SPWLA 48th Annual Logging Symposium, June 3-6, 2007
QUANTITATIVE STUDIES OF RELATIVE DIP ANGLE AND BED THICKNESS EFFECTS ON LWD DENSITY IMAGES ACQUIRED IN
HIGH-ANGLE AND HORIZONTAL WELLS
E.A. Uzoh, A. Mendoza, and C. Torres-Verdín, The University of Texas at Austin; W.E. Preeg,
Consultant, E. Stockhausen; Chevron Energy Technology Company
Copyright 2007, held jointly by the Society of Petrophysicists and Well Log
Analysts (SPWLA) and the submitting authors.
short-spaced and long-spaced sensors included in the
tool.
th
This paper was prepared for presentation at the SPWLA 48 Annual Logging
Symposium held in Austin, Texas, June 3-6, 2007.
INTRODUCTION
ABSTRACT
The interpretation of LWD measurements acquired in
HA/HZ wells is often questioned because of the effects
of well geometry together with the geometrical and
environmental properties of the rock formations
penetrated by the wells. Well geometry includes
relative dip angle, while rock formation properties
include bed thickness and formation density.
Therefore, reliable interpretation of log measurements
and quantitative evaluation of potential hydrocarbon
deposits requires understanding how geometrical and
borehole environmental effects as well as properties of
rock formations influence LWD measurements.
Logging While Drilling (LWD) density images
acquired in high-angle and horizontal (HA/HZ) wells
can reveal much about the sedimentary structure of rock
formations penetrated by the well. However, the effect
of sedimentary structure on the measured density has
only now begun to be explored.
This paper describes numerical simulations undertaken
to quantify the influence of relative dip angle and bed
thickness on LWD density images acquired in HA/HZ
wells penetrating thinly-bedded formations comprised
of alternating sands and shales. Typically, the azimuthal
binning scheme used to construct LWD density images
divides the tool into 16 azimuthal sectors, each sector
subtending an angle of 22.5o from the center of the tool.
Count rate data are binned to angular sectors facing
density detectors. Our objective is to assess the effects
of adjacent beds on sector density measurements due to
finite bed thickness and variable relative dip.
There is a limited volume of technical literature
available on the effects of borehole geometry and rock
formation properties on nuclear measurements. Passey
et al. (2005) summarized the technical challenges
associated with quantifying rock properties from
HA/HZ wells. Ellis and Chiaramonte (2000) simulated
neutron measurements acquired in HA/HZ wells and
used the results to explain how modeling improves the
understanding of complicated measurements acquired
in HA/HZ wells. Furthermore, Yin et al. (2006)
assumed theoretical LWD density and neutron tools to
examine the effects of borehole shape, azimuthal angle,
bed thickness, and relative dip on nuclear
measurements acquired in HA/HZ wells. They found
that the response of short- and long-spaced detectors in
HA/HZ wells depends on the tool’s azimuthal position
as well as on the relative dip angle. Their analyses were
based on the raw response of the detectors and they
emphasized the need to perform more studies using
specific source-sensor configuration of commercial
nuclear tools. Badruzzaman et al. (2007) assumed a
generic wireline tool to study sensitivity of density
measurements to bed thickness, azimuth, and borehole
angle. They analyzed the influence of processing
techniques on density measurements acquired across
thin beds. Radtke et al. (2006) evaluated the response
The Monte Carlo N-Particle (MCNP) transport code is
used to simulate LWD density measurements from
several combinations of relative dip angle and bed
thickness.
Commercial
count-rate
processing
techniques are applied to the short-spaced and longspaced detector measurements in each sector. The
assumed source-sensor configuration corresponds to the
commercial adnVISION675® LWD nuclear tool
designed to operate with an 8.25-in stabilizer in an 8.5in borehole.
Our study provides a way to estimate the corresponding
depth shifts in true stratigraphic thickness (TST)
observed in HA/HZ wells, which are caused by the
difference in the radial lengths of investigation of the
®
Mark of Schlumberger
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
of a commercial LWD density tool to sandwiched rock
formations of varying bed thicknesses using numerical
modeling. Their study excluded the effects of relative
dip since they primarily focused on the simulation of
rock formations penetrated by horizontal and vertical
wells. Radtke et al. (2006) found that for the case of
thinly-bedded reservoirs, density measurements
acquired in horizontal wells resolved thinner beds than
in vertical wells. They also emphasized the technical
challenges of estimating accurate bed boundaries and
formation density in reservoirs with thin beds.
