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BACKGROUND OF THE DISCLOSURE
In general terms, the optimum approach for interpreting well log data
obtained by dipmeter is visual inspection and correlation by a skilled and
experienced operator. The dipmeter analyst utilizes an optical device to
shift and contrast two or more curves obtained from a dipmeter. Attempts
to accomplish this by something other than human observation have been
made in the past. Another approach is correlation of dipmeter logs by
fixed interval correlation methods. The various and sundry mechanized
correlation methods impose on the data the requirements for data free of
noise, or what otherwise is termed as high quality data. The quality of
data sometimes will vary in a fashion that poor quality cannot be
overcome. For instance, the quality of data is dependent on downhole
conditions which vary with a multiple of factors. The conditions impact
the quality of curves presented for dipmeter interpretation. When such
difficulties arise, as a practical matter, the only approach then left is
optical correlation. Again, optical correlation may be the most accurate
and desirable approach but it is also a good deal more expensive and
tedious in that it requires an experienced human operator.
The present approach is able t o provide interpretation in the stead of
fixed interval correlation techniques. In general terms, the approach of
the present disclosure utilizes what are called segmentation trees with
hierarchial multilevel optimization. These terms will be defined in
greater sweep below.
The use of the generally described concepts implemented in the present
apparatus and procedure enhance showings of dip angle and direction.
Because there is a hierarchy of data, dynamic programming techniques are
more readily applied.
DESCRIPTION OF THE DRAWINGS
So that the manner in which the above recited features, advantages and
objects of the present invention are attained and can be understood in
detail, more particular description of the invention, briefly summarized
above, may be had by reference to the embodiments thereof which are
illustrated in the appended drawings.
It is to be noted, however, that the appended drawings illustrate only
typical embodiments of this invention and are therefore not to be
considered limiting of its scope, for the invention may admit to other
equally effective embodiments.
FIG. 1 is a profile curve from a sensor on a dipmeter where local minima
are connected by segments of straight lines to initiate segmentation;
FIG. 2 shows the same curve of FIG. 1 with a double line connection to
minima of the segments of FIG. 1;
FIG. 3 shows further curve segmentation utilizing minima in the double line
segments;
FIG. 4 shows a tree structure representing the segments in the curve shown
in FIG. 3;
FIG. 5 shows an approach for breaking events into subevents for ease of
correlation involving relative contrasting areas;
FIG. 6 shows a profile curve and a segmentation tree arrangement wherein
adjacent subtrees are united in a common segmentation tree;
FIG. 7 shows a simplified regrouping of subevents in a segmentation tree to
provide additional events for analysis;
FIG. 8 shows an application of segmentation to a profile curve to assure
event detection in the curve;
FIGS. 9, 10 and 11 shown the same profile curve which is segmented in FIG.
8, and which has selected events additionally marked in FIG. 9, event
simplification in FIG. 10, and event selection in FIG. 11;
FIG. 12 is an example of dipmeter results utilizing the algorithm of the
present disclosure;
FIG. 13 shows another result from the use of the present invention with
different strata;
FIG. 14 is a set of data processed by the present algorithm in contrast
with fixed interval correlation;
FIG. 15 is an expanded vertical scale of a portion of the data shown in
FIG. 14 for increased emphasis;
FIG. 16 is dip determination for thin beds; and
FIG. 17 is a flow chart for implementation of the algorithm of the present
disclosure.
DETAILED DESCRIPTION OF THE PRESENT DISCLOSURE
A dipmeter is a device which utilizes a measurement pad having a sensor
thereon. Typically, four pads are arranged around the dipmeter located at
90.degree. spacing. They make measurements of resistivity. The resistivity
measurements are taken along a borehole. When a formation is encountered,
it is observed at one of the pads before it is observed at the other pads
assuming that the formation has dip. This is ordinarily the fact. The
extent of dip can be determined by interpretation of the dipmeter data
obtained from the multiple sensors. The preferred arrangement of four
sensors provides four data traces for a typical borehole. They are
analyzed by hand in one approach, the four data being plotted as curves on
a common strip chart. The strip chart normally records the data as a
function of depth so that one axis of the paper is depth in the well
borehole. Optical analysis involves locating a common event in one curve
and then in another curve. This is optically assisted by utilizing
instruments which superimpose one curve over the other and which also
permit lateral movement so that they can be shifted.
