Dipmeter Surveys (Computation)

Computation

Methods and Parameter Selection

Computation Methods

One method used to obtain dip information from the raw data involves correlating intervals of the dip curves. To a mathematician, a correlation coefficient is a measure of agreement between any two curves. Numerically, coefficients may run from zero (representing two completely dissimilar curves) to one (representing two identical curves).

The computer calculates the similarity between a section of one curve and an equal section of a second curve. The length of the interval on the first curve is the correlation length or interval. The computer then moves the first curve by some small, preset increment and recomputes the coefficient. This process is repeated many times.

When plotted with respect to depth, the resultant series of coefficients forms a function called the correlogram. This correlogram shows a peak value where the curves have the best fit with each other ( Figure 1 ). The position of this peak with respect to the center of the interval chosen on the first curve is the shift, or displacement, between curves.

 

The process is repeated for all curve pair combinations at that depth; the result is the relative position of correlated points around the borehole, which (when combined with the other measurements such as tool orientation, drift, and caliper data) are used to calculate the dip answer for that depth. A new interval is then chosen on the first curve at a distance equal to the step distance from the previous round of correlations just described, and the process is repeated to produce another dip answer displaced in depth from the previous one by an amount equal to the step distance. This step distance is normally chosen to be some fraction (usually 25 to 50%) of the correlation interval.

During the curve-to-curve comparison it is essential to prescribe for the computer the distance up and down the second curve to which the first curve is to be compared. This distance is fixed by the choice of the input parameter called search angle.

Search angle is chosen according to the dip environment. For low structural dip areas, a 45° search is common, as most stratigraphic dips fall within that range.

In tectonically disturbed areas, higher search angles may be required. The choice in such circumstances must be guided by both local knowledge and close inspection of the dip curves. Large displacements may be visually evident and an approximate dip range may be estimated.

The user of the computed data should be aware of a particular characteristic of the interval correlation system. In order to prevent some data from not being used in the computation, the step distance is normally (as mentioned above) less than the correlation interval. This may allow a dominant anomaly (a large sharp peak or trough) to influence the dip answer for each step in which it is included in the correlation interval. This can cause two or more adjacent dips to be essentially identical, giving the user the impression that several parallel beds exist when in fact there may be only one. For example, a 25% step may produce four similar dips from one anomaly, a 33% step may produce three similar dips, and a 50% step may produce two similar dips.

If the user is aware of the parameters used for the computation he will recognize the duplications and interpret the dips correctly. However, if the effect is not desirable, a method called pooling may be used to present the results. In pooled plots, adjacent dips within a very small solid angle (2° to 3°) are presented as one dip answer. Dips that do not pool are still presented, so that no data is discarded.

Figure 2 shows another interval with both the unpooled and pooled results side by side. Note the groups of four dips on the unpooled data set that appear as single dips on the pooled result. Also evident is the marked decrease in dip density in the pooled data for the upper half of the log. This can be a desirable presentation, particularly when plotting data on reduced scales, such as 1:600 or 1:1200, for structural dip analysis.

 

Computation Parameter Selection

There are three basic types of interpretation problems that users of dipmeter data may wish to solve:

structural interpretation

large-scale stratigraphic features

maximum detail, very fine stratigraphic features, as observed on detailed core inspection.

It is often desirable to interpret a combination of the above from a single dipmeter log. As a result, a variety of systems have evolved to handle widely different requirements.

The most commonly used and generally applicable approach is the correlation interval system described earlier. For analysis of structure and large-scale sedimentary features, a 4-ft correlation interval and a 1-ft or 2-ft step is usually the first approach to analysis. For special applications or difficult logging conditions, other values of these parameters may be more useful. In fact, if the user of the data is specifically interested in one of the three interpretations mentioned above, parameters must be chosen to optimize that result.

Therefore, it is important to understand how the tadpole plot is affected by the choice of these parameters. For each step, a single dip answer is produced, and all the data within that correlation interval are used to obtain that single dip. A 4-ft interval may contain from 0 to more than 100 correlations, due to bedding contrasts, but only a single dip is calculated, based on the best fit of the correlation curves. Large correlation intervals tend to smooth the dip results. Short correlation intervals allow the system to find more detailed results.