According to Radtke et al (2006), it is possible to
improve the reliance on density measurements by
estimating the bed thickness from density images.
detectors. In addition, we attempt the estimation of the
relative dip angles using similar techniques commonly
applied to azimuthal borehole resistivity logs. Through
a back calculation, we estimate values for the correction
term usually included in the assessment of electrical
penetration. Finally, we compare the effects of drilling
up-section versus down-section on LWD density
measurements acquired in HA/HZ wells.
METHODOLOGY
Azimuthal Binning Scheme of the LWD Density Tool
– Figure 1 describes the azimuthal binning scheme
applied on LWD density tool measurements in this
study. As described by Radtke et al. (2003), the tool is
divided into 16 sectors, each subtending an angle of
22.5o from the center of the tool. To conform to
conventions used in the LWD industry, tool rotation
commences and stops in the top quadrant. In our
modeling, we assume that the tool is eccentered in the
borehole and exhibits a maximum tool standoff when
the tool faces the top quadrant. The start and end
positions of the tool are designated by sectors 0 and 15,
as shown in the diagrams of Fig. 1a and Fig. 1b,
respectively. As shown in Fig. 1, to simulate the density
measurement, the LWD tool is placed at the center of
each sector (at an angle of 11.25o). In so doing, we
assume that a measurement obtained in a sector of the
tool represents the average LWD density measurement
for that sector. To justify this assumption, we analyzed
the azimuthal sensitivity of a commercial LWD density
tool. Figures 2a, 2b, and 2c describe the sensitivities of
the source, short- and long-spaced detectors of the
assumed LWD tool around the perimeter of the
borehole. Based on this figure, we can conclude that as
much as 80% of the azimuthal density response
originates from an azimuthal angle of about 25o of the
borehole, thus validating our assumption. Mendoza et
al. (2007) provide a comprehensive description of the
procedures involved in obtaining the azimuthal
measured sensitivities shown in Fig. 2.
Clearly, more information is needed to interpret
measurements acquired in HA/HZ wells, particularly
for decreasing values of bed thickness. Density images
embody a wealth of information about borehole
geometry and formation environment. They facilitate
the selection of bed boundaries and the estimation of
relative dip angles and are commonly used in
geosteering and completion decisions. However,
quantitative use of density images is yet to be explored
in formation evaluation.
In this paper, we use density images to quantify the
effects of high relative dip angle, bed thickness, and
tool standoff on LWD density measurements acquired
in HA/HZ wells. To that end, we simulate the response
of a commercial LWD density tool in measured depth
to variations of bed thickness and relative dip using the
MCNP code developed by Los Alamos National
Laboratory. In so doing, we maintain a fixed quarterinch difference between the tool and the top of the hole
and a zero standoff at the bottom of the hole as is
typically the case in HA/HZ wells. Two dimensional
(2D) measurements are simulated from the sequential
translational and rotational motions of the density tool,
similar to the actual measurement acquisition
environment during drilling. These simulated 2D
measurements are subsequently used to construct the
borehole image.
Description of the Formation – The formation models
adopted in this paper are similar to those described by
Radtke et al. (2006). Beds alternate between high and
low densities but have a constant thickness. While the
density of the low-density bed is 2.0 g/cm3, the highdensity bed has a density of 2.6 g/cm3. Such a density
range is representative of the typical values found in
practice. The same density range also allows us to study
density measurements acquired across large density
contrasts between formation layers. Table 1 provides a
summary of fluid and rock-formation properties used in
the modeling of alternating sand-shale sequences. In
our simulations, we assumed an 8.5-in borehole drilled
with freshwater mud. Likewise, we assume that tool
In the remainder of the paper, we examine the
individual detector response for each sector together
with the processed density data along the well
trajectory. Visualizing the detector response for each
azimuth facilitates the quantitative analysis of effects
due to HA/HZ well geometry on density measurements.
Important observations made from the simulation
results include that short- and long-spaced detector
response are out of phase; therefore their modulation
amplitudes do not coincide as in the case of vertical
wells. This phase difference is due to the discrepancy in
the radial length of investigation (LOI) of the two
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
standoff varies from 0 in at the bottom quadrant to 0.25
in at the top.
Approximately 300 minutes of CPU time were required
to simulate density measurements for each detector at
each measurement location in the HA/HZ wells. To
ensure a statistical error below 0.7%, 120 million
particle histories were used to compute the short- and
long-spaced density measurements. Processing
techniques and algorithms applied to the count rates
from each detector are those used with commercial
software developed for the tool. Specifically, rib
corrections were applied to compensate for standoff
between the tool and formation based on the difference
between short- and long-spaced densities.
SIMULATION OF DENSITY MEASUREMENTS
Simulations of density measurements were performed
using the MCNP code. Because the tool is symmetric,
we simulated only the tool’s response to one-half of the
perimeter of the borehole. Therefore, we rotated the
tool in the clockwise direction from an azimuth of
11.25o (top quadrant) to 168o (bottom quadrant). An
azimuthal sampling rate of 22.5o was used to ensure that
the tool remained in the mid-point of each sector. Thus,
for each depth sampling point, there were eight density
simulations around the borehole perimeter.
Density Measurement in a 95o Well –Figure 4 displays
the azimuthal density response of each detector and
their density correction curves when measurements are
acquired in a 95o well penetrating 6-in laminated beds.
We note that the short- and long-spaced detector
density curves are out of phase. This exercise represents
the LWD tool drilling up-section. Figure 5 shows the
density images for a 95o well penetrating 6-in laminated
beds.
The general principle of operation of the density tool is
such that gamma rays emitted from the source are
scattered to the scintillating detectors. The received
gamma ray flux is inversely proportional to the electron
density of the formation, which is related to the bulk
density by the expression
(1)
ρ e = 2 ( Z / A ) ρb ,
Figure 6 shows the density response when the 95o well
penetrates 4-in laminated beds. The observed effect of
bed thickness is the decrease in the amplitude of the
long-spaced density modulation below that simulated
for the 6-in formation. Figure 7 shows the density
images simulated for this case.
where ρe is electron density, ρb is formation density and
(Z/A) is the ratio of the atomic number to the atomic
weight of the formation. Using MCNP, we tracked the
transport of photons from the gamma ray source to the
detectors.
Three bed thicknesses (2in, 4in, and 6in) were
considered in the laminated formation models used in
this study. Each of these formations was penetrated by
wells inclined at three different angles from the vertical.
Well inclinations span across HA/HZ wells as
described in the technical literature. According to
Passey et al. (2005), HA wells are inclined at 60o – 80o
from the vertical axis while HZ wells include those
with inclination greater than 80o from the vertical axis.
Therefore, in the simulations we considered wells with
relative dip angles of 95o, 100o, and 105o relative to the
top of the hole. We note that the 90o case has been
previously studied by Radtke et al. (2006)
Figure 8 shows the simulated density response with the
95o well penetrating 2-in laminated beds. The simulated
short- and long-spaced density curves average bed
densities in a similar manner to what was observed by
Radtke et al. (2006). Figure 9 shows the density images
associated with this well.
Density Measurements in a 100o Well – We now
consider the case of a 100o well penetrating alternating
beds. Figure 10 displays simulated short- and longspaced density measurements in 6-in laminated beds.
Figure 11 displays the images acquired in this case.
Figure 12 summarizes the simulated density for the
various detectors when measurements are in a 100o well
penetrating 4-in laminated beds. The amplitude of the
oscillations of the short- and long-spaced density curves
are slightly lower than those simulated for the 4-in
formation penetrated by a 95o well. Figure 13 shows the
density images acquired when the 100o well penetrates
4-in beds.
Figure 3 describes the measurement acquisition in a
HA/HZ well penetrating a laminated formation. The
true vertical depth (TVD) is measured from the bottom
of the borehole as described by Radtke et al. (2006).
Furthermore, the zero-depth position is the interface
between the high-density and low-density bed with the
low density bed located above this interface. To secure
acceptable resolution and save CPU time, we assumed a
sampling rate in TVD of 1.2 in for 6-in beds, 1 in for 4in beds, and 0.5 in for 2-in beds. In measured depth,
these sampling rates vary with relative dip angle.
However, field measurements are sampled at a higher
density of 1.2 in in measured depth.
