SPWLA 56th Annual Logging Symposium, July 18-22, 2015
RAPID AND PRACTICAL CHARACTERIZATION OF NEARWELLBORE LAYER STRUCTURE AND PROPERTIES IN HIGH-ANGLE
AND HORIZONTAL WELLS
Mohammad Taghi Salehi, Joan Abadie, Shahzad Asif, Koji Ito, David Maggs, Chris Morriss, Luca Ortenzi, John
Rasmus, Roger Griffiths, Schlumberger
Copyright 2015, held jointly by the Society of Petrophysicists and Well Log
Analysts (SPWLA) and the submitting authors.
This paper was prepared for presentation at the SPWLA 56th Annual Logging
Symposium held in Long Beach, California, USA, July 18-22, 2015.
Once the LLM has been created and populated with
formation properties, the corresponding logging tool
measurement response is simulated by a fast-forward
modeling algorithm. Through a model-compare-update
workflow, the user adjusts the formation model so that
the forward modeled logs and images reasonably match
the measured responses. In many cases, especially in
beds thicker than 6 inch, little to no manual adjustment
is required.
ABSTRACT
Complex geometry effects observed on log responses in
high-angle and horizontal (HaHz) wells can create
challenges in achieving accurate formation evaluation.
The situation is made even more difficult in thinly
bedded reservoirs where measurements may respond to
multiple layers within their volume of investigation.
Recent publications have outlined techniques in which
a layered earth model is used to define the geometry of
layering relative to a wellbore. A model-compareupdate workflow is then used to solve for layer
properties. Although these techniques are efficient in
horizontal wells, they require good geological
understanding to manually create the formation model
and can be time consuming if there are many thin
layers. This paper presents a semiautomatic method to
construct a layered earth model in the immediate
proximity of the borehole and to solve for the formation
properties and geometry of the layers locally. The
approach is particularly useful for shallow-reading
measurements and complements the more extensive
layered earth models commonly used for modeling the
response of deeper reading measurements.
When modeling log responses in HaHz wells there is
not always a unique solution to the problem. There are
two main unknowns in the layer-cake formation model:
1) the layer geometry, that is to say the boundary
positions and dips, and 2) the layer petrophysical
properties. In this new workflow, the layer geometry is
clearly defined by the wellbore images, leaving the
layer properties as the main unknown. In many cases,
the layer properties can be read directly from highresolution measurements such as density images, but
this is not always the case in thin beds or nonplanar
layers or when lower resolution non-azimuthal
measurements are interpreted (e.g., neutron porosity).
By enforcing a common formation geometry and
matching simulated logs that take into account both
geometry and formation properties with measured logs,
the described workflow significantly increases the
confidence in the computed layer geometries and
properties.
In this method, high-resolution density and volumetric
photoelectric factor (PEF) images, acquired by loggingwhile-drilling (LWD) tools while rotating, are analyzed
to define the boundary position, dip, and properties of
layers as thin as 2 inch. The computed dips, layer
boundaries, and log values are then used to
automatically create a local layer model (LLM) and
provide an initial estimate of the density and PEF layer
properties in the high-angle intervals. In the horizontal
intervals where layer boundaries do not cross the
wellbore axis, the user completes the LLM manually
using the density image projected onto the well
trajectory as an aid.
The methodology is demonstrated on two highangle/horizontal wells, one from offshore West Africa
and the other from the North Sea. The paper shows how
the LLM is quickly created and updated to provide a
formation model proximal to the wellbore. The rapidly
created LLM provides information about formation
geometry, which facilitates determination of the true
properties of thin layers, free from the geometry effects
that are observed on the measured logs. The true layer
properties enable more accurate formation evaluation
than use of the geometrically uncorrected measured
logs.
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The automatic inversion technique has been described
previously (Shetty et al., 2012). Where the user wants
to override the inversion result or when the layer has
not been crossed by the wellbore, such as in bed
parallel situations, the MCU method described in this
paper can be used to determine the layer properties.
This combined new workflow results in two significant
developments: 1) the combining of inversion and
manual methods into one workflow to determine the
layer properties and 2) the ability to define the
properties of non-crossed layers that influence the
measurement.
INTRODUCTION
Formation evaluation in high-angle and horizontal
(HaHz) wells is a known challenge for the petrophysics
community (Passey et al., 2005). Logs acquired in these
wells may be responding to more than a single layer,
thus complicating interpretation. This has led to HaHz
well logs not being used to their full potential and also
to overestimation or underestimation of the reservoir
volumetrics when the measured logs are directly used
for quantitative petrophysical evaluation.
A workflow has been developed and introduced to the
industry that corrects measured logs for geometrical
effects in HaHz wells (Griffiths et al., 2012). The
measured logs are used to manually define a layered
formation model surrounding the wellbore. The
position of the layers and their relative dip to the
wellbore trajectory are determined from the logs and
images acquired in the HaHz well. The measured logs
are also used to obtain initial estimates of the formation
properties of each layer. Fast-forward models of the
logging measurements are then used to compute the
tool responses based on the geometry and formation
properties defined by the formation model. The
geometry and/or formation properties are manually
updated until an acceptable match between the forward
model and measured logs is achieved.
Formation dip is computed automatically from features
on the density images (Shetty et al., 2012). The
automatic dip method is fast and is not subject to user
bias. This eliminates the potential errors in relative dip
associated with manual dip picking.
In high-angle intervals, the initial LLM is automatically
computed from the density and photoelectric factor
(PEF) images. This results in significant time saving,
especially when analyzing thinly laminated formations.
For the horizontal sections where the well is parallel to
the bedding, the layer geometry and properties are
defined automatically or by the user based on an
analysis of the images. Projection of the density image
onto the well trajectory provides a guide for helping the
user define and verify boundary positions.
After completion of this model-compare-update (MCU)
workflow, the final formation model is a validated
representation of both the subsurface geometry and
formation properties. The resulting formation properties
are then available for use in conventional formation
evaluation techniques. The MCU workflow provides a
very efficient interpretation methodology in horizontal
wells where layers are crossed multiple times. Because
the initial model is manually defined by the user, an
extensive understanding of the geological structure is
required. Also, in the case of thinly bedded formations,
building the model manually can be a time-consuming
task.
Figure 1 shows the MCU methodology with the LLM
definition as the first step. It also shows how the
inversion methods are integrated into the workflow.
This paper presents a method for visualizing and
interacting with a formation model close to the
wellbore, typically a few feet radially from the
trajectory centerline. The formation model is projected
onto a 2D curtain section and referred to as a local layer
model (LLM). The LLM characterizes the formation
around the wellbore within the depth of investigation of
the nuclear measurements. The LLM is then modified
and validated using inversion and MCU methodologies.
Fig.1 MCU and inