of a 
field in which are made several boreholes or wells which encounter 
formations exhibiting analogies from the geological viewpoint. 
Of course, there are preferred embodiments of the method according to the 
invention and these will be dealt with below. In addition, it goes without 
saying that the process for determining litho-facies from logs can only be 
advantageously guided by additional operations carried out manually or 
automatically and which can involve evaluation by experts or the specific 
knowledge of certain situations or regions as well as comparisons with 
other sources of information. 
According to a particular embodiment, the procedure involves two steps. In 
a first step, one considers a multidimensional space of points obtained 
from groups of consecutive levels in the order of depths preselected 
according to a given criterion. According to this criterion, the only 
levels considered to belong to such a group of consecutive levels are 
those between which the value of each log does not undergo a variation 
greater than a given deviation value determined in advance and 
corresponding to the effects of holes or to uncertainties inherent in the 
measurement. An example of points not considered to belong to the groups 
of selected levels are those in the vicinity of which the logs undergo a 
relatively marked transition or evolve according to a ramp, or adopt a 
configuration in the form of a bump whose height is greater than the 
deviation range previously defined. 
By means of groups of levels thus selected, the previously mentioned 
analysis is carried out in the multidimensional space of the logs. This 
gives a set of classes each of which characterizes an electro-facies 
present in the explored borehole interval. 
In a second step, the position of the points is analyzed at the levels not 
considered in the first analysis in relation to the groups of levels which 
have been classified after the first step. 
In particular, for each point or group of points not considered, an 
analysis is made of its position in relation to groups of adjacent, higher 
and lower, levels in the scale of depths and which have already been 
classified. This position analysis can be carried out advantageously 
through an analysis of the distances in the multidimensional space of the 
logs between the representative points of the levels not considered and 
the representative points of the adjacent upper and lower level 
classified, or a point considered to be representative of each group of 
such consecutive levels already classified. 
This study makes it possible to bring out either sudden transitions 
corresponding to the limit between distinct facies, or ramps which are 
relatively less steep and corresponding to transitions between facies 
whose respective characteristics are similar, or, in the case of 
bump-shaped configurations, the existence of beds or strata of small 
thickness exhibiting a facies different from the immediately surrounding 
facies. This distance analysis then makes it possible not only to 
determine the types of situation (ramp or bump) encountered but also to 
locate in depth the corresponding transition(s) between different facies. 
For example, in the case of a transition ramp between a sand and a clay, 
the separation level of the two facies is fixed at a point having a 
maximum distance, in the space of the logs, from the groups of consecutive 
adjacent levels in depth which correspond respectively to the sand and to 
the clay. In the case of a bed of small thickness, the assignment of a 
facies to this bed is also carried out on the basis of the distances of 
the points of this bed, in the multidimensional space of the logs, from 
the facies which surround it in the space of the depth. 
This, according to this embodiment, the first step identifies the 
electro-facies simply on the basis of stable sampling intervals by means 
of an analysis in the multidimensional space of the logs without regard to 
depth. On the other hand, the second step reintroduces the depth element 
from the assignment of facies to the sampling levels which were not 
considered in the first step. 
According to a preferred embodiment of the invention, one considers, for 
the determination of the modes characterizing the electro-facies, or 
terminal modes, a multidimensional space in which all the points or 
samples representative of the different levels in the interval considered 
are related to a system of axes made up of at least two main components of 
the scatter diagram formed by this set of points. Furthermore, it is 
preferred to make this determination of terminal modes in a space whose 
number of dimensions is small compared with that of the space of the logs 
used, by choosing or selecting only the most significant main components 
on the basis of their maximum variability with respect to the distribution 
of the scatter points. 
Thanks to this technique, it is possible to arrive at a selection which is 
both efficient, i.e. relatively fast if one considers the processing time, 
and correct, i.e. providing results which are utilizable in practice for 
determining the electro-facies. 
Of course, from the determination of a plurality of terminal modes over a 
given depth interval with the class assigned to each of them, it is 
possible not only to plot a stepped graph representative of the said 
levels assigned to the corresponding class index, or the representation of 
a sequence of facies, but also to plot a curve representative of any 
desired characteristic as a function of depth, by assigning to all the 
levels falling within the small class the same value of this 
characteristic, for example an average value for all the levels falling 
within this class. 
According to a feature of the invention, the distribution of the clustered 
measurement samples which are each characterized by a respective mode is 
used to simplify the log data interpretation operations or formation 
evaluation. These well-known operations make it possible to provide 
information on the porosity of the formation traversed and their fluid 
content, notably their hydrocarbon content, as well as the ease with which 
these fluids can flow within the cavities in which they are located. It 
has in fact been observed that it was possible to obtain good quality 
results for this evaluation by means of a data compression technique in 
which, instead of carrying out the required processing for each level of a 
borehole interval, one considers only the measurements obtained at the 
levels corresponding to chracteristic modes of a respective cluster. 
According to another feature of the invention, the designation of the 
litho-facies corresponding to each measurement sample or to each mode is 
carried out in an essentially automatic manner. For this purpose, one 
considers, in the space of the logs plotted, a volume characteristic of 
each litho-facies capable of being encountered in the formations traversed 
by the borehole interval considered. These volumes are defined from 
previous measurements carried out in these formations under given borehole 
conditions. An analysis is made of the position of each measurement sample 
or characteristic mode of a cluster in the multidimensional space of the 
logs in relation to these characteristic volumes to assign to each of 
these samples or modes a corresponding facies or litho-facies. This 
assignment can be made on the basis of a calculation of the distances from 
the sample or mode considered to the point defining each volume 
characteristic of a distinct litho-facies. In the event of conflict 
between concurrent litho-facies, provision is made for differentiating the 
latter on the basis of their respective possibilities. 
According to an embodiment of this invention, from the possible 
litho-facies a subassembly of probable litho-facies in the considered bore 
hole interval is selected. Each is then assigned a respective plausibility 
index. These plausibility indices can be used to clear up conflicts or 
ambiguities in the determination of the litho-facies of the measurement 
samples. 
These most plausible litho-facies are advantageously selected by means of 
an artificial intelligence procedure in which one takes into account not 
only the results of a rough analysis of the lithology of the borehole 
interval obtained from respective logs but also information relative to 
the morphology of these logs and also data external to the borehole. This 
step can advantageously be applied by means of an interactive program in 
which a machine initiated dialog is established between a processing 
machine and an operator who gives the machine the data at his disposal.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
In FIG. 1 is shown logging equipment in a borehole 10 going through 
sedimentary formations 12 made up of strata represented schematically by 
separation planes such as 14 and 15. The equipment includes a sonde 16 of 
elongated form and capable of being moved in the borehole 10 at the end of 
a cable 18 from which it is suspended and which connects it both 
mechanically and electrically, by means of a pulley 19 on the surface, to 
a control installation 20 equipped with a winch 21 around which the cable 
18 is wound. The control installation comprises, notably, recording and 
processing equipment known to the art and making it possible to produce 
graphic representations called logs of the measurements obtained by the 
sonde 16 according to the depth of the sonde in the borehole or well 10. 
This depth is effectively a function of the length of cable wound and is 
detected by means of a roller 22 bearing on the cable. 
FIG. 2 represents the results of a geological analysis of a core sample in 
a borehole. The results are presented as a function of depth in a log 30 
using a conventional representation of the geological structure revealed 
by the core sample. This representation 30 is composed of a series of beds 
such as 31, 32, 33 or zones of facies differentiated by a different 
symbolic representation. That of the bed 31 corresponding, for example, to 
a clay, that of the formation 32 to a limestone, and that of the formation 
33 to a different variety of limestone. The facies of these different 
zones is described in a column 35 entitled "Description". From the 
geological viewpoint, each of these successive zones is characterized by a 
relative homogeneity defined by a set of characters which vary from one 
zone to another. These characters, which depend in particular on the 
mineralogical composition, the texture and the structure of the rocks 
making up these zones, define respective litho-facies. Their study makes 
it possible to obtain information on the conditions under which these 
rocks were formed. 
The response of sonde 16 as it is moved in the borehole 10 depends on the 
formations traversed by the borehole. The sonde applies different 
measurement techniques to the formations traversed and it is thus possible 
to obtain information of a quantitative type on the reservoirs encountered 
thanks to a suitable combination of the information provided by different 
types of logs. These have been standardized so as to furnish measurements 
at discrete levels separated by equal depth intervals. The techniques for 
correlating different logs in depth for this purpose are of a classical 
nature. They allow the automation of measurement data interpretation in 
order to obtain, for example, estimates of the porosity of the rocks 
encountered, the pore volume occupied by hydrocarbons, and the ease of 
flow of hydrocarbons out of the reservoirs in the case of petroleum 
prospecting. 
Techniques for analyzing formations of the type mentioned above are well 
known, for example under the name of SARABAND and are described for 
example in French patent published under the number 2,080,945 
corresponding to U.S. Pat. No. 4,495,604. 
However, as each of the different logs is affected not only by the fluid 
content of the rocks traversed by the borehole but by all their other 
physical characteristics, it is desirable to also consider obtaining 
information on the geological structure of the formations encountered 
which, in turn, can be used for determining, by actual geological 
considerations and on the scale of a field, a basis or a region, the 
probable accumulation zones of hydrocarbons having an economic value. 
FIG. 3 represents a table providing information of a qualitative nature on 
the sensistivity of the different types of logs to the main geological 
factors characterizing the rocks in which these logs are made. 
In the left-hand column 50 of FIG. 3 are indicated different parameters of 
which a measurement can be obtained by logging, opposite which, in column 
51, is given an abbreviated designation of the corresponding tool. The 
methods and tools making it possible to obtain the 16 parameters (a) to 
(p) of column 50 are all well known in the field of geophysical 
measurements in boreholes. In the patents previously mentioned can be 
found references to documents describing these measurement methods. We 
shall thus confine ourselves here to indicating certain patents covering 
some of the most recent logging methods: for electromagnetic progagation 
time parameter (c), U.S. Pat. No. 3,944,910; for electromagentic wave 
attenuation parameter (d), U.S. Pat. No. 3,944,910; for natural gamma ray 
spectrometry parameter (f), U.S. Pat. No. 3,976,878; for the photoelectric 
capture cross section parameter (i), U.S. Pat. No. 3,922,541; for the 
thermal neutron capture cross section parameter (j), the U.S. Pat. No. 
3,971,935; and for nonelastic gamma-ray spectrometry parameter (m), U.S. 
Pat. No. 4,055,763. 
For each type of log (a) to (p) has been indicated in column 52 the degree 
to which it is influenced by the mineralogy of the rock encountered, using 
three types of characters ranging from the boldest to the smallest 
depending on whether the parameter in question is more or less sensitive 
to this mineralogy. 
Similarly, in column 53 has been indicated for each of the parameters (a) 
to (p) its sensitivity to the texture of the rocks traversed by the 
borehole and, in column 54, its sensitivity to the structure of the rocks, 
according to a scale with three degrees similar to that used for column 
52. 
Finally, in column 55 is given a qualitative indication of the sensitivity 
of the parameters (a) to (p) to the fluids contained in the formation, 
fluids which can in particular consist of petroleum, gas or water of 
variable salinity. 
The observation of the table in FIG. 3 suggests that it is possible to 
establish a correspondence between, on the one hand, different 
litho-facies characterized by the mineralogical factors, texture and 
structure and, on the other hand, electrofacies which can be obtained 
directly from a suitable quantitative analysis of a set of logs taken for 
example from the logs of column 50. 
The possibility of establishing such a correspndence between electro-facies 
and litho-facies is capable of providing a precious aid in the geological 
knowledge of a zone of the earth's crust within a given region, such 
knowledge being useful in completing the information usually available to 
geologists and, in certain cases, helping them in the interpretation of 
the facies encountered to obtain information on the history of the 
formations and for determining the concentrations of minerals sought. 
It was thus noted that it was possible to make use of the specific 
sensitivities of each log in a set of logs at least to produce an 
approximate image of the litho-facies. This image is obtained from an 
investigation of zones over a given interval of a borehole and is capable 
of being classified in a set of classes. 
To accomplish this, one begins with n number of logs obtained over a 
borehole interval H.sub.1, H.sub.2 (FIG. 1). The measured values are 
discretized and correlated in depth so as to have, for each level of the 
interval considered, a plurality of distinct log values. A typical value 
of an interval between consecutive levels is 15 centimeters (six inches). 
According to a preferred embodiment, the successive log values thus 
obtained in 15-centimeter intervals are analyzed so as to determine groups 
of consecutive levels for which said log values remain within a range 
defined by an upper value and a lower value. This range is determined by 
considering the interval of the possible variations of each log according 
to borehole conditions, for example the roughness or caving of borehole 
walls, and errors inherent in the measurement itself. It is in fact 
possible to consider that, for all the levels whose measurements fall 
within such a range, the physical characteristic measured by the log 
conserves a substantially constant value. Within a given depth interval, 
it is possible to find several groups of levels which fulfill this 
condition for average values of this characteristic which may be 
altogether different from each other. 
Outside of the groups of consecutive levels which are characterized by a 
certain stability of the corresponding log value are found other levels 
which do not exhibit these stability characteristics. In particular, if a 
given log is considered, there are ramp phenomena in which a first and a 
second group of consecutive levels, for which the log is relatively stable 
at a first and a second respective characteristic value, are connected by 
levels for which the log evolves between the characteristic value of the 
first group and the characteristic value of the second group. There are 
also situations in which, between two groups of levels characterized by 
relatively stable values, there are consecutive levels for which the 
values of the log are quite far from the first and the second values 
characteristic of each of the two stable level groups, thereby revealing 
the presence between these two groups of a stratum or a bed of small 
thickness whose geophysical characteristics are substantially different 
from those of the beds surrounding it. In this case, the log shows a 
relatively marked bump or peak. 
In both cases, these bumps and these ramps are the result of a convolution 
of the measurement owing to the insufficient resolution of the measurement 
tools. 
In FIG. 5 a log is represented in which the curve 105 represents the 
fluctuations of a variable Li as a function of the depth measured in the 
direction perpendicular to the axis L.sub.i. In this figure a portion 115 
of the curve 105 has values which are located between a bound L1-.DELTA.L 
and a bound LI+.DELTA.L for a set of consecutive levels. 
One also notes for a portion 116 of the curve another group of consecutive 
levels whose values remain between two levels L2+.DELTA.L and L2-.DELTA.L. 
The transition between the portion 112 and 116 is relatively sudden. The 
difference 2 .DELTA.L corresponds to a tolerance interval for the 
measurements of the characteristic Li as a function of the borehole 
conditions and of the uncertainty inherent in the measurement. 
Also represented is a portion 117 of the curve 105 which forms a bump with 
a relatively steep front such that it is not possible to define over the 
depth interval .DELTA.P a group of levels for which the measured value L 
remains within a variation range 2 .DELTA.L. 
At 118 another type of situation is represented in which such a group of 
levels cannot be recognized. What is involved is a ramp 118 in which the 
value of the measured variable tends to grow more or less regularly 
between a first and a second portion 113 and 119 of the log 105 and in 
which the level of the log remains relatively stable at different 
respective values. 
According to the preferred embodiment described here, a first analysis is 
made of all the logs to identify only the measurements which correspond to 
groups of consecutive levels in which these measurements can be regarded 
as stable. The following discussion will exclude all the levels which do 
not correspond to such stable groups. 
We consider a space with several dimensions, each corresponding to one of 
the n logs. With each depth level not excluded may be associated a point 
in this space of which each coordinate consists of the value of the 
measurement of the respective log. 
Thus, in the space considered, all the measurements made over the interval 
H.sub.1, H.sub.2 are represented by a scattering of points or samples. 
The observation of such a scattering of points shows that, in practice, for 
a given depth interval, the distribution density of these points is far 
from being uniform. These points tend to group in the form of clusters in 
which the concentration of the points is relatively high, and which are 
more or less clearly separated from each other by zones containing 
relatively few points. Each of these clusters corresponds to a set of 
particular physical characteristics which it may be desired to represent 
by a characteristic point of this cluster. 
In order to facilitate the interpretation and the characterization of said 
clusters, it is convenient to proceed, by means of data processing 
equipment in control installation 20 of FIG. 1, with a series of 
operations represented in FIG. 4 
We begin with the scatter of points defined by the value of the different 
logs at the different selected levels of the investigated borehole 
interval. These values are stored in memory, block 81, FIG. 4, and an 
analysis is made of the main components of this scatter in space (or axes 
of maximum inertia) in step 82 according to a technique which will be 
described later. 
We then consider a reference system whose coordinates are the main 
components determined in step 82 and we transform the coordinates of the 
points of the scatter of the space 81 so as to represent them in step 83 
in the space defined by the main components. 
The number of dimensions of this space is then, in step 84, reduced so as 
to adopt for the representation of the scatter only the coordinates 
defined by the most significant main components and so as to reject those 
which correspond essentially to measurement noise. The scattering of 
points associated in this space with each depth level is thus defined by a 
number of coordinates smaller than the number of original logs. This 
reduction in the number of dimensions is made possible by the existence of 
correlations between the logs, with the elimination of the other factors 
corresponding to a negligible loss of information in many situations. 
A graphic representation is made preferably of the values of each of the 
main components adopted as a function of the depth of the levels with 
which they are associated so as to obtain, in step 86, logs of main 
components which we shall call PC logs hereinafter. 
In the space of the selected main components, an analysis, in step 88, is 
made of the clusters composing the scatter of points by a data compression 
technique in which, for each cluster of points in the space, a local mode 
is defined which is obtained here by the selection of one of the points of 
this cluster where there is a maximum point distribution density. 
The analysis is continued by a study, in step 89, of the mutual distances 
between the local modes thus determined which can be represented 
graphically by a tree-type construction or dendrogram. 