Figure 14 shows the various detector measurements as
well as the density correction curves when the 100o
well penetrates 2-in bed laminations. As expected,
peaks of the density fluctuations simulated for this well
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
are lower than those simulated when a 95o well
penetrates 2-in bed laminations.
Density images
simulated from the 100o well penetrating the 2-in bed
laminations are shown in Fig. 15.
We estimated the relative dip angle, α, from simulated
short- and long-spaced, and compensated density
images. To that end, we applied the same technique
used in electrical images (Yin et al., 2006).
Accordingly, as shown in Figs. 22 through 24, we fit a
sinusoid to the bed boundary. The vertical axis of these
images is in measured depth: A, is the crest of the
sinusoid and B is the trough. Relative dip angle is
calculated with the equation
⎛ A− B ⎞ ,
(2)
α = tan −1 ⎜
⎟
⎝ D + 2 ΔR ⎠
where D is the bit size and ΔR is referred to as the
electrical penetration. Table 2 describes estimates of ΔR
required to yield accurate relative dip angles from
short- and long-spaced, and compensated density
images. From back calculations, we note that ΔR must
be 1.5in and 1in to estimate accurate relative dip angles
from the compensated and long-spaced density images,
respectively. Relative dip angles estimated from the
short-spaced density images require no correction for
measurement penetration.
Density Measurements in a 105o Well – Finally, we
consider the case of a 105o well penetrating 6-in, 4-in,
and 2-in bed laminations. Figure 16 shows the
simulated azimuthal density measurements for various
detectors, acquired for the 105o well penetrating 6-in
laminated beds.
Peaks of the short- and long-spaced density modulation
remain similar to those obtained when a 95o or 100o
well penetrates 6-in bed laminations. Furthermore, as in
the case of 95o and 100o wells, the corresponding
density images, shown in Fig. 17, accurately resolve the
bed thickness.
Figure 18 shows the short- and long- spaced density
curves for the case of 4-in bed laminations. Figure 19
shows the density images simulated for this case.
Figure 20 shows the simulated short- and long-spaced
density logs when the 105o well penetrates 2-in bed
laminations. At the top quadrant, the long-spaced
density curve has slight deflections. Figure 21 shows
the density images simulated for this case. These
density images do not resolve the bed thickness as for
the case of 4-in or 6-in beds.
Finally, we compared the effect of the drilling direction
on LWD measurements acquired in HA/HZ wells.
Figures 23 and 24 display the simulated density
measurements while the tool is assumed to travel downsection. We note that measurements simulated in this
case are similar to those acquired while drilling upsection. The estimated relative dip angles with respect
to the top of the borehole are also listed in Table 2 for
this case. Therefore, we conclude that there are no
significant differences between LWD measurements
acquired drilling up-section or down-section in HA/HZ
wells.
SUMMARY AND CONCLUSIONS
Simulated azimuthal density measurements acquired
with the short- and long-spaced detectors have been
applied in this study to assess the effects of relative dip
angle, tool standoff, and bed thickness in HA/HZ wells.
While the maximum standoff was assumed to be 0.25in throughout the study, relative dip angles and bed
thicknesses were varied to include a practical range of
environmental and geometrical conditions.
ACKNOWLEDGEMENTS
We thank Schlumberger for providing the tool
configuration used in this paper and M. Evans, R.J.
Radtke, and J. Rasmus of Schlumberger for their expert
guidance throughout the project. The work reported in
this paper was funded by The University of Texas at
Austin’s Research Consortium on Formation
Evaluation, jointly sponsored by Anadarko, Aramco,
Baker Atlas, BP, British Gas, ConocoPhilips, Chevron,
ENI E&P, ExxonMobil, Halliburton Energy Services,
Hydro, Marathon Oil Corporation, Mexican Institute for
Petroleum,
Occidental
Petroleum
Corporation,
Petrobras, Schlumberger, Shell International E&P,
Statoil, TOTAL, and Weatherford.
Our simulation work indicates a general depth
difference between short- and long-spaced detector
measurements as the tool rotates around the perimeter
of an inclined borehole. Relative to the short-spaced
density curve, the long-spaced density curve assumes
the following positions: first, it is above the short
spaced response when the tool faces the top quadrant
(sector 0); second, it is in phase with the short-spaced
response when the tool faces the left/right quadrant;
third, it shifts above the short-spaced density curve
when the tool faces the bottom quadrant. These depth
shifts are associated with HA/HZ wells and are due to
the difference in the radial lengths of investigation of
the long-spaced and short-spaced density detectors.