A fixed interval correlation has been described by Kemp in the 1980 paper
entitled "An Algorithm for Automatic Dip Computation" Computers and
Geosciences, Volume 6, Page 193-209. Various and sundry fixed interval
correlation techniques are discussed in other references, Schoonover,
Larry L., and Holt, O. R.: Computer Methods of Dip Log Correlation, paper
SPE 3566 presented at the 1973 SPE Technical Conference and Exhibition,
and Moran, J. H., Coufleau, M. A., Miller, G. K. and Timmons, J. P.,
"Automatic Computation of Dipmeter Logs Digitally Recorded on Magnetic
Tapes", J. Pet. Tech. (1961), Vol. 14, No. 7, 771-82. In the various
correlation strategies, event selection from a given profile curve is
somewhat arbitrary and in many instances the event selection criteria
differs from that which would prevail with an optical correlation
utilizing an experienced analyst. Clearly, optical correlation is the
better because it is not confined by arbitrary limitations such as
specific lengths of curve, limiting analysis to predefined peaks, troughs
or plateaus, etc.
When one looks at this problem in a broader light, it can be stated in
general terms as breaking a curve into meaningful pieces. It has been
found that the major impediment to analysis is that the curve can be
organized into events of different relative sizes, and these sizes are set
in advance. Accordingly, various schemes based on multiple scales had to
be repeated for each selected scale. This approach, however, creates
computational burdens.
The scheme of curve analysis suggested below is data-driven, rather than
parameter-driven. This approach allows it to find simultaneously all
meaningful events of all sizes. It is based on a simple geometrical
procedure and on a number of rules extracted from the process of optical
dipmeter correlation.
Consider a curve 20, such as the one on FIG. 1, with all its local minima
marked and connected by segments 21 of straight line. This connecting
contour can be regarded as a first approximation to the initial curve.
Similarly, one can find the local minima of the connecting line and
connect them with the second-order connecting line 22, as in FIG. 2. The
connecting line in FIG. 2, drawn as a double line, can be regarded as a
second-order approximation to the initial curve. This process can be
continued with third-order line 23, fourth-order line 24 and so on, until
no two minima can be found on the last approximating line, as in FIG. 3.
Notice that most apparently meaningful events are present in the resulting
segmentation on FIG. 3 and are delineated by a single, double, or triple
lines. After all line segments in FIG. 3 are numbered, their relationship
can be represented by a tree diagram 25, such as the one shown in FIG. 4.
The segmentation described above and its associated tree structure are
called a segmentation tree. It is important to realize that the initial
curve is not substituted by approximation lines, rather, the lines serve
only as a means of segmenting the curve, or breaking it into pieces; this
explains the term segmentation tree.
As would seem clear, not every curve segment in a segmentation tree is
important and can be used for correlation. A selection criterion can be
formulated using the following simple consideration. Consider FIG. 5,
where curves 26, 27 and 28 represent variations of one basic shape. The
curve 26 is almost always considered as one whole event, curve 27 is
almost always separated into two constituent events, while curve 28 is a
transition case.
To distinguish between these cases in the algorithm, an area criterion is
used. Let S.sub.sons denote the total area of the sons, and S.sub.father
the area of the father, as shown in FIG. 5. S.sub.sons is determined as
the sum of the areas under all son segments. In the curve 26, S.sub.sons
is less than S.sub.father ; in the curve 27, S.sub.sons is greater than
S.sub.father ; and in the curve 28 the areas are comparable.
Following the discussion above, the area comparison criterion for the
selection of meaningful events can be formulated as follows: any event,
for which S.sub.father is greater than S.sub.sons is correlated as a
whole; any event for which S.sub.father is less than or equal to
S.sub.sons will not be correlated as a whole, although its sons may be
considered for correlation. It should also be noted that when an event is
correlated as a whole, its sons may also be used for correlation.
Segmentation of a curve using segmentation trees and the area comparison
criterion described above allows the algorithm to select the same events
that would be considered as good candidates for correlation in optical
processing. These ideas are general, however, in the sense that they
provide the basis for event detection and will give satisfactory results
in most, but not all cases. A number of additional rules, derived from the
observation of optical processing, are used to improve the event
detection. These rules are described below. Before proceeding with them,
however, some consideration will be offered for the explanation and
justification of the event detection scheme suggested above.