A selection is then made from among the local modes according to their 
mutual spacing so as to adopt, in step 90, only a limited number of modes 
called terminal modes whose mutual distances are the greatest and which 
correspond to respective groups of characteristics exhibiting maximum 
dissimilarties. This selection is followed, in Step 91, by the assignment 
of each of the scatter points, in the reduced main component space, to one 
of the selected terminal modes, all the point assigned to a terminal mode 
being grouped in the same class. Each class, in step 92, may be assigned 
an index taken, in this example, equal to the value of the first main 
component (PC.sub.1) of the respective terminal mode. 
Thus, each of the levels belonging to one of the groups of selected levels 
at the outset for the first step of the analysis can be related to one of 
the classes just determined. Consecutive level groups can then appear in 
the space of the depth which belongs to the small class and thus 
correspond to what is referred to as the same electro-facies. 
The method includes a second step in which each of the levels set aside 
during the first step is also assigned to a class of electro-facies. 
During this second step, each of the levels initially set aside is tested 
to determine whether it belongs to a ramp or a bump. The test is carried 
out in the space of the main components by calculating the distance from 
the representative point of this level in this space to the representative 
points of the two groups of consecutive levels classified during the first 
step and which are closest to the tested level, on each side of it in the 
space of the depths. This distance calculation can be carried out with 
respect to each point of this group of levels or any of them, for example 
a representative local mode. 
It is significant that this determination of the distance of each of the 
points initially set aside is carried out with respect to two groups of 
adjacent classified levels, one over and the other under in the space of 
the depths. Such a distance analysis for all the levels corresponding for 
example to the portions 117 or 118 of the curve 105 of FIG. 5 makes it 
possible to show the type of evolution of the characteristics calculated 
for each of the levels separating two groups of classified levels. It 
makes it possible in particular to show evolutions in the form of a ramp, 
a bump or a bell. 
In the case where a ramp is thus detected, it is assumed that each point of 
the ramp belongs to one of the two electro-facies which flank it and we 
place the transition between these two electro-facies at a point of this 
ramp which is equidistance in the space of the main components from the 
points representative of two groups of adjacent levels in the space of the 
depths. 
In the case of a bump-shaped distribution, such a distance analysis makes 
it possible to determine the transition levels in depth limiting a 
relatively thin bed. As a function of the maximum Euclidean distance 
between the points representative of the levels of this bed and the 
surrounding groups of classified levels, this distance analysis also makes 
it possible to assign this thin bed to one of the classes already 
determined during the first step or to assign it a different 
classification if none of the classifications already determined during 
the first step appear to correspond to it. 
After these two steps, the classification obtained can then lead to the 
plotting, in Step 93, of a pilot curve in which is represented, as a 
function of depth, the class index assigned to each level. A stepped curve 
100, shown in FIG. 5, is thus obtained in which there are as many 
different step values as there are selected classes. Thus, for example, 
the plateau 102 is composed of a succession of adjacent g levels in a 
given depth zone Hi, Hj within the borehole interval H.sub.1, H.sub.2 and 
which, after the analysis of the blocks 88 to 92 and 85, fall within the 
same class represented by the abscissa 103. 
This pilot curve 100 illustrates the fact that, after the analysis, the 
adjacent levels tend to group in zones of variable thickness, with several 
distinct zones corresponding to the same class, i.e. having similar 
characteristics which may be shown within the considered interval. The 
curve 100 is thus a numerical electro-facies log, each class index number 
constituting a brief identification of a respective electro-facies. 
An optional step, shown in step 94, for the purification or refinement of 
results in which manual intervention may in certain cases permit certain 
corrections and allow the introduction into the process of information 
obtained through knowledge of local geology. 
One of the products of the pilot curve 93 can consist of a 
rectangularization of the logs forming the starting point 80 of the 
process, steps 95 via 96 in FIG. 4; this step can benefit from a possible 
refining operation in step 94. 
According to an embodiment of the present invention, the rectangularization 
of a log can be carried out by plotting, for each depth zone as defined by 
a plateau such as 102 of the curve 100, a plateau whose abscissa on the 
scale of the parameter Li measured by this log is equal to the average of 
the values of this log L.sub.i for all the levels which fall within the 
index class 103. 
The log L.sub.i 105 has been represented in solid lines as a function of 
the depth as actually measured, the broken lines being the rectangularized 
curve 106 for this same measurement parameter. It is noted in particular 
that for the zones 102 and 108 defined by the pilot curve 100 and which 
are assigned the same class index 103 the respective plateaus 112 and 113 
of the rectangularized curve are at the same abscissa value 114. 
A subsequent product of this step 76 can consist in plotting zoned logs, 
i.e. logs assigned a single and unique value for all the levels which fall 
within a given class of the electro-facies log 100, this value being the 
value actually measured at the level which corresponds to the terminal 
mode giving rise to the considered class. The zoned logs are thus made up 
of values actually measured and not of averages, unlike the product of 
step 95. 
In a particular embodiment, the value of the first main component is 
selected for all the levels of the same class and the average is 
calculated, this value being compared with the corresponding class index, 
or modal value, of said main component for the terminal mode corresponding 
to this class. The different between this average and this modal value 
furnishes a quality of criterion for the electro-facies represented by 
this class. 
On the basis of the process leading to the numerical electro-facies log 
just defined, it is possible to produce an image of the litho-facies of 
the formations encountered. The correspondence between litho-facies and 
electro-facies may if necessary be detailed by an intervention involving 
data external to the logs. It is of interest to furnish, from this 
analysis, at step 97, a graphic representation of the litho-facies by 
means of conventional diagrams similar to those represented for example in 
the left-hand part of FIG. 2. This graphic representation can be obtained 
by recording means or automatic plotting means in a graphic band parallel 
to the axis of depth along with other recordings either of original 
measurements or of the results of their processing. 
The determination of the terminal modes and the local modes, can also be 
used in order to simplify the calculations needed for interpretative 
processing of measurements such as that carried out in order to analyze 
the formation according to conventional interpretation techniques using a 
computer. 
Finally, the graphic outputs of the step 97 may benefit, not only from the 
operations of step 94 but also from the establishment of correlations with 
dipmetering products. It is possible in particular to use, in step 98, 
along with the curves of electro-facies or derivatives of their 
determination, results of programs such as GEODIP (see for example French 
Pat. No. 2,185,165, and U.S. Pat. No. 4,320,458). 
The dimension compression technique summarized in reference to steps 82 to 
84 of FIG. 4 is described below in greater detail. 
When one analyzes a scattering of points representative of the logs carried 
out on a succession of levels in a borehole interval, it is noted that the 
distribution density of these points in the scatter varies. 
It is noted in particular that the points of the scatter are not 
distributed in the same manner in all directions. On the contrary, they 
tend to become oriented in privileged directions corresponding to 
directions of maximum variability of the distribution of these points in 
this space. Thus, in a two-dimensional space, a scatter represented by the 
FIG. 6 tends to fall within an approximately elliptical envelope 140, and 
the maximum variability directions of this scatter can be represented by 
the major axis 142 of this ellipse for the main direction of variability, 
or first main component, and by the minor axis 144 of the ellipse for a 
second component or main direction of variability in a direction 
perpendicular to the first. The clusters of points encountered can take on 
very diverse forms. For example, in the diagrams with two dimensions, one 
consisting of neutron porosity measurements (CNL tool) and the other of 
density measurements such as they result from an FDC tool, there are point 
distributions having a form similar to that of a boomerang. This is 
typical of the presence of zones with a high percentage of clay in 
conjunction with sand or sandstone. 
The first main component is determined, by statistical processing, as the 
line for which the sum of the Euclidean distances from the points of the 
scatter to this line is smallest. In other words, if the points of the 
scatter are projected orthogonally on this line, the sum of the distances 
between these points and their respective projections is the smallest. The 
second main component is determined by considering, from among the lines 
perpendicular to the first main component, that for which the sum of the 
distance from the points of the scatter to this line is smallest. What is 
involved is hence a determination in a sub-space excluding the dimension 
of the main component. If the scatter is plotted in n dimensions, it is 
possible to determined n main components each corresponding to a direction 
of maximum variability in a sub-space perpendicular to the previously 
determined components. 
The mathematical processing which makes it possible to obtain the main 
components includes the determination of the scatter correlation matrix. 
Each term of the correlation matrix between two dimensions i and j (i.e. 
two different measurements or logs) obeys the definition: 
##EQU1## 
in which the coeffients .sigma..sub.ij are covariance coeffients 
determined by the relation 
##EQU2## 
In these relations, x.sub.l.sup.i is the value of the log i for the point 
x.sub.m.sup.i is the average value of the measurements of logs which can 
be expressed in the form: 
##EQU3## 
is the total number of points in the scatter. 
In view of the correlation matrix of the scatter, according to the 
expression: 
##EQU4## 
the main components of the scatter are the actual vectors of this matrix. 
In the determination of the correlation coefficients, we normalize 
perferably the value of the measurements before the calculation. This 
normalization can be carried out for example by bringing the variability 
interval of all the logs to a common value, for example 0 to 100. Of 
course, other hypothesis can be used. For example, for certain 
measurements such as resistivity, the logarithm of the measurement can be 
normalized. 