REFERENCES
Badruzzaman, A., Mendoza, A., Stockhausen, E.J., and
Reik, B. A., 2007, “Density measurement sensitivity
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
to varying angle and tool azimuth in medium to thin
beds,” 2007 SPWLA Annual logging Symposium,
Austin, Texas, June 3-6.
Ellis, D.V., and Chiaramonte, J.M., 2000,“Interpreting
neutron logs in horizontal wells: a forward modeling
tutorial,” Petrophysics, vol. 41, no. 1, pp. 23-32.
Mendoza A., Torres-Verdín, C., and Preeg, W.E., 2007,
“Rapid simulation of borehole nuclear measurements
based on approximate spatial flux-scattering
functions,” 2007 SPWLA Annual logging
Symposium, Austin, Texas, June 4-7.
Passey, Q.R., Yin, H., Rendeiro, C.M., and Fitz, D.E.,
2005, “Overview of high angle and horizontal well
formation evaluation: issues, learning, and future
directions,” 2005 SPWLA Annual Logging
Symposium, Paper A, New Orleans, Louisiana, June
26-29.
Radtke, R.J., Evans, M., Rasmus, J.C., Ellis, D.,
Chiaramonte, J.M., Case, R.C., and Stockhausen, E.,
2006, “LWD density response to bed laminations in
horizontal and vertical wells,” 2006 SPWLA Annual
Logging Symposium, Paper ZZ, Veracruz, Mexico,
June 4 -7.
Radtke, R.J., Adolph, R. A., Climent, H., and Ortenzi,
L., 2003, “Improved formation evaluation through
image-derived density”. 2003 SPWLA Annual
Logging Symposium, Paper P, Galveston, Texas,
June 22-25.
Yin, H., Han, X., Xu, L., Guo, W., Shehata, A., and
Gardener, R.P., 2006, “Field and benchmark studies
of LWD nuclear tool response in high-angle and
horizontal wells,” 2006 SPWLA Annual Logging
Symposium, Paper AAA, Veracruz, Mexico, June 47.
Austin in 2002 and 2005, respectively. From 2002 to
2003 he worked for Schlumberger as a field engineer in
the area of well testing. He has been an intern with
Schlumberger-Doll Research and Chevron. He was
granted a 2003-2004 scholarship by the SPWLA. His
research interests include petrophysics, log analysis,
inverse problems, and well testing.
Carlos Torres-Verdín received a Ph.D. degree in
Engineering Geoscience from the University of
California, Berkeley, in 1991. During 1991–1997 he
held the position of Research Scientist with
Schlumberger-Doll Research. From 1997–1999, he was
Reservoir Specialist and Technology Champion with
YPF (Buenos Aires, Argentina). Since 1999, he has
been with the Department of Petroleum and
Geosystems Engineering of The University of Texas at
Austin, where he currently holds the position of
Associate Professor. He conducts research on borehole
geophysics, well logging, formation evaluation, and
integrated reservoir characterization. Torres-Verdín has
served as Guest Editor for Radio Science, and is
currently a member of the Editorial Board of the
Journal of Electromagnetic Waves and Applications,
and an associate editor for Petrophysics (SPWLA) and
the SPE Journal. He is co-recipient of the 2003 and
2004 Best Paper Award by Petrophysics, and is
recipient of SPWLA’s 2006 Distinguished Technical
Achievement Award.
William E. Preeg received a Ph.D. degree in Nuclear
Science Engineering from Colombia University in
1970. From 1980 to 2002, he held various positions
with Schlumberger, including Director of Research at
Schlumberger-Doll Research, Vice President of
Engineering Houston as well as Manager of the Nuclear
Department at Schlumberger-Doll Research. Prior to
1980, he worked for Los Alamos Scientific Laboratory,
Aerojet Nuclear Systems Company, and the Atomic
Energy Commission, largely in the area of nuclear
radiation and transport. He has also served on advisory
committees at The University of Texas at Austin, Texas
A&M University, Colorado School of Mines, and
Georgia Tech.