DISCUSSION OF THE EVENT DETECTION ALGORITHM
As one can notice, the basic curve elements used for correlation and for
constructing more complex events are pieces of a profile curve from one
local minimum to another, where the direction of curvature is upwards.
First, upward curvature is a preferred direction in optical correlation.
Second, it corresponds to determining dip from resistive beds of those
whose resistivity is higher than the resistivity of the surrounding beds.
When the dipmeter is operated in saline, conductive muds, the resistivity
is measured more precisely in resistive beds. In addition, the measurement
taken against resistive beds is less impacted by the conductivity of the
mud. Both considerations above lead to greater curve repeatability in
resistive beds, making them more preferable for dip determination.
For some special cases of logging, however, this situation is reversed, and
correlation should be done primarily on conductive beds. Since the dip
results depend to some extent upon the selection of resistive or
conductive beds for correlation, the option for their selection has been
implemented in the program. It is called the oil-based option, and it is
primarily used for dipmeter logs recorded in oil-based muds, although it
can be applied to any log. Technically it means event selection based on
local curve maxima, rather than minima, and it is implemented by inverting
the base curve values and searching it again for local minima.
It must also be noted that there is a certain quality of a resistivity
measurement which makes the suggested scheme particularly effective. It
has been noticed that, as a sensor goes out of a resistive bed, the
measurement usually exhibits a small overshoot. Due to this a resistive
bed becomes delineated by two local minima on the upper and lower side,
thus providing the minima needed for curve segmentation.
ADDITIONAL STEPS FOR EVENT DETECTION
There are a number of additional steps, which make the event detection
scheme, based on segmentation trees and area comparison, more precise and
adapt it particularly to the task of dipmeter correlation. Some of these
are technical in nature while some are practical, and have been formulated
by analyzing the process of optical correlation. Following are certain
technical rules.
TREE STRUCTURE
The tree describing the relationships between events on the curve is not a
single tree with one root, but rather a collection of trees because, at
the end of the curve interval, some events of the lower level do not group
to form an event of a higher level. An exemplary structure is illustrated
in FIG. 6. This figure also shows how the subtrees 30, 31 and 32 are
joined into one connected tree from the curve 34.
TREE GROUPING
Some events that appear as a unity (in optical analysis) do not come out as
a unity in the segmentation tree; instead, they are either broken up or
are united with other events. This situation is illustrated in FIG. 7. The
curve 36 develops the tree 37 at the right. From this example, an
operation called grouping is introduced. This operation checks all
possible combinations of events belonging to one father and tests them
using the area comparison criterion described above. Any group that passes
the test (i.e. has the area of the father event greater than the sum of
the areas of the son events) is marked as an event, and this event is
added to the segmentation tree 38, as illustrated in FIG. 7.
TREE MARKING
This step refers to means marking those events in the segmentation tree
that are considered as good candidates for correlation. It is accomplished
using the area comparison criterion described above. All events of level 2
(see FIG. 2) and higher are tested against this criterion and are marked
for correlation if they pass the test. All events of level 1 are marked
for correlation unless they are marked down by any other rule that
prohibits using them. Examples of such rules are given below. Depending
upon the curve character, the number of events marked for correlation
constitutes from 50% to 10% of the total number of events in the
segmentation tree.
TREE REDUCTION
Since not all events are selected for correlation, the initial tree
structure is changed after the tree is marked. When all unnecessary events
are deleted, and all new connections between them are recorded, the new,
reduced tree contains only events that make good candidates for
correlation. The initial segmentation structure is, therefore, called a
segmentation tree and the final structure, with many unnecessary events
eliminated, is called an event tree. Using the event tree instead of the
segmentation tree is also helpful for saving computer time and memory.
The steps described above for making the segmentation tree complete, for
grouping, for marking, and for reducing the tree, together with a few less
significant steps given below, serve to adjust the initial segmentation
algorithm and to make event selection closer to the one done in optical
analysis. These rules relate to the internal work of the algorithm.
There are also, as has already been mentioned, practical steps, taken
directly from observation of optical correlation. Examples of these rules
are listed below
EVENT SIZE
The width of an event considered for correlation should be no less than 0.2
feet (0.6 cm) and no more than 5 feet (1.6 m). The lower limit is
introduced because, with diminishing size of events, the possibility of
miscorrelation increases. The upper limit comes from experience in log
analyzing and knowledge of the area logged. It is also explained by the
goal of looking for precise, and not averaged, dip; thick beds usually
consist of several sub-beds, each with its own dip. When using beds that
are too thick, one may loose information.