Thus, the first main component PC.sub.1 is the direction of maximum 
variability. 
The second main component PC.sub.2 is the direction of maximum variability 
of the scatter in a plane or a hyper-plane perpendicular to the first main 
component. 
The third main component PC.sub.3 is the direction of maximum variability 
of the scatter perpendicular to the preceding two main components, and so 
on. 
When all the main components PC.sub.1 to PC.sub.n corresponding to the 
scatters have been determined, the coordinates are changed to express the 
position of the points of the scatter in the system of axes made up of the 
main components (or maximum inertia axis) thus determined. The 
transformation takes place linearly by means of a computer using the 
knowledge of the specific vectors of the correlation matrix. 
In practice, the existence of main components or main axes of inertia 
reflects the presence of correlations between the physical characteristics 
measured by the logs. The coordinates of the points of the scatter along 
the first main component, for example, furnish a measurement of an 
underlying factor present in the formations encountered by the sonde and 
for which the different types of measurements made have a tendency to 
respond in the same direction in a more or less marked manner. 
We consider for example a borehole interval over a depth of about 180 
meters and including 120 equidistant measurement levels spaced by about 15 
cm each. 
In this borehole interval the following logs have been plotted: 
RHOB: density measurement 
PHIN: porosity measurement by CNL neutron tool (see Table in FIG. 3) 
GR: natural gamma radiation measurements 
HRT: temperature measurement 
HRXO: inverse of square root of resistivity measurement near the wall of 
the well in the "invaded" zone 
DT: acoustic wave transit time measurement 
RT: measurement of resistivity of formation far from well or borehole 
RXO: measurement of resistivity near borehole wall. 
Only the first six measurements are considered for determining the main 
components. They are called active logs as opposed to the lasst two 
resistivity measurements called passive logs. 
An elementary statistical analysis is made of the values obtained for these 
different measurements. From this is determined, for example, the average, 
the standard deviation, the maximum, the minimum and the dynamic interval 
between consecutive values. A calculation is then made of the correlation 
indices between each type of measurement carried out in the borehole 
interval. The results of this calculation can be expressed by a 
correlation matrix according to the relation (4) above. This matrix is 
constructed in the six dimensional space of the active logs. 
The specific vectors of the correlation matrix are then determined. It is 
possible to deduce therefrom the inertia of the scatter of 1200 points in 
relation to each of the main components. The calculated values appear in 
the table below: 
______________________________________ 
Main Axis Inertia In Percent 
Cumulative Percent 
______________________________________ 
1 1.5130 85.5 85.5 
2 0.5633 9.4 94.9 
3 0.1389 2.3 97.2 
4 0.0957 1.6 98.8 
5 0.0452 0.7 99.5 
6 0.0269 0.4 100.0 
______________________________________ 
It is noted that the inertia of the scatter in relation to the first main 
component is 85.5% of the total of the inertias. The cumulative inertia 
relative to the two main components amounts to 94%. 
This means that by themselves these two main components make it possible to 
translate the major part of the information contained in the logs in 
relation to the formations traversed by the considered borehole interval. 
The determination of the correlation coefficient of each log carried out 
with the six main components obtained reinforces this observation. These 
components are designated PC.sub.1 to PC.sub.6 in the order of decreasing 
inertia in the following table: 
______________________________________ 
Log PC.sub.1 
PC.sub.2 PC.sub.3 
PC.sub.4 
PC.sub.5 
PC.sub.6 
______________________________________ 
RHOB 0.907 0.315 0.244 0.133 0.005 0.036 
PHIN -0.975 -0.107 -0.064 
0.128 0.064 0.116 
GR -0.745 -0.658 -0.078 
-0.075 
0.032 0.001 
HRT -0.970 -0.099 0.160 0.079 0.128 -0.081 
HRXO -0.958 -0.074 0.209 -0.155 
-0.082 
0.052 
DT -0.972 0.069 -0.014 
0.176 -0.130 
-0.053 
Passive 
RT 0.746 0.154 -0.286 
0.007 -0.165 
0.137 
RXO 0.763 0.126 -0.281 
0.064 -0.137 
0.029 
______________________________________ 
The coefficients in this table each illustrate the extent to which the 
values of the main components associated with the different points of the 
scatter correlate with each of the logs plotted in the borehole interval. 
It is thus possible to say that if a log is plotted as a function of the 
depth of the values of the first main component for each of the levels of 
the scatter, one obtains a correlation index of almost 90% between the 
variations in this first main component and that of the measured density. 
It is noted that these correlations are very close between the first main 
component and each of the measurements made. They are less so, except in 
the case of the gamma-ray measurement, for the second main component. They 
tend to become negligible for the other main components. 
This observation is used to limit the analysis of the scatter of points 
representative of measurements carried out on the considered borehole 
interval to a space with two dimensions, possibly three, if we remain 
within the space of the main components. This reduction in dimensions in 
fact makes it possible to simplify the operations for recognizing clusters 
in the scatter and the characterization of each cluster. 
The dimensional compression of the space of the scatter analyzed, in step 
84 of FIG. 4, can thus be carried out using only the main components whose 
cumulative inertia exceeds a predetermined threshold. 
This selection can be completed and its validity verified by other means. 
For example, it is possible to plot the logs of the values of the main 
components rejected or to be rejected over the interval. 
FIG. 7 represents a plot of two logs 160 and 162 corresponding to the third 
and fourth main components of the scatter considered as a function of the 
depth of the interval from which the measurements are taken. It is noted 
that the variations of these two curves are of small amplitude and do not 
exhibit salient characteristics. Their general form is related to a noise, 
which reflects well the notion already expressed whereby the information 
content of each main component decreases in the order of decreasing 
inertia. 
On the other hand, the observation of projections such as 165 and 167 in 
the logs of the third and fourth main components is capable of providing 
valuable information either on the presence of factors having a particular 
geological significance but having no influence on certain measurements, 
or because, for example, they denote errors on one of the measurements. 
Except for these anomalies, the elimination of the main components of 
secondary order corresponds to a filtering operation, which explains why 
the rest of the processing in a space with dimensions reduced by the 
selection of certain main components can furnish results of good quality. 
Another means of checking to what extent the selection of a limited number 
of main components is justified consists in imposing an inverse coordinate 
change on the points of the scatter from the space of reduced dimensions 
in order to restore them in the space of the measurements actually carried 
out and reconstitute the logs from each of the components of the points 
thus restored. It is often observed that it is sufficient to conserve two 
main components out of six from the measurements made in the considered 
interval to obtain excellent correspondence between the original logs and 
the logs so reconstituted. 
FIG. 7 illustrates the curves 190 and 192 corresponding respectively to the 
plotting of the values of the first and second main components PC.sub.1 
and PC.sub.2 of the points of the scatter obtained in step 86 of FIG. 4. 
In FIG. 7 are also shown four curves, respectively 194, 195, 196 and 197, 
corresponding to the logs of density RHOB, neutron porosity PHIN, transit 
time DT (acoustic) and GR (natural gamma radiation). These logs make it 
possible to note a rather clear correspondence between the measurement 
logs and the PC logs, for example at the transition levels indicated by 
the references 198 and 199. 
The analysis of the scatter in the reduced space of the main components is 
continued by the determination of clusters within this scatter. These 
clusters are made up of regions of the space containing a relatively high 
density of points separated from other similar regions by regions in which 
the density of points is relatively low. 
FIG. 8 represents a flow chart of this phase of analysis. It illustrates 
the different processing operations which we will now review and which can 
be carried out by means of data processing equipment programmed for this 
purpose. It covers substantially the operations described by the steps 88 
to 92 of FIG. 4. 
The data corresponding to the scatter may be stored in step 200. For each 
point of the scatter, we determined the closest Kth neighbor in the space 
of the selected main components step 202 NEIGH. 
This operation corresponds, considering a number K=3 of neighboring points, 
to determining from points such as 250 and 260 (FIG. 9) the radius 
dk.sub.1 of the hypersphere which contains the three points 251, 252 and 
253 nearest the point 250 and, for the point 260, the radius dk.sub.2 
which contains the three points 261, 262 and 263 nearest the point 260. 
This proximity is expressed by Euclidean distance measurements in the 
considered space. 
For a given number of neighboring points K, the inverse of the distances 
dk.sub.1, dk.sub.2 represents a measurement of the local density of the 
points of the scatter around the point 250, 260 considered. 
The input data for the step NEIGH 202 are thus the number K and the PC logs 
selected for the representation of the scatter. The output of this phase 
is a number dk for each point of the scatter. 
The next operation in step 204 is designed to determine local modes (output 
205) which correspond to points of maximum density in the scatter 
according to an analysis of the values dk. This analysis is performed in 
one and/or the other of the two spaces. The first space is the depth. All 
the values dk are scanned according to the level of the associated points 
to determine whether, in a given window or section of depth I around a 
level, no other level has a lower index dk. A level fulfilling such a 
condition is then considered to be the local depth mode. The indication of 
the width of the section of depth I is symbolized as being transmitted on 
an output 207 of the step LOCMOD 204. 