Ed Stockhausen is a staff research scientist with
Chevron Energy Technology Company and is based in
Houston, Texas. Ed graduated from the University of
Florida with an M.Sc. degree in geology in 1981. Over
the next 16 years, he held various exploration and
production geology positions for Chevron in New
Orleans,
focusing
on
Gulf-of-Mexico
field
development. For the past eight years, Ed has been
responsible for leading the development and
deployment of geosteering technology for Chevron
worldwide.
ABOUT THE AUTHORS
Echezona Uzoh is a Graduate Research Assistant and a
M.Sc. candidate in the Department of Petroleum and
Geosystems Engineering of The University of Texas at
Austin. He obtained a B.Sc. degree in Mechanical
Engineering from the University of Lagos, Nigeria in
2004. He is a recipient of the 2005 Moni Pulo/Baker
Hughes Endowed Scholarship. He has worked as a
nuclear scientist intern with Baker Hughes/INTEQ. His
research focuses on petrophysics and well-log
interpretation.
Alberto Mendoza is a Graduate Research Assistant
and a Ph.D. student in the Department of Petroleum and
Geosystems Engineering of The University of Texas at
Austin. He received both B.Sc. and M.Sc. degrees in
Petroleum Engineering from The University of Texas at
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
Table 1: Rock and fluid properties assumed in the
simulation of borehole density measurements
Variable
High density bed
Low density bed
Borehole fluid density
Borehole diameter
Maximum tool standoff
% Quartz in high density bed
% Calcite in high density bed
% Illite in high density bed
%Water in high density bed
% Quartz in low density bed
% Calcite in low density bed
% Illite in low density bed
%Water in low density bed
Units
g/cm3
g/cm3
g/cm3
in
in
Value
2.6
2.0
1.0
8.5
0.25
60
0
0
40
62
30
4
4
(a)
Fig. 2: (a) Spatial Sensitivity of the gamma- ray source
(Cs -137) of the commercial density tool. The upper
panel displays the spatial sensitivity in the radial and
azimuthal directions while lower panel contains spatial
sensitivity in the azimuthal direction only. (b) Similar to
(a) but for the short-spaced detector of the density tool.
(c) Similar to (a) but for the long-spaced detector of the
density tool.
Table 2: Estimates of relative dip angle from short- spaced,
long-spaced, and compensated density images.
Bed True
(in) Dip
(o)
6
4
6
4
6
4
6
95
95
100
100
105
105
85
SS
density
Est.
Est.
Dip
ΔR
(o)
(in)
LS
density
Est.
Est.
Dip
ΔR
(o)
(in)
Compensated
density
Est.
Est.
Dip
ΔR
(o)
(in)
95
95
100
100
105
105
85
95
95
100
100
105
105
85
95
95
100
100
105
105
85
U
-0.034
0.224
0.113
0.102
0.098
0.098
-0.034
0
0.997
0.999
1.000
1.059
0.986
1.000
1.000
15
(c)
(b)
1.515
1.472
1.500
1.494
1.518
1.500
1.515
U
0
α
L
R
mud
l
B
(a)
L
mud
col
Fig. 3: Section views of a density tool in a well with
relative dip angle, α, and 8.5-in borehole. Bed
thickness is 2in; dark gray is the drill collar; dark
blue is tungsten shielding and the sensors are orange.
Brown layers have a low density and light gray layers
have a high density. The arrow shows the direction of
drilling (up-section). Standoff between the tool and
the borehole wall is largest at the top quadrant. True
vertical depth is measured from the interface tangent
to the bottom of the borehole as indicated by the
number 0 in the figure in the right panel. This
convention implies that the logs in the figures
reference the source as the measurement point, unlike
commercial processing.
R
B
(b)
Fig. 1: (a) Cross section of an LWD density tool
showing the relative location of the borehole and the
azimuthal binning scheme. The brown region is the
gamma ray sensors; dark blue is tungsten shield; light
gray is the collar and stabilizer; light blue is drilling
mud. The arrow shows the direction of tool rotation.
Sector 0 is the tool’s start position (top of hole). The
bottom (B), right (R), up (U) and, left (L) quadrants are
used in reference. (b) Sector 15 is the tool’s end
position.
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
M
(b)
Fig. 4: (a) Filtered short- and long-spaced detector response in a 95o well penetrating 6-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector
is indicated at the top of each panel. (b) Density correction (Δρ) curves.