ALLOWING NO FURTHER DIVISION OF AN EVENT
When an event is small and consists of a number of even smaller events,
insignificant when compared to the main event, it should not be subdivided
further, and its constituent events should not be correlated. This rule
prevents the algorithm from correlating insignificant curve variations. It
is expressed using the language of segmentation trees, by a number of
subrules, for example: if the event level is 2, its own area S.sub.father
is 2 times greater than the area of its sons S.sub.sons, and its width is
less than 1 foot (0.3 m), then the event is not further subdivided.
REUNITING EVENTS
If an event was not selected for correlation, for example, because it did
not pass the area comparison test, but none of its constituent events at
any level has been selected because of a different step, then the event in
question is nevertheless marked for correlation. This rule, therefore,
overrides the work of some of the rules above.
EXAMPLES AND IMPLEMENTATION OF CURVE SEGMENTATION
An example of the use of the present algorithm is shown in FIGS. 8-11
inclusive. The curve 40 is illustrated to show first, second and third
levels of curve segmentation. By contrast, the same curve 40 is now shown
in FIG. 9 where additional segmentation is made by the additions at 41 and
42. In other words, additional markings have been made to show added
events. This is more apparent from the contrast of FIG. 8 with FIG. 9.
As stated above, not every marked event is useful. The events that are
important are illustrated in FIG. 10 where the curve 40 has been
simplified. That is, the segmentation tree has in large measure been
simplified to reduce the marking and thereby obtain only those events
which are more useful for correlation. Going now to the companion curve at
FIG. 11, again, the curve 40 is reproduced. A less important change has
occurred in the region 45 in FIG. 11 and is a simplification illustrated
in the transition from FIG. 10 to FIG. 11 can not be seen in the, figures,
but is reflected in the data structure stored in the computer.
EVENT CORRELATION
Event correlation is the next step of the algorithm. In this stage events
marked on one curve, which is from now on called a base curve, are
correlated to the other curves. The correlation is implemented in a
hierarchical multilevel optimization process; this allows the algorithm to
take into account as many interdependencies between events and
corresponding correlations as can be identified. Event correlation can be
broken into four steps.
STEP #1
The first step is to determine all correlation choices for each event. This
is done by computing a correlogram (a plot of correlation coefficients,
measuring curve similarity, versus all possible displacements) and by
selecting displacements corresponding to all local maxima of this
correlogram, where the value of the correlation coefficient exceeds a
certain threshold. These possible displacements from curve pairs 1-2, 1-3,
and 1-4 are then combined, and for all combinations planarity is checked.
The value of planarity cutoff is determined by known statistical and area
geological considerations. All found correlations between all four pads
are then recorded.
It may be noted here that all correlations found for an event refer to this
event. It is therefore logical to store this information as part of this
event's record. All event records in the event tree, when copied from a
segmentation tree, are supplied for storing correlations. Typical computer
protocol involves use of a scratch pad memory.
STEP 190 2
The second step in event correlation is called family optimization. To
explain this step, consider an event in the event tree which contains
subevents, called sons Obviously, any correlation choice of the son event
should agree with the correlation choice of the father. The agreement can
be formulated as a requirement that the correlation of the sons should not
conflict with the correlation of the father. Even then, for each
correlation choice of the father there may be a few possible correlation
choices for the sons, all agreeing with that of the father.
Therefore, in this step for each correlation choice of the father, the best
combination of sons' correlations is determined. This is done by solving
the following optimization relationship of equation (1):
##EQU1##
where
##EQU2##
is displacement between curve 1 and i in the correlation choices s;
##EQU3##
is the correlation coefficient for correlation choice p;
M is the number of correlation choices of sons (no more than one choice per
son) selected for the given correlation of the father;
N is the number of correlation choices that are left out;
k is a proportionality coefficient discussed later;
indices s=1, 2, . . . , M indicate selected correlations;
indices p=1, 2, . . . , N indicate correlations that are not selected.
Equation (1) selects the most consistent combination of sons' correlations
for a given correlation of the father. Naturally, no correlations are
allowed to cross. When a certain son correlation is inconsistent or is not
consistent enough with the other sons' choices, it is skipped. Differences
between displacements are used as a measure of consistency. Correlation
coefficients are used as a measure of importance for each correlation
choice.