The second space is that of the main components where the considered 
scatter is defined. We determine all the points of which the nearest K 
points are assigned a higher dk value. A second set of local modes is thus 
obtained. 
For the rest of the operations, we select local modes constituting the 
output of this step 204 under the control of a step 206 LMSA corresponding 
to a local mode search algorithm according to one of the following 
procedures: 
1. (BATH): exclusive use of only local modes coming from the analysis of 
depth; or 
2. (DELF): only local modes coming from spatial analysis; 
3. (BAID): use is made of only the local modes common to the two sets 
considered (intersection of two sets); 
4. (BAUD): all local modes determined are used (union of sets of local 
modes determined in depth and in space). 
Each of the local modes thus selected is representative of a cluster whose 
density at this point has a maximum value or at least one which is high 
locally. By an operation represented by block 210 we attach each of the 
points of the scatter to the local modes to which it is closest on the 
basis of its Euclidean distance in the space of the scatter. The value of 
this attachment stage will become apparent later. 
Its main effect is to allow a compression of data thanks to which the 
initial scattering of points is represented only by the local modes thus 
selected. On the basis of this sparse scatter, it is possible to carry out 
a certain number of operations of an already known type in the 
interpretation of log data, for example analyses of characteristics of the 
formation which make it possible, with a considerably reduced calculation 
volume, to obtain precise results. 
After the attachment step 210, we proceed with an operation for connecting 
and grouping segments (UPLINK, AGGREG) in step 212 which can result in the 
establishment of a dendrogram. We consider (FIG. 10) all the local modes 
selected after the phase 204 and we look for the two local modes 300 and 
302 whose Euclidean distance is the smallest in the space of the PC logs 
of a considered scatter. The segment thus determined is oriented in the 
direction of the mode whose index dk is minimum (maximum density). This 
vector is thus oriented in the direction of increasing densities and is 
characterized, for the search for new segments, only by its end 302. Its 
origin is no longer considered in the following search. 
We then search for the segment of minimum length immediately following, 
made up here of the points 304 and 306 oriented in that order. Then we 
determine the smallest segment remaining in this space after the 
elimination of the origin points 300 and 304 which, in this example, is 
the segment 306, 302 leading to the elimination of the point 306. The 
process is continued in this manner until all the points of the scatter of 
local modes have been grouped by segments whose length will increase as we 
progress in the scatter. 
The end of the last segment thus selected corresponds to the minimum index 
dk for all the considered local modes. In addition, it may be noted that 
the last r segments selected during this process have lengths greater than 
those of the previously determined segments. They moreover correspond to 
high densities in all the densities of the local modes considered. Thus, 
the local modes which are at the ends of these r segments of maximum 
distance have a set of characteristics which, on the whole, are the most 
mutually dissimilar among all the local modes considered. It is thus valid 
to select the r ends of these segments as terminal local modes 
representative of groups of distinct characteristics defining a class 
corresponding to a litho-facies and with which are attached a plurality of 
levels whose measurements reflect similar if not identical 
characteristics. 
The method of grouping the local modes just described is characterized by 
the name of hierarchical single link up-hill, conglomerate clustering. It 
makes it possible to represent the distances between the local modes 
analyzed in the form of a tree structure called a dendogram from which a 
selection of certain of these modes, called terminals, can be made in 
accordance with a minimum distance threshold. 
On the basis of this selection of terminal modes, it is possible to attach 
to each of these all the other local modes which are associated with them 
by links or segments, but which were assigned a lower density value 
(higher dk). It is also possible to enter in the same class, for the 
selected terminal mode, all the levels which were attached in step 210 to 
each of the local modes associated with this terminal mode. 
Thus, following this operation, all the levels of the scatter analyzed are 
classified in as many classes as there are terminal modes. Each of these 
classes can be characterized by a number or index attached to this 
terminal mode, for example, the value of the first main component for each 
terminal mode. As already indicated, it is possible to establish from this 
classification a stepped pilot curve in step 93 of FIG. 4. It is 
sufficient for this purpose to plot, for each level, as a function of 
depth, the index value of the corresponding class to obtain a stepped 
curve whose shape provides utilizable information in correspondence with 
the litho-facies of the formations encountered. 
A precise example of the application of the link up-hill clustering 
technique is set forth below with reference to a scatter of points 
distributed in a space defined by three main components taken, after 
corresponding coordinate changing, from measurements carried out on the 
previously discussed 1200 levels of the borehole. 
The local modes have been selected on the basis of a number K =5. The width 
of the window or section I used for the selection of the local minima in 
depth is 2. The local mode search algorithm provides a selection of the 
local modes in the space of the depth and in that of the main components 
(BAID mode). 
The number of local modes adopted according to this algorithm on the 1200 
levels is 69. 
During a first phase, the distances of these local modes are selected by 
determining for each local mode the nearest local mode, the two nearest 
local modes of the whole constituting the first segment 300, 302 of FIG. 
10. In FIG. 16, the 69 segments appear in order of decreasing lengths. 
The origin and end local modes are identified by their respective 
appearance numbers from 1 to 69 in the scale of increasing depth and, for 
the origin modes, also by their respective level number. 
In this table and in the one of FIG. 17, produced by means of a computer, 
the entry E-01 at the end of a numerical value means that this value must 
be multiplied by 10.sup.-1 to obtain the value of dk. 
It is noted that the smallest segment corresponds to the link between mode 
4 and mode 5. It appears at the bottom of the table in FIG. 16. 
Mode 5 appears in the "segment end" column according to the rule indicated 
previously, the index dk associated with the origin mode 4 being equal to 
0.08910 whereas the dk associated with mode 5 (fifth row from the top in 
the list of FIG. 16) is equal to 0.07991. Mode 4 is set aside and the 
analysis is continued with the remaining 68 modes. It shows that the 
shortest segment succeeding the segment (4, 5) is the segment (12, 17) 
whose length is 0.122360, the dk of the mode 12 being 0.1168 whereas the 
dk of the mode 17 located further up in the list is equal of 0.11011. Mode 
12 is thus eliminated and the process is continued, the result appearing 
in the form of the table in FIG. 16. 
When this table is scanned from the bottom up, it is noted for example that 
the mode 17 constitutes the end of two segments respectively (12, 17) and 
(11, 17) before appearing in a longer segment (17, 15) of which it 
constitutes the origin, owing to its dk which is higher than the dk of 
mode 15 (the latter being 0.08585). Mode 17 is then eliminated and does 
not appear again in the upper part of the table in FIG. 16 when it is 
scanned from the bottom up. 
Mode 15, which forms the end of the segment (17, 15), constitutes the 
origin of a longer segment (15, 14) whose end mode 14 has a dk of 0.05338 
which is smaller than all the dks of all the other local modes considered. 
This local mode 14 is found again as ends of two segments (18, 24) and 
(50, 14) at the top of the list in FIG. 16, these latter segments moreover 
having lengths greater than those of all the segments preceding them. 
A graphic representation, called a dendrogram, more formal than that of 
FIG. 10, can be plotted notably by means of a computer from the data of 
FIG. 16. 
In the diagram of dendrogram of FIG. 11 formed by the juxtaposition from 
top to bottom of the FIGS. 11A and 11B is placed on the abscissa, at the 
upper part, a scale of segment length from left to right. 
The 69 local modes selected at the origin appear with their corresponding 
designation on the ordinate in the left-hand column of the diagram. 
Opposite the designation of the mode 47, for example, at the top of FIG. 
11, there is a broken line 300 whose length, parallel to the axis of the 
abscissas 399, is equal to the distance between this mode 47 and a mode 59 
immediately under it which constitutes the end of a respective segment 
which has been indicated by an arrow 450 in the list of FIG. 16. A 
parallel broken line 302 with a length equal to the line 300 extends from 
mode 59. The ends of the lines 300 and 302 are joined parallel to the axis 
of the ordinates or the vertical axis 400 by a line 309. By convention, in 
the representation of a segment such as (47, 59), the mode 59 
characterizing the end of the segment is placed lower than that 
characterizing the origin (47) of this segment. 
Every segment present in FIG. 16 is represented by a broken line link on 
the dendrogram of FIG. 11 between the representative points of the origin 
and end modes of the segment on the line of the ordinates 400. Each of 
these links includes two lines or series or horizontal lines such as 300 
and 302 connected by one or more vertical lines such as 309. 
Level 47 does not constitute the end of any segment of FIG. 16. On the 
other hand, level 59 constitutes the origin of an end segment 50 (arrow 
452 in FIG. 16). Mode 50 also constitutes the end of a segment 49, 50 
(arrow 454, FIG. 16) which is relatively short (line 306, FIG. 11) and a 
segment 46, 50 (arrow 456, FIG. 16) whose length is greater and marked by 
the line 304 itself corresponding to the cumulative length of the lines 
306 and 308 of the dendrogram of FIG. 11. Mode 50 is still the end of a 
segment 56, 50 (arrow 457, FIG. 16, cumulative length of lines 306, 308 
and 316) and a segment 57, 50 (arrow 458, FIG. 16, length of lines 306, 
308, 316 and 318). The link between the modes 59 and 50 of the segment 59, 
50 includes the horizontal lines 302 and 310 whose cumulative length is 
equal to that of the lines 306, 308, 316 and 318 and represents the length 
of the segment, and a vertical line 313 joining the ends 312 and 314 of 
these two horizontal series. 