Figure 3 contains a 4-m section of dipmeter computed in a sand section using several correlation intervals. Note that although the dip direction trend is similar in each, the implied cross-sectional view of the formation is significantly different.

 

Plot A clearly shows detailed internal sedimentary structures with a much better suggestion of environment than do B and C. Plot B retains most of the characteristics of Plot A, but with some apparent averaging and smoothing at dip magnitude boundaries. Plot C suggests large-scale, almost parallel crossbedding. This plot fails to indicate the more complex internal sedimentary structures evident on the A plot.

It is apparent from comparing these three computations that the choice of the computation parameters should be influenced by the type of information required to support exploration and production programs. Although the basic principles described in the foregoing apply to all correlation interval techniques, algorithms differ significantly for different tool types, allowing the best adaptation to the data obtained by the tool.

Dip Computations with the 4-Curve Dipmeter Tool

For the 4-curve tool, two correlation techniques are available to determine the magnitude of the dip and the azimuth of its direction: interval correlation (CLUSTER* Program), as described above; and feature correlation (GEODIP* Program), where individual peaks and troughs are first classified by size, shape, and other characteristics, and these features are matched from curve to curve, taking into account certain constraints. The objective of the latter method is to adapt the program to variations in bedding frequency and thickness, with the result that dip computations are made at points on the dip curves rather than over preselected intervals. This system then frees the computation from a fixed interval constraint, and allows computation of dips of individual bed boundaries.

Note: Throughout this document an asterisk (*) indicates a mark of Schlumberger.

The overlapping correlation sequences of CLUSTER processing are an improvement over previous programs, but it still has the disadvantage of a fixed, rigid correlation "window," unresponsive to variations in the density of geologic data in the curves.

A close study of dipmeter curves shows that many curve features or elements are identifiable from curve to curve. As shown in Figure 1 , these features have various thicknesses (from 1 in. to several feet), amplitudes, and shapes. Each feature may be considered to be the signature of a geological event in the depositional sequence of the formation. Moreover, the dip of the bed boundaries is not necessarily constant, and sometimes varies rapidly.

 

In the GEODIP program, each of the four dip curves to be correlated is mathematically decomposed into a depth-ordered sequence of ranked elements.

In feature extraction, which is the first phase of the program, elements such as peaks, troughs, spikes, and steps are identified in the curves. Each feature has one or two boundaries and a set of parameters that describe its shape.

In the second phase, the GEODIP program attempts to match elements of one curve with similar elements of the others according to the following logic:

· By a built-in order of precedence (e.g., first large troughs, then large peaks, then medium troughs, and so on), the program first evaluates higher-order features, then when necessary also evaluates lower-order ones. This is done during multiple passes through the four sets of elements.

· Because geologic strata are deposited in succession, their boundaries do not cross. So, if event A appears above event B on one curve, it cannot appear below event B on another. This is the rule of noncrossing correlations.

If no correlation can be found within the specified search angle among all four curves, the program lowers its standards and looks for 3-curve correlations instead. Planarity is monitored continuously, and if it fails to meet preset standards, the program makes no attempt at 4-curve dips, but computes the four different 3-curve dips and displays them all.

Because the program works from identifiable features on the curve, each one corresponds to a geologic event and the density of the output data depends on the density of geologic information at that level. This makes GEODIP processing particularly successful in fine-structured sedimentary sections and for definition of lithological changes, such as scour surfaces.

The calculation of dip angle at each depth is from displacements measured on boundaries rather than on feature centers. These boundaries are shown on the correlation curves of a GEODIP log. They are themselves useful features for interpreting lithology, as Figure 2 suggests.

 

Determining Data Quality

The geologic validity of each dip determination may be tested in several ways.

Closure If displacements are determined between each adjacent pair of curves, taken cylindrically (1-2, 2-3, 3-4, 4-1) they should have an algebraic sum of zero. (Moving from one electrode to the next, you should return to where you began after making a traverse of all four electrodes.) This condition is called perfect closure. Small closure errors may be due to inaccuracies in the computed displacements; large closure errors indicate that one or more of the correlations are in error.

Planarity Another test is for planarity, the condition that the four points should define a plane. After four displacements have been calculated, the lines joining diametrically opposed electrodes may fail to intersect, if there is an anomaly in the calculation or in the bedding.

For the 4-curve tool, the geometry of the pad linkage ensures that distances between opposing adjacent pairs remain equal. Displacements computed from opposite pairs of curves (h 1-2 and h 3-4 for example) must therefore be equal but opposite if the bedding surface is planar. (The line segment connecting Pads 1 and 2 on the dipping plane parallels and equals in length-but is oppositely directed to-the line segment connecting Pads 3 and 4, for example.) For perfect planarity, h_1-2+h_3-4 = 0  and h_2-3 + h_4-1 = 0.

Likeness A third test is for likeness, a quality derived from the correlogram, to compare the similarity of the curves. The highest correlation coefficient computed over the search interval is the likeness of the two curves, and the trial displacement of that maximum is the displacement retained for that interval of the curves. Since more than one cross correlation is required to compute a dip, the credibility of the dip answer is roughly proportional to the lowest likeness of all the correlations used.

Despite these tests, the results sometimes show excessive scatter that is not of geologic origin, particularly when shorter correlation lengths are selected to improve resolution. The CLUSTER program reduces the scatter in the output by statistically reducing the data. It is assumed that random noise does not repeat itself through small changes of the correlation environment. Thus, at a given level the redundancy inherent in having four correlation curves allows the curves to be grouped in various combinations in a search for consistency. In addition, coherence between consecutive overlapping levels above and below each point in the hole is checked.

The program computes correlations between five of six possible pairings of the four curves, taken two at a time. To define a plane, any two of these pairs must have one curve in common. The CLUSTER program, working with this output, considers eight such solutions. Each of the eight yields a solution for the true dip plane, and generally each is slightly different. Calculations from an adjacent level yield another set of eight solutions. Since the correlation interval is greater than the step distance, neighboring correlation intervals overlap. Comparison of dips from several overlapping levels (eight solutions from each level) shows statistical scatter among the different solutions, but there should be a tendency for many of them to "cluster" near some numerical value. When several solutions (not all from one level) fall within an acceptable range of values, the program quotes the value for the group, rejecting those that scatter outside. As a result, legitimate dip trends can be sorted from noise.

Computing Dip with 8-Curve Data

This section discusses the methods developed specifically for processing 8-curve data using the principles of interval and feature correlation, the presentation of the results, and the presentations available at the wellsite and at the computing centers.

The determination of formation dip measurements using the 4-curve dipmeter tool depends on the bedding plane being detected by at least three of the four measure electrodes. This, in turn, implies that the formation is well-bedded or laminated. Unfortunately this is not always the case, and for many formations pad-to-pad correlations are impossible to establish, making sedimentary studies difficult or impossible. Also, pad-to-pad correlations may be difficult in highly dipping formations or in highly deviated holes.

The 8-curve tool was designed specifically to overcome this limitation by providing two microresistivity curves, 3 cm apart, on each of the four pads. The density of the results is an order of magnitude higher than with previous 4-pad hardware and processing. In addition, the improved sonde velocity correction, using accelerometer data to compute instantaneous sonde speed and length of travel along the borehole, greatly increases the coherence of the results and helps salvage data affected by severe hole conditions.

The processing methods discussed here have been developed to take advantage of the tool improvements. They provide three independent computations of formation dip and allow adaptation of the interpretation of the results to the specific problem of interest (e.g., structural, sedimentary, geometry of the sand body).

Programs for computing dip from 8-curve measurements include the basic interval correlation program, called mean square dip (MSD), which uses all 28 possible cross correlations to compute 28 displacements (if all are successful). Since only two adjacent displacements are needed to define a plane, considerable redundancy has been built into the measurement system. The program thus tries to find a "best fit" plane that satisfies most of the displacements.

A second interval correlation method called continuous side-by-side (CSB) is also used. It only considers displacements computed from the side-by-side buttons on the pad. These four computed displacements represent the apparent angle of the set of bedding planes that cut across the borehole.

Finally, feature correlation is provided by the LOCDIP* computation. These pad-to-pad correlations are made over short intervals centered on bed boundaries, as defined by the major inflection points on the microresistivity curves. This method is used to identify and then correlate major individual curve features. The correlation lines are displayed with the actual microresistivity curves in a way similar to the GEODIP computation and presentation.

Mean Square Dip (MSD) Processing

At any one depth level, there are 28 possible cross correlations for the 8-electrode measurements, as compared to six for the 4-curve recording. As in 4-curve processing, the correlation method for the eight curves requires defining an interval length, a step, and a search angle; however, there is a significant difference in the way the cross correlation is made. In the standard interval correlation program, a specific interval of a reference curve is defined and then slid along the interval of the matched curve. For the 8-curve dipmeter tool, the MSD method considers the same depth interval on each curve and uses only the data within that interval to make correlations. In the case of low apparent dip, nearly all the data points within the interval are considered when the correlation is made. As the apparent dip increases, fewer and fewer points enter into the correlation. A limit is imposed when the search angle is increased until only half the points in the intervals are being used. This corresponds to an apparent dip of about 72° in an 8-in. borehole with a 4-ft correlation interval.

In areas where high dips (or high apparent dips due to deviated hole conditions) are expected, this limitation can be overcome by displacing the curves by a known amount before cross-correlations are attempted. The amount of the curve displacement or shift would be that corresponding to the displacement one would expect if the actual dip plane were the same as the assumed or "focusing" plane. Hence, the net displacement used in the dip computation is the interval shift plus the displacement computed between the curves after the shift. The focusing plane can be chosen as either

• a fixed plane defined by the analyst (default is a horizontal plane), or
  

a plane defined by a previously computed dip

For moderate structural dip computations, experience has shown that the following input parameters are usually satisfactory:

• interval length, typically 4 ft.
  

  step distance, expressed as a percent (usually 50%) of interval length-(e.g., for a 4-ft interval, step distance would be 2 ft)
  

search angle; 45° usually find most dips relative to a horizontal plane

The MSD program, then, is primarily used to determine structural dip by finding strong planar events crossing the borehole. The button-button displacements are computed and the best-fit plane through these displacements is found.

This initial best-fit can then be refined by an iterative process in which points beyond k (which varies from 2.5 to 1.4) standard deviations from this initial best-fit plane are rejected, and a best-fit plane through the remaining points is calculated. An empirical quality factor is assigned to the final best-fit plane. This factor, ranging from 0 to 20, is a function of the number of iterations made and the final number of displacements retained.

There is no vertical continuity logic or clustering routine in the MSD computation; each level is autonomously processed. The redundancy available (28 possible displacements, when two are enough to define a dip) reduces the possibility of producing mathematical dips or noise correlations.

Continuous Side-by-Side (CSB) Processing

Continuous side-by-side (CSB) processing is a unique feature of the 8-curve measurement and takes advantage of the fact that there is great similarity between the two microresistivity curves recorded by each pad since the two measure electrodes are separated by a horizontal spacing of only 3 cm. With side-by-side correlations, CSB processing is able to define formation dip that may not be apparent on pad-to-pad correlation. Even more important, the CSB program is responsive to the fine bedding structure of the formation, making it particularly effective for defining stratigraphic features. This is illustrated in
Figure 1 , where the curves recorded by Pads 2 and 3 are shown for 12 ft of hole. Side-by-side correlations are shown as thin lines, and, for reference, the pad-to-pad correlations found for the same interval are shown as thick lines. From this example, we see that the number of side-by-side correlations is approximately an order of magnitude greater than the pad-to-pad correlations, and that the resolution is on the order of a few inches.

 

Another important feature, due to the proximity of the buttons on the pad, is that the displacements found by side-by-side correlations are much smaller than pad-to-pad displacements. This allows the measurement of very high dips that are not detected by pad-to-pad correlation. For such cases, once credible dips are found by CSB processing, they can be used as input to the focusing option for the MSD program.

Figure 2 shows a conventional pad-to-pad MSD correlation for a case of high apparent dip. The well is deviated about 35° to the southwest, in the same direction as the regional structural trend (30° to 40°). Thus, a given bedding surface would cut the borehole high on the northeast side and low on the southwest side. Obviously, getting a good correlation is difficult, although the quality of the dip curves and the borehole condition is excellent. Figure 3 shows the results obtained with side-by-side CSB processing. In this case, the 3-cm spacing of the buttons allows an unambiguous correlation to be made.

 

In the standard CSB computation, each pair of microresistivity curves (e.g., buttons 1-lA) is cross-correlated using short correlation intervals of 12 in. or less, and under favorable conditions even 4 in. or 3 in. The step distance can be taken equal to half or three-quarters of the correlation interval. This gives a vector parallel to the dip plane. Under ideal conditions (planar beds) another vector is found at the same depth by cross-correlating the microresistivity curves of an adjacent pad (e.g., buttons 2-2A). These two vectors are then used to define a dip plane.

With only four side-by-side correlations, a cross-check is needed to verify that the bed is indeed planar. If it is, then displacements obtained using microresistivity curves from opposite pads (e.g., buttons 1-lA, 3-3A) should be equal in value but opposite in sign, and the dip can be obtained from any two orthogonal pairs at that depth. If this is not the case, however, a window is opened around the level under examination, and the vertical continuity of the displacements a certain number of levels above and below it is checked. The pad showing the best vertical continuity is kept. A similar procedure is then followed for Pads 2 and 4 and, again, the pad showing the best vertical continuity is kept. The orthogonal pair showing the smoothest continuity within the window is used for dip computation.

In order to evaluate the credibility of the dip, a quality value ranging from 0 to 20 is assigned to each dip according to the vertical continuity and the quality of the correlograms at the various levels or depths.

If the environment of deposition produces little contrast between beds or the formation is highly crossbedded with sequences terminating over lateral distances of the same order as the borehole diameter, then pad-to-pad correlation may be difficult or impossible due to curve dissimilarity. CSB provides an excellent solution to this problem.

Correlation intervals as small as 2 in. have been matched with detailed core information, although 6-in. to 1-ft correlation intervals are most commonly used.

Figure 4 shows the detail available from the CSB as compared to visible core features. To make this comparison the CSB was processed with a 6-in. correlation interval and a 2-in. step and then plotted on a scale one-quarter of full size in order to match with the core photographs. Good dip agreement is apparent. Note the low contrast on the dip curves correlating to the fore-sets in the lower one-third of the photo. The truncation visible on the core is also evidenced on the dip plot. Such detail would not be possible with standard pad-to-pad correlation systems.

 

The good likeness of the side-by-side curves is useful in cases of high apparent dip. Under these conditions it becomes difficult to find an unambiguous curve match between the pads. Use of the side-by-side configuration allows reliable measurement of displacements between the curves from the same pad and computed dip values.

LOCDIP Computation

As discussed earlier, inflection points on the microresistivity curves describe geological events in the depositional sequence of the formation. The purpose of the LOCDIP program is to detect the geological events, or boundaries, and where applicable to associate a dip precisely at that boundary independent of dips at other depths. Instead of correlating intervals of curves, it detects features (inflection points) on each curve and attempts to link these around the borehole, in a manner somewhat similar to GEODIP processing. There are, however, some important differences:

· To be retained as a LOCDIP result, an event must be recognized on at least seven of the eight microresistivity curves; GEODIP logic requires only three out of the four curves. Thus, LOCDIP logic is more demanding than GEODIP logic.

· A measurement of the planarity is derived for each of the possible dip planes at any level. The retained value corresponds to the surface that best approximates the set of these planes. By convention, a perfectly planar surface has a planarity of 100.

· Some events are recognized on only a few of the dip curves. In this case, the available correlations are traced across the applicable curves, with an "options" notation of "F" (fracture) or "P/L" (pebble or lens) for single-pad events or two/three-pad events, respectively. These interpretations, however, are not to be considered as certain, but rather as possible.

The processing of the 8-curve data is designed to extract the maximum amount of dip information from the raw curves. A well may present several interpretation problems due to variations in lithology and bedding characteristics. A single computation system may not offer the total solution. It is useful, therefore, to be able to combine the results of several types of computation in one presentation.

 

 

DUALDIP* Presentation

The DUALDIP presentation for the 8-curve dipmeter tool allows results from more than one computation to be combined. Figure 5 is an example of multiple computations on a short section. In the figure, the dips on the left side are side-by-side (CSB) results with a correlation length of 8 in. and a step of 4 in. This produces three dips per foot, or about 10 dips per meter.

 

The tadpoles on the right are of two types. The round-headed tadpoles were computed from pad-to-pad correlations with a correlation interval of 4 ft and a step of 2 ft. This is the MSD computation.

The triangular-headed tadpoles are LOCDIP computations, also known as pad-to-pad feature correlations. These dips usually correspond to the more prominent bed boundaries, and are computed by the earlier mentioned pattern-recognition system. For each LOCDIP computation which used seven or eight of the dip curves, a solid correlation line is drawn on the plot showing exactly where the bed boundary was interpreted. For each of these correlations a local dip is shown. If fewer than seven curves are correlated, then the correlation is shown as a dotted line, but dip is not computed.

This presentation not only gives a visual impression of the frequency of stratification and its planarity and parallelism, but it also allows the user to judge the validity of the correlations. This is of particular value in detailed studies of sedimentary features.

All three systems may not, nor should they necessarily, give the same dip answer. This characteristic can be used to great advantage in interpreting sedimentary features, particularly thin, highly bedded clastics.

In Figure 5 , the two local dips at A and B correspond to the top and bottom of a distinct sedimentary unit. They suggest the boundaries both dip at 1° northerly. All the finer bedding within these boundaries produced CSB or round-headed dips consistently north-northeast between 4° and 10°.

The internal bedding indicates sediment transport direction from south-southwest to north-northeast, with topset and bottomset surfaces approximately 1° northerly. The CSB result is different from that obtained from LOCDIP and MSD processing, whose computation system is restricted to major events, which can be correlated from pad to pad. The CSB logic favors events with some continuity; individual single events are less likely to be computed, particularly where both types are visible within the correlation interval. This tendency for different systems to favor different types of bedding planes has been very useful, particularly in the interpretation of fluvial environments.

Note also that the 4-ft MSD correlation showed the dip at C to be southwest about 90 and consistent over 4 ft. This is easily explained, considering the previous discussion of overlap effects, and it is supported by the LOCDIP computation at that depth. This boundary presents a dominant anomaly to the 4-ft correlation system, and for fine stratigraphy would be misleading by itself. When all bedding features, large and small, are parallel, all systems should give the same answer as at D.

Formation-Imaging Tool

Successful dipmeter interpretation depends greatly upon the accurate evaluation of geological features. The application of the classic dip patterns is a relatively simple matter when geological events such as current bedding or lateral accretion are known. In many complex environments this is a severe problem. A whole core over the zone of importance solves these problems, but whole core availability is the exception rather than the rule. Formation imaging provides a continuous oriented borehole representation that can be used in conjunction with a whole core or, in most cases, by itself to evaluate geological events.

Interpretation The goal of formation-image interpretation is to characterize formation properties to assist sedimentological interpretation, determine the presence of permeability paths and permeability barriers, help calculate net pay, plan perforation and fracturing, and to help decide whipstocks and where to drill next.

Formation images must always be interpreted after lithology has been fairly well defined, so supplemental data are usually necessary to enhance the confidence of image interpretation. As with other dipmeter interpretations, the more supplemental data available, the better the interpretation.

Measurement The clustered microresistivity buttons on two or four of the microscanner pads provide a continuous electrical image of the borehole wall. The pads are oriented at right angles to achieve a three-dimensional perspective. These resistivity data are then mapped to a gray-scale or color "corelike" borehole wall image. This allows fine-scale features to be described through essentially the same interpretation procedure as that used in the examination of slabbed cores. The images characterize many types of structural and stratigraphic features. These oriented features, combined with a conventional dipmeter plot, are used to evaluate these events to extend the reservoir geometry beyond the wellbore.

Images of the rock formation exposed by the wellbore are processed from the microresistivity traces. Each image pad covers 2.8 in. of the borehole wall. Thus, 22% of an 8-in. borehole can be imaged with two pads and 44% with four pads on each logging pass. This coverage can be increased with multiple logging passes. The tool also contains a triaxial accelerometer and three magnetometers for orientation and to enable speed corrections to be made on the acquired data.

Presentation of Images

Several presentations are available for displaying the data. The vertical scale provides the most striking difference between the formation-imaging presentations and other logs. The normal detail scale for logs is 1:240, while the formation images are presented on a 1:5 scale. The standard presentations can be broadly classified into two types: straight-line images and azimuthal images.

Straight-Line Images A straight-line presentation shows the images in a stationary horizontal scale ( Figure 1 ). This presentation is divided into several sections. The left section contains the depth scale, the pad orientation, and the borehole deviation. The long arrow on the tadpole indicates the direction of borehole drift; the body of the tadpole indicates the magnitude of deviation by its position on the horizontal scale. The small arrow shows the azimuth of Pad 1. The next section contains the caliper and resistivity correlation curves. The calipers from Pads 1-3 and 2-4 are shown. The resistivity curve is used only for correlation and not for quantitative purposes. Pads 3 and 4 of the 2-pad tool provide the image. Both the raw microresistivity traces and the processed images are presented. The microresistivity traces are from the 27 image buttons. The image traces are computer enhanced using 16 gray levels; they range from white (resistive) to black (conductive).

 

 

Another popular presentation is shown in Figure 2 . In this example, the formation images are displayed on the same depth scale as the dipmeter log. This scale is not as effective for identifying individual sedimentary features but is better for displaying the overall features of a zone and showing how they relate to dip patterns.

 

Azimuthal Images A BORMAP presentation is shown in Figure 3 . The horizontal scale shifts according to the respective azimuths of each pad. Thus, multiple passes can be merged to portray a more complete picture of the wellbore. In this example, images from two logging passes (from a tool with two imaging pads) were merged to cover approximately 44% of the well-bore. There are vugs present at 4208.7 ft and at 4210.4 ft. This presentation is very effective for secondary porosity evaluation and for sedimentary structure identification.

 

Image-Examiner Workstation

Image interpretation can be enhanced by means of a computer workstation equipped with image-examiner processing programs. This allows such interactive processing features as

scale changes of both the vertical and horizontal, to enhance the interpretation

a display of other logs for correlation on the same scales

graphic enhancement of specific features, such as bedding, texture, vugs, and fractures

dip computation of bedding surfaces, fault planes, and fractures

correlation of images to whole core sections, extending the interpretation to noncored sections

orientation of cores from features present in both the core and the formation images

quantification of images (such as sand count and calibration to core porosity) to increase interpretation accuracy

Dip Computation/Thin Bed Definition Computation of the dip magnitude and azimuth of specific beds is essential to many interpretations and can be performed on an image-examiner workstation. An example is shown in Figure 4 . The magnitude is measured from horizontal (0°) to vertical (90°). The azimuth of the downdip direction is measured from true north. The thin sand shown at 6969 ft dips to the northwest. A sine wave is fit through both the upper and lower surface of the sand, indicating a 39° dip magnitude and an azimuth of 317°.
These dips are "true dip", since hole deviation is compensated. Apparent dips may be presented if a direct comparison with a whole core is required The actual thickness of the sand stringer, measured be-the sine waves, is 1.61 ft.