(c)
(d)
(a)
(e)
(b)
Fig. 5: (a) Filtered short-spaced detector density image from a 95o well penetrating 6-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
(b)
Fig.6: (a) Filtered short- and long-spaced detector response in a 95o well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each
sector is indicated at the top of each panel. (b) Density correction (Δρ) curves.
(c)
(d)
(e)
(a)
(b)
Fig. 7: (a) Filtered short-spaced detector density image from a 95o well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
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SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
M
(b)
Fig. 8: (a) Filtered short- and long-spaced detector response in a 95o well penetrating 2-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector
is indicated at the top of each panel. (b) Density correction (Δρ) curves.
(a)
(e)
(b)
(d)
(c)
Fig. 9: (a) Filtered short-spaced detector density image from a 95o well penetrating 2-in bed laminations. Light brown
blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector density
image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density image.
9
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
(b)
Fig. 10: (a) Filtered short- and long-spaced detector response in a 100o well penetrating 6-in bed laminations.
Light brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each
sector is indicated at the top of each panel. (b) Density correction (Δρ) curves.
(a)
(b)
(c)
(d)
o
(e)
Fig. 11: (a) Filtered short-spaced detector density image from a 100 well penetrating 6-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
10
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
M
(b)
Fig. 12: (a) Filtered short- and long-spaced detector response in a 100o well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector
is indicated at the top of each panel. (b) Density correction (Δρ) curves.
(c)
(e)
(a)
(d)
(b)
Fig. 13: (a) Filtered short-spaced detector density image from a 100o well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
11
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
(b)
Fig. 14: (a) Filtered short- and long-spaced detector response in a 100o well penetrating 2-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector is
indicated at the top of each panel. (b) Density correction (Δρ) curves.
(a)
(e)
(c)
(d)
(b)
o
Fig. 15: (a) Filtered short-spaced detector density image from a 100 well penetrating 2-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
12
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
M
(b)
Fig. 16: (a) Filtered short- and long-spaced detector response in a 105o well penetrating 6-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector is
indicated at the top of each panel. (b) Density correction (Δρ) curves.
(a)
(b)
(c)
(d)
o
(e)
Fig. 17: (a) Filtered short-spaced detector density image from a 105 well penetrating 6-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
13
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
(b)
Fig. 18: (a) Filtered short- and long-spaced detector response in a 105o well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector is
indicated at the top of each panel. (b) Density correction (Δρ) curves.
(d)
(e)
(a)
(c)
(b)
o
Fig. 19: (a) Filtered short-spaced detector density image from a 105 well penetrating 4-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
14
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
LS
SS
(a)
M
(b)
Fig. 20: (a) Filtered short- and long-spaced detector response in a 105o well penetrating 2-in bed laminations. Light
brown blocks identify low-density beds and white blocks identify high-density beds. The tool position in each sector is
indicated at the top of each panel. (b) Density correction (Δρ) curves.
(a)
(c)
(e)
(d)
(b)
o
Fig. 21: (a) Filtered short-spaced detector density image from a 105 well penetrating 2-in bed laminations. Light
brown blocks identify low-density beds and dark brown blocks identify high-density beds. (b) Long-Spaced detector
density image. (c) Density correction (Δρ) image. (d) Compensated density image. (e) Alpha-processed density
image.
15
SPWLA 48th Annual Logging Symposium, June 3-6, 2007
(a)
(b)
(c)
Fig. 22: (a) Estimation of relative dip angle from a short-spaced density image acquired from a 95o well
penetrating 6-in bed laminations. The black line identifies the modeled bed boundary. The letter A identifies the
trough and the letter B identifies the crest (b) Similar presentation as in (a) but for a long-spaced density image (c)
Similar presentation as in (a) but for a compensated density image.
LS
SS
Fig. 23: Filtered short- and long-spaced detector response in an 85o well penetrating 6-in bed laminations with
drilling down-section. Light brown blocks identify low-density beds and white blocks identify high-density beds.
The tool position in each sector is indicated at the top of each panel.
(d)
(e)
(c)
(b)
(a)
o
Fig. 24: (a) Filtered short-spaced detector density image from an 85 well penetrating 6-in bed laminations with
drilling down-section. Light brown blocks identify low-density beds and dark brown blocks identify high-density
beds. (b) Long-Spaced detector density image. (c) Density correction (Δρ) image. (d) Compensated density image.
(e) Alpha-processed density image.
16