The dendrogram is constructed beginning with the mode 14 at the lower end 
of the diagram in FIG. 11 (arrow 460, FIG. 16). The density at the mode 14 
is maximum (minimum dk) and forms the end of a first segment (13, 14) 
which is the smallest segment connected to the mode 14. Mode 13 is placed 
over mode 14 on the axis 400. 
The table of FIG. 16 shows that mode 13 is itself not the end of any 
segment. 
The next shortest segment connected to mode 14 is the segment (15, 14) 
(arrow 462, FIG. 16). Its origin 15 is placed over mode 12 in the 
dendrogram. 
Segment 15 is the end of a segment (17, 15) (arrow 463, FIG. 16) which 
makes it possible to locate point 17 over segment 15. 
This segment 17 is the end of a segment (12, 17) of shorter length (arrow 
464, FIG. 16) which makes it possible to locate mode 12 over mode 17 (FIG. 
11). Moving up the list in FIG. 16, it is noted (arrow 466) that mode 17 
is also the end of a segment (11, 17) which makes it possible to locate 
mode 11 on the dendrogram of FIG. 11. 
FIGS. 16 then shows that mode 11 is the end of a segment (21, 11) (arrow 
468) which makes it possible to locate mode 21 in the dendrogram of FIG. 
11, and so on. 
Such a dendrogram can be plotted under the control of a computer which 
performs the preceding operations. The local modes are plotted on the axis 
400 in the order just described and connected as explained above by means 
of forks with two branches, such as 330 and 332 for modes 14 and 13. The 
lengths of these branches each represent the length of the segment 12, 14. 
Each fork is connected toward the center of its summit 334 to a following 
fork making it possible to materialize the links between each pair of 
modes defining a segment and the length of this segment. 
It is possible, if a single minimum length is chosen for the segments thus 
defined in the set of local modes, to select from among them a subassembly 
of terminal modes. In the present example, a minimum length threshold 
equal to 0.751 has been chosen whose position is materialized by a broken 
line 401 with long dashes in FIG. 16. This threshold defines 18 modes 
which characterize segments whose lengths are greater than it and which 
constitute so-called terminal modes in the borehole interval thus 
analyzed. These 18 terminal modes are grouped in the table of FIG. 17 
where it has been chosen to classify them in the order of the increasing 
value of their coordinates of the first main component as they appear in 
column 360, the coordinates of the other two main components appearing 
respectively in columns 361 and 364. 
It is chosen to consider each of the values of column 360 as characteristic 
of a class attached to each terminal mode thus detected. These modes are 
identified by their number in the set of local modes (column 370), in the 
set of treated levels (column 372) and by a number, called the facies or 
electro-facies number, in the extreme left-hand column 376, from 1 to 18, 
in the increasing order of the indices of column 360. Also indicated, in 
column 378, are the decay values corresponding to each terminal mode. 
The threshold selection operation just described is symbolized in the flow 
chart of FIG. 8 by the block 214 (THRESHOLD) which receives an input 
instruction NFAC which can be, as just explained, a predetermined distance 
threshold or which can be a predetermined number of terminal modes. 
This threshold selection operation can take on other forms and in 
particular can be carried out by a more diversified procedure which may 
lead to a finer selection of the terminal modes to be adopted. For 
example, one may consider, after having carried out a first selection of 
terminal modes, the adoption of a second threshold lower than the first, 
which is applied selectively only to one or several segments attached to 
these terminal modes. 
Referring to the dendrogram of FIG. 11, the line 401 parallel to the axis 
of the modes 400 and intersecting the axis of the abscissas at the point 
1.751 defines the threshold mentioned with reference to FIG. 16. In the 
dendrogram, all the designation of local modes selected as terminals from 
this threshold value have been circled. 
Each of these modes is connected by a double line at the point of 
intersection of the threshold line 401 with the corresponding distance 
fork. Each of the intersection points such as 404 between the threshold 
line 401 and the horizontal branches of the forks of the dendrogram is 
connected by such a double line to the local mode occupying the lowest 
position among all the local modes connected to this intersection point 
404. This observation reflects the fact that the segments have, by 
hypothesis, been oriented by placing their end modes (minimum dk) toward 
the bottom of the diagram. 
Referring to FIG. 8, the phase for the application of the threshold 401 in 
step 214 is followed by a phase for connecting each of the local modes not 
used to a corresponding terminal mode. Each phase in step 216 CUTLNK is 
illustrated by the observation of the dendrogram in FIG. 11. To each 
terminal mode corresponding to an intersection point 404 are attached the 
other local modes connected to this point 404. They occupy an immediately 
higher position in the dendrogram. Thus, to the hyperterminal mode 14 
(lower end of dendrogram) are attached the local modes 12, 15, 17, 12, 11, 
21 and 16. To the terminal mode 18 are attached the local modes 22 and 23, 
and so on. By this attachment, we assimilate to a terminal mode, for the 
classification within the same electro-facies, all the local modes whose 
distance from this terminal mode is smaller than the given threshold, i.e. 
which do not reflect a dissimilarity considered sufficient in relation to 
this terminal mode to be regarded as possible characteristics of a 
distinct particular facies. 
As the line 401 is approached to the axis 400 toward the left in the 
dendrogram, increasingly greater refinement is obtained in the 
determination of the terminal modes and of the associated facies. 
It is moreover understandable that it is possible, depending on external 
considerations based upon additional observations, to select terminal 
modes not from a single threshold but from several thresholds, for example 
as a function of certain decay index values or main component values, etc. 
It is moreover obvious that the selection thus made depends on weighting 
factors or normalization factors which may have been assigned to the logs 
used in the measurement space in order to transpose them into the space of 
the main components. As explained above, the attachment phase 216, FIG. 8, 
is followed by a phase 218 for classifying the terminal modes according to 
an index value defining each class attached to each terminal mode. 
In the table of FIG. 17 are shown, in column 363, the number of local modes 
attached to each terminal mode 1 to 18. 
In FIG. 15 is represented a diagram in which the neutron porosity and the 
density measured at different levels of a borehold interval have been 
plotted respectively on the abscissa and the ordinate. In this 
representation, each point of a two-dimensional scatter is represented by 
a figure between 0 and 9 which corresponds to an identification number of 
the terminal mode to which it has been attached by the operations 
described above. A double circle has been placed around the 10 modes 
determined in this example. A single circle has been placed around the 
points of the scatter which correspond to local modes selected on the 
basis of the density analysis previously described. These local modes, 
like the other points, are attached to the terminal modes 0 to 9. This 
latter attachment has been made on the basis of an analysis corresponding 
to the dendrogram of FIG. 11. The other points of the diagram have been 
attached to the terminal modes through their attachment to the circled 
local modes. 
Each class or electro-facies determined in the borehole interval 
corresponds to a litho-facies which is designated according to the values 
of the logs for the points which fall within this class. This designation 
is hence carried out from knowledge of the overall response to all the 
logs for each of the most current litho-facies. This is a priori knowledge 
which can be applied manually by a geological expert or automatically by 
means of a body of rules using so-called artificial intelligence 
techniques. These rules constitute applications of deductions similar to 
those applied by geological experts and are formulated in accordance with 
their experience. They each make it possible, on the basis of one or more 
initial observation, to draw conclusions on the presence of a particular 
litho-facies. These conclusions can be expressed in the form of 
probabilities. Whether manual or automatic, the designation of the 
litho-facies can make use of data other than logs. It can also make use of 
an iteration process to refine the first conclusions. 
The result of this designation is that each class designated by a number is 
made to correspond to a litho-facies; for example: class 1, quartzite; 
class 2, well-compacted limestone, etc. These conclusions can be fixed in 
graphic form as a function of depth by means of characteristic diagrams of 
each facies. The processing machine can be programmed to control a log 
plotting device according to the diagram corresponding to the lithological 
designation of each class identified for each group of consecutive levels 
attached to the same class. The stepped curve, or pilot curve, of FIG. 5 
may or may not, depending on the requirements of the end user, be produced 
on the recording. 
Such a recording of the results of the method appears in FIG. 12 for a 
borehole interval in which are found more or less clay sands containing 
petroleum and two thin coal seams. 
The facies appear in column 412 and are accompanied by a literal 
description printed automatically in an adjacent column 416. 
To the right of these indications concerning the litho-facies are shown 
(column 418) the results of a formation evaluation analysis giving in 
percent the total porosity PHI, the percentage of water in the pores 
(white zones 420), the residual percentage of petroleum in the invaded 
zone (black zone 422), the volume of matrix rock (mat, zone 424) the 
nature of which is indicated in 412 and 416, and the clay and silt 
volumes. 
To the left of column 412 appear the results of dipmeter measurements HDT 
with (column 426) the four resistivity curves coming from four pads (curve 
1 is repeated) and the transverse junction lines 427, or correlation 
lines, furnished by correlation processing of the GEODIP type as described 
in the French patent already cited. 