The solution to the problem in equation (1) is obtained using dynamic
programming. A general problem of this kind and its particular
applications are treated in detail by Kerzner, "Image Processing in Well
Log Analysis", IHRDC, Boston (1986), where the algorithms for their
solution are also given.
When the solution to equation (1) is found, correlation coefficients of
selected son correlations are added to the correlation coefficient of the
father correlation, thereby increasing its importance. This step imitates
giving more importance to those matches that repeat in detail, and not
only in general shape.
The proportionality coefficient k in equation (1) influences the scatter of
resulting correlations. When the value of the coefficient is increased,
the scatter is increased; when the value is diminished, the correlation
progression becomes more gradual. However, the correlation progression can
not be made much more gradual or more scattered than the data actually
indicate. The solution is not very sensitive to the changes in k, and the
increase or decrease of k by a factor of 2 almost does not change the
result. The correct value of k is established through experimentation or
is known for a given geological region.
The results computed in Step 2 are recorded in the scratch pad of each
event, which is prepared in Step 1.
STEP #3
Step 3 in the correlation process is called optimization of correlations
for senior events. In this step, the correlations for all senior events in
the event tree are found. Senior events are defined as all events that do
not have a father. Some of these events are indeed large events with a
number of descendants, while some are merely low level events at the end
of the depth interval; the majority of senior events, however, do have
families, and their families have been optimized in the previous step,
Step 2.
Since the families were optimized in Step 2, conditional correlations have
been computed; for each correlation choice of a senior event, the
algorithm has decided which combination of correlation choices of sons is
in the best agreement with the correlation of the senior event, and the
coefficients of importance have been modified accordingly. Using this
information, it is now possible to find the best correlations for all
senior events. The model (1) used for family optimization is also used
here. This model, again, allows selection of the best set of correlations
by considering all combinations of them and by finding the most
consistent. Solution to this model is described above.
STEP #4
This step involves decoding correlations for all remaining events. Once the
correlations for senior events are determined in Step 3, they induce
correlation selection at all lower levels, since conditional correlations
have already been determined in Step 2. Step 4 thus finishes the
determination of displacements. The dip angle and directions are computed
using the known formulas of analytical geometry.
One note should be added here. As it can be seen from the description of
the algorithm, the dips are determined for events of all sizes and all
levels of enclosure (levels in the event tree) simultaneously. In fact,
the information from all levels is used to help the algorithm decide the
correct correlation selection. Geologically, this can be regarded as a
simultaneous computing of structural and stratigraphic dip information at
all possible levels. Graphically all this information can be presented in
one plot, as described below.
EXAMPLES
The example in FIG. 12 shows the presentation of dipmeter results computed
using the algorithm. Track 1 contains the two calipers and the drift angle
and direction, which is indicated in the same manner as the dip angle and
direction. As in the standard dip arrows plot, dip angles and directions
are shown in the track 2 using circles with arrows. The position of the
circle in the horizontal extent of the track indicates the dip angle, and
the direction of the arrow indicates the down dip direction.
Important features of this presentation are the profile curves with events
and correlations indicated on them and the corresponding dip arrows of
varying sizes. Represented in the track 3, events are indicated with thin
lines, the midpoint of an event being taken as the representative point
for this event. From this point, connected lines with arrows are drawn to
the other curves. These lines show the direction of matching. Since the
events are meaningful to the eye, the visual check of the correctness of
the basic correlations can be performed even by a person unfamiliar with
details of dipmeter processing.
Many events are enclosed within each other. This corresponds to more gross
bedding and to finer interbedding within it. Accordingly, dips from
thicker beds, or poly anomalies, can be interpreted as structural, while
dips coming from thinner beds within them as a stratigraphic. In the plot
these dips can be distinguished by the size of the dip arrows. Dips from
thickest beds are indicated by dip arrows of standard size. Dips from beds
of the second level of enclosure are shown by the arrows that are half the
size; dips from the next level of enclosure are again half that size, and
so on. Because of the scale limitations of the plotter, the smallest size
of the dip arrow is limited to be eight times smaller than the standard
size. It should be noted that the dip corresponding to any given event can
be found at the depth of the midpoint of this event; this depth is
indicated by the correlation line starting from this midpoint.
The example in FIG. 13 shows an interval of a computed dipmeter log, where,
due to a number of specific curve characteristics, one can see a number of
enclosed beds of different levels with corresponding variations in dip
arrow sizes. One may notice that the direction of dipping in the
interbedding may agree or disagree with the dip direction in the enclosing
bed. From analysis of displayed correlation lines, differing conclusions
may be drawn for specific cases. The agreement may indicate both the
stable character of the deposition and the precise character of the
recording; logging speed was constant, all sensors functioned correctly.
The disagreement also has to be interpreted. If unrelated to tool
recording problems, it shows the actual direction of interbedding inside
larger structures, thus providing important stratigraphic information.
The example in FIG. 14 is a comparison between the results of the fixed
interval correlation method and the new algorithm. One can see that the
dip progression resulting from the new algorithm is more precisely
delineated. All repetitive correlations resulting from overlapping in the
fixed interval correlation method are eliminated.
The differences between the two plots in FIG. 14 can be analyzed using the
presentation of the new results showing profile curves and correlations
between them. Consider for example, the interval from XX70 to XX80. The
fixed interval correlation results show a group of very low angle dips
which is not present in the results of the new algorithm. The check is
accomplished using the expanded plot in FIG. 15. The event which produces
miscorrelations in the fixed interval correlation method is marked in this
plot with a thick line 50. This correlation however, is caused by the
similarity in general shape only, and analysis shows that it is incorrect.
It is repeated a few times because of overlapping correlations intervals.
Checks similar to the above confirm that all discrepancies in the two plots
in FIG. 14 are resolved in favor of the new method.
FIG. 16 contains an example of dipmeter computation where sandbars have
been suspected. Computations have proved this suspicion to be true. An
additional interesting feature may be noticed in this example. High
variation in the profile curves indicates significant interbedding. This
interbedding may be regarded as producing stratigraphic information even
though few dip arrows of smaller sizes are indicated. This example
illustrates the capabilities of this new method in a case of thin beds.
DESCRIPTION OF ALGORITHM
In general terms, all of the foregoing is accomplished by an algorithm
having the flow chart illustrated in FIG. 17. Beginning with the initial
input of data, the algorithm is divided into two portions, the first being
directed to breaking the respective curve up into segments. The first
several steps relate to the segmentation tree while the last several steps
relate to correlation.
The step 60 converts the curve into a segmentation tree as exemplified
hereinabove at FIG. 1 and following. The curve is thus defined by a number
of segments at multiple levels, three levels being exemplified in this
example. The various curve minima are thus found and connected with a
first line and then minima in that first line are located and connected
with a double line, etc. until the several levels of minima are defined.
The next step is identified at 62 and this involves defining certain groups
in the segmentation tree. This regrouping is exemplified at FIG. 5. This
is accomplished by considering adjacent or connecting groups including
father and son groupings. This is also accomplished by performing area
comparisons as exemplified at FIG. 7.
The third step 64 in this sequence is to mark events that are possible
candidates for correlation. To do so implies that certain events will be
discarded. An example is shown in FIG. 10 above. Marking generally is done
by comparing areas. Typically, the area of the node is measured along with
the area of all its sons. This summation is a preliminary. If the area of
the node by itself is greater than the area of its sons, the event is
considered as a whole. Possibly it will be reevaluated later when specific
sons are evaluated.
The next step 66 in the algorithm is to reduce or eliminate nodes of the
segmentation tree that are not needed for correlation. After this pass,
the segmentation tree is then called an event tree. An example of such
reduction is shown in FIG. 7 of the drawings. This then prepares the data
representing the event tree for correlation described in the sequence of
four steps 70, 72, 74 and 76 and these steps are illustrated in FIG. 17.
After correlation has occurred, the actual dip is computed in step 80 and
the data is then presented on a suitable graphic basis.
The results of the implementation of the new dipmeter correlation
techniques prove the validity of an approach based on event detection and
correlation. The formal techniques for implementing these steps are based
on the use of segmentation trees and on hierarchial multilevel
optimization. The multilevel event structure used in the algorithm
corresponds to multilevel stratification taking place during deposition.
The use of this correspondence allows the correlation algorithm to produce
structural and stratigraphic dip information in one pass and to present it
in one plot, supported with a display of events and correlations.
While the foregoing is directed to the preferred embodiment, the scope
thereof is determined by the claims which follow.
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