The dip angle and direction indications coming from dipmeter measurements 
are indicated by the arrows in diagram 428. Finally, the left-hand columns 
in the diagram give the different logs with the respective codes (solid 
lines: micro SFL; long dashes: induction; short dashes: deep lateral log; 
dots: surface lateral log). Column 430 shows the resistivity logs. Column 
432 shows the porosity logs: gamma (RHOB), sonic (DT) and neutron (NPHI). 
Column 434 shows the gamma ray (GR) and spontaneous potential (SP) logs. 
Using only the local modes after the data compression phases described 
earlier and their associated log value, it is possible advantageously to 
interpret the log data over a formation interval with excellent accuracy, 
while considerably reducing the amount of processing necessary for this 
purpose. Thus, in the previous example, instead of calculating the 
parameters of the formation derived from interpretation by means of 1200 
levels, the 69 local modes selected are used to plot a formation 
evaluation diagram with a shape similar to that of column 418 in FIG. 12. 
The method just described of course has many variants. In particular, it 
can integrate into the analysis additional measurements such as obtained 
by means of a dipmeter of the type described in U.S. Pat. No. 4,251,773. 
Such a dipmeter is equipped with four pads each provided with means for 
measuring the resistivity of the zones of the well wall with which they 
are brought into contact so as to obtain four series of measurements which 
are a function of the depth of the tool in the well. 
The measurements furnished by the dipmetering tools are much denser in 
depth than those used for other logs. The latter are in fact sampled about 
every 15 cm whereas the sampling of dipmeter measurements can be carried 
out every 0.25 or 0.5 cm. Consequently, to make the vertical resolutions 
of the dipmeter measurements compatible with those of other logs, the 
following procedure may be used. At each dipmeter sampling level, the 
resistivity measurements carried out by the four pads are combined to 
obtain a single value for this level, notably the average value of the 
four measurements. The resistivity log thus obtained is then transformed 
to change the sample density to one sample every 15 cm by a moving average 
technique. For this purpose, with each level H.sub.i for which samples of 
the other logs or standard logs of columns 430 to 434 of FIG. 12 have been 
obtained, is associated a resistivity value R.sub.i obtained by 
calculating the average of the samples of the resistivity log coming from 
the dipmeter over an interval of 90 cm framing this level H.sub.i. 
One then calculates a resistivity value of the dipmeter associated with the 
next level H.sub.i +15 in the sampling scale of the standard logs. One 
thus obtains another resistivity value R.sub.i +15 coming from the 
dipmeter. The procedure is reiterated for all the explored borehole 
interval levels H.sub.1 to H.sub.n. The new resistivity log obtained can 
then be correlated in depth with one of the resistivity logs coming from 
measurements other than those furnished by the dipmeter, for example the 
values furnished by conventional resistivity measurements or laterolog 
measurements, or from induction tools. This depth correlation operation 
can be carried out using the conventional techniques already mentioned 
above. It allows the correlation in depth of the measurements coming from 
the dipmeter in relation to other measurements. 
From this correlation, according to one embodiment, one can determine one 
or several synthetic logs sampled every 15 cm from respective 
characteristics taken from the measurements coming from the dipmeter. 
These characteristics can, for example, include the frequency of certain 
characteristics of measurements coming from each pad, for example the 
local peaks of these measurements, or the variance of the conductance 
measurement over a reference depth interval, or the average thickness of 
the strata shown by the dipmeter over this interval, or the number of 
correlation lines derived from the correlation processing of the curves 
obtained by the GEODIP method already mentioned above within this 
reference depth interval. 
The snythetic logs are obtained by a moving average technique in which, 
after in-depth correlation of the raw measurements, we determine for each 
sampling level of the other logs, the average value of the chosen 
characteristic (variance, correlation density, etc.) over a depth interval 
of 90 cm centered on the chosen level. 
The physical characteristics to which synthetic logs are sensitive depend 
on the characteristic chosen for constructing each one of them. These 
synthetic logs are advantageously used in conjunction with other logs in 
the analysis leading to the determination of the electro-facies mentioned 
earlier. 
The advantage of using dipmeter measurements in an electro-facies analysis 
is that these measurements are very sensitive to the structure of the 
formations encountered. It is thus possible to improve, by the use of 
these measurements, the information available on the transition levels 
between two different facies. 
It is possible to refine the determination of the transition between the 
electro-facies determined in accordance with the procedure just described, 
for example after having plotted the pilot curve of FIG. 5, by means of a 
parallel representation of the results of a GEODIP correlation between the 
curves. 
In FIG. 12 has been represented a network of four curves 460.sub.1 to 
460.sub.4 coming from such a correlation process. The comparison of the 
results of dipmeter measurements with the products of the correlation 
which lead to the establishment of a classification of the measurement 
levels and to a segmentation of the borehole into zones can be 
accomplished through an iterative procedure. Thanks to dipmeter 
information, it is possible to modify the cutoff criteria after the 
clustering operation (THRESHOLD operation of step 214, FIG. 8) in order to 
obtain adequate matching between the transitions between classes such as 
they appear for example in the stepped curve FAC (curve 100, FIG. 5) and 
the structural indications coming from the dipmeter. The points of the 
curves 460 which have been correlated after GEODIP processing are 
connected by transverse lines such as 427. A verification is made of the 
correspondence between these lines and the transition depths between the 
different transitions of the litho-facies log 412. When a discrepancy is 
noted between the position in depth of a series of levels correlated 
between each other in the GEODIP representation and a neighboring 
transition between two litho-facies, the position of the latter is 
modified so as to make it match with the indication furnished by the 
GEODIP curve. This makes it possible to apply to the search for 
electro-facies or their positioning the contents of the dipmeter 
measurements in terms of structural information. 
The results of the comparison between the curves derived from the dipmeter 
and the results of the classification obtained after the processing 
operations described above can thus be used in an iterative manner to 
repeat this analysis procedure and notably to modify the rules leading to 
the selection of the terminal modes in order to refine the choice of the 
number of electro-facies and the criteria used for defining them. 
Such an iterative process can also make it possible not only to specify the 
limits between classes corresponding to different electro-facies but also 
to proceed with a fine analysis of the litho-facies within a class 
previously determined during a first relatively rough analysis phase. 
In this respect, the termination of the transition levels between 
electro-facies in the second phase of the process during which are added 
the levels not used in the first analysis phase of the measurements can be 
influenced by such data. 
The procedures according to the invention also offer the significant 
advantage of being useful within the framework of a given well as well as 
that of an oil field comprising several wells. 
According to a particular embodiment of the present invention, instead of 
considering a scatter of points corresponding to only the measurements 
carried out in a given well, we consider a scatter of points corresponding 
to all the measurements in several wells. This scatter is analyzed 
according to the techniques mentioned above for the detection of clusters 
in the respective hyper-space and the determination of the characteristic 
logic modes of each of these clusters, the other points of the scatter 
being attached to these local modes. 
As in the case of a single well, it is possible, on the basis of the local 
modes thus determined, to carry out a formation evalulation interpretation 
according to the techniques already mentioned under the names of SARABAND, 
or GLOBAL. It is noted, as previously, that the results of the analysis 
carried out on a limited number of levels in each well, selected according 
to the method just indicated, are as good from the practical standpoint as 
the results coming from an application of these techniques to all the 
measured levels of the well. 
It is moreover possible to determine, on a field-wide scale, a series of 
terminal modes from which associated classes can be defined. Analysis of 
the correspondence of the levels of each well to those different classes 
make it possible to analyze the litho-facies of each well. 
According to a variant of that embodiment, in the case of a field in which 
a certain number of wells, called key wells, have already been carefully 
analyzed, in particular with the determination of their main components, 
their local modes and their terminal modes, it is possible to use these 
results to facilitate the analysis of the facies of the formations 
encountered by other wells in the same field. 
For this purpose, the logs made in a particular well or in several wells, 
other than the key wells, can be combined with the measurements obtained 
in the key wells to obtain a multidimensional scatter in which we consider 
the distances of the points corresponding to the wells to be analyzed in 
relation to the local modes and to the terminal modes of the key wells. 
These points are attached to the key local modes and terminal modes to 
which they are closest on the basis of the comparison of their Euclidean 
distance from these key points or key modes. This analysis makes it 
possible to locate, in the well or wells not analyzed, the position of the 
electro-facies or possibly of only part of the electro-facies encountered 
in the key wells. 
Finally, it is also possible to carry over the measurements from an unknown 
well into the space of the main components determined during the analysis 
of one or more key wells. 
We shall now describe, as yet another embodiment of the present invention, 
a method in which the data furnished by the logs are used to obtain as 
precise an identification as possible of the facies of the formations 
encountered, taking into account sources of information, other than the 
logs, which may be available to the analyst. 
FIG. 14 summarizes the analysis process. In a first step 502 we consider a 
data base 504 which characterizes a plurality (e.g. 90) of different 
litho-facies each for a respective volume in the hyper-space of the logs. 
This hyper-space can include eight basic dimensions constituted by the 
eight standard logs mentioned earlier by way of example. These values are 
advantageously completed by the measurements coming from the dipmeter 
tools, essentially synthetic logs of the type previously described. 
The volume associated with each electro-facies is made up of a set of 
points corresponding to possible measurement values for this 
electro-facies. The number of these measurements varies according to the 
litho-facies considered. The number of points of each volume can be 
typically between a half dozen and several dozen, and possibly up to 100. 
The log values characterizing each facies are usually obtained in a 
reference well with known condition of the borehole, temperature, 
pressure, etc. The analysis technique presently described is applied to 
borehole intervals which are previously selected on the basis of a direct 
observation of the log or from general knowledge of the field. Typically, 
an interval whose thickness can be between 50 and 200 m is considered. 
According to the particular hole conditions (block 506) prevailing in this 
interval (hole diameter, caving) and which can be furnished by a diameter 
measurement, as well as the specific conditions of the mud filling the 
borehole (mud resistivity in particular), the data base defining the 90 
litho-facies just described is shifted so as to make these measurements 
applicable to the particular hole conditions in the borehole interval 
considered. This adaptation is carried out in step 510 for each log 
according to known correction or shifting procedures and provided as an 
output in step 512. 
The measurements actually obtained in the borehole, inputted in Step 514, 
are then analyzed according to the output provided in step 512 in order to 
designate the facies encountered at each measurement level. This 
designation includes several phases. In particular, it is not always 
possible to obtain an unambiguous assignment of litho-facies for each 
level by a direct comparison of the values of each point of the scatter 
resulting from borehole measurements with the data base for defining the 
different electro-facies. There are overlaps between the definition 
volumes for each litho-facies (reference will be made hereafter to these 
volumes as electro-facies). Consequently, a point of the scatter measured 
can fall within a zone corresponding to several possible litho-facies. 
A relatively rough statistical analysis is thus first carried out step 516 
in which is determined, for each measurement point, its distance to four 
definition points of respective electro-facies which are the closest to 
it. A definition index is assigned for each of the electro-facies which 
are closest to the measurement point considered. We assign an index of the 
plausibility of this point belonging to each of the electro-facies in 
inverse proportion to the distances determined. We group the results of 
this statistical analysis at the level of the interval studied to produce 
a list of plausibility indices for the presence of each of the 90 
electro-facies in this interval. 
It is also possible to limit this preliminary study to the determination of 
the number of measurement points falling within each volume of a set of 15 
typical volumes determined in advance in the space of the logs. 
We thus obtain a rough lithological identification of "litho-data," 
provided as an output in step 518, which constitutes one of the input 
information items for the next phase. 
This next phase, step 524, applies "artificial intelligence" techniques 
allowing the automation of relatively complex deductive processes such as 
those used by experts in a given field of investigation, the field of 
geology in this case. The deductive process applied here makes use of two 
other sources of data at steps 520 and 522 respectively: (1) the 
morphology of the different logs obtained in the analyzed interval, namely 
plateau (wide, medium, or narrow), ramp (long, medium, short or very 
short) (examples shown in FIG. 13) and (2) "external" data, namely 
mineralogy, geological formation, stratigraphy, sedimentology, geography, 
paleontology (examples shown in FIG. 13). The latter include in particular 
the region data, notably the geographical location of the borehole which 
make it possible to deduce the different consequences on formation geology 
of the data furnished by an analysis of the borehole debris, the 
description of cores and general geological knowledge which may be 
available to the analyst in the geological zone in which the investigated 
interval is located. The regional rules for example take into account 
certain specific lithological characteristics of a given region. These are 
of a very general nature and correspond to information published in 
geological works of general interest. 
Artificial intelligence techniques are well known. They apply rules which 
relate initial data to one or more conclusions. These initial data are 
obtained from sources as described above. The conclusions are formulated 
in the form of deductions on the nature of the formations in the analyzed 
interval, presented in the form of possible characteristics each 
accompanied by index values representing the plausibility of the presence 
or absence of this characteristic. Plausibility indices can take on values 
between -1 and +1. Positive plausibilities correspond to the probability 
of the presence of the characteristic or of the litho-facies to which they 
are related. The negative values correspond to probabilities of absence, 
this probability increasing as the negative values increase. The 
plausibility interval (-0.1, +0.2) corresponds to an uncertainty zone. 
The types of conclusions furnished in the present example form three 
categories: 
the best depositional paleo-environment (coastal, lagoonal, etc.); 
the main geological type (biological, evaporitic, carbonated or plutonic); 
a list of the most plausible litho-facies in the interval considered, 
accompanied by a respective plausibility index (block 526). 
This part of the process to provide the needed information at step 520 can 
be applied by means of a dialog between the processing machine and the 
user. The machine is programmed to guide the user by a series of questions 
and to pursue the investigation in accordance with the replies provided by 
the user. Thus, the program asks the user questions on stratigraphy, 
sedimentology, mineralogy, and so on (examples shown in FIG. 13). It then 
asks questions on the structure or the morphology of the different curves, 
proposing the different possible answers. 
In the present illustrative example, there are 400 application rules. Three 
simple examples of these rules are given below, in connection with each 
source of information furnished to the program. Many variations on these 
rules are possible and the process can be evolutionary in nature. 
Rule 054 
"if, according to the litho-data, the percentage of measurement points 
falling within the typical volume No. 13 (corresponding approximately to 
measurement values encountered in gypsum) is greater than 5%, 
then the plausibility that the depositional paleo-environment of the zone 
will be lagoonal is 0.7, pelagic -1, lacustrine 0.2, arid -0.6, coastal 
0.3 or reefal 0.2." 
Rule 295 
"if (1) the stratigraphy period is cretaceous and (-2) the geological 
province is the fold of the Zagros range in Iran; then: 
(a) this indicates that the depositional paleo-environment of the zone is 
fluviatile (-0.6), lacustrine (-0.6), deltaic (-0.6), glacial (-0.8), and 
so forth 
(b) this indicates that the main lithological type of the zone is detritic 
(-0.3), biological (0.3), evaporitic (0.3) or plutonic (-0.5) 
(c) this is a minor indication that the geological formation of the zone is 
not entirely compacted, and 
(d) there is a minor indication (0.3) of the presence of petroleum in the 
zone." 
Rule 008 
"(1) If there is a plateau in the curve of the gamma radiation of the zone, 
and 
(2) the radioactivity level of the zone is less than 40 API, then thre is a 
strong indication (0.8) that the zone is a clean zone, i.e. with a low 
clay content." 
FIG. 13 represents very schematically the deductive process applied by 
these rules which link the three major sources of data to the three 
categories of conclusions mentioned above, passing through intermediate 
conclusions on whether or not the formation is compacted, its cleanliness 
(absence of clay) and the deposit energy. 
With reference again to FIG. 14, the list of the most plausible 
litho-facies the output of which is provided in step 526 makes it possible 
to proceed with the assignment of each of the points of the interval to 
the most plausible litho-facies, all the other litho-facies being 
eliminated. 
For each of the measurement points, its distance is calculated to the five 
closest points of the plausible litho-facies in step 530. The distances 
thus obtained are weighted by a factor derived from the plausibility of 
each of these facies in order to assign to the point considered the 
litho-facies which corresponds to the minimum weighted distance. 
This process thus furnishes directly a segmentation of the borehole 
interval into successive zones each corresponding to a respective facies 
which is designated directly and outputted at step 532. 
Of course, in the preceding example, it is possible to refine each zone, 
notably by making use of data coming from dipmeter measurements. 
The addition of external data and the morphology of the curves thus makes 
it possible to obtain an indication of certain genetic characteristics of 
the facies, i.e. other than petrographic or petrophysical. It also clears 
up any ambiguities in the determination of the litho-facies on the basis 
of log data alone. 
An example is given below of conclusions obtained after the process just 
described for a formation interval called "Zone 1". All the negative 
plausibility values and those lower than a certain threshold have been 
eliminated. 
(a) The best depositional paleo-environment for "Zone 1" is: 
coastal 0.978 
lagoonal 0.837 
neritic 0.549 
(b) the main lithological type for the zone is: 
biological 0.988 
evaporitic 0.571. 
(c) The most plausible litho-facies for "Zone 1" are: 
well-cemented dolomitic limestone 0.967 
well-cemented dolomite 0.959 
moderately cemented dolomitic limestone 0.956 
moderately cemented dolomite 0.956 
well-cemented limestone 0.941 
moderately cemented limestone 0.914 
anhydrous dolomite 0.910 
sandy limestone 0.958 
clayey limestone 0.856 
solid shale 0.816 
siliceous limestone 0.807 
silex 0.791 
gypsum 0.766 
weakly cemented dolomitic limestone 0.766 
moderately compacted or cemented sandstone 0.756 
siderite (FeCO.sub.3) 0.743 
salt 0.730 
cellular dolomite 0.711 
oil shale 0.848. 
In describing the invention, reference has been made to its preferred 
embodiment. However, those skilled in the art and familiar with the 
disclosure of the invention may recognize additions, deletions, 
substitutions or other modifications which would fall within the purview 
of the invention as defined in the appended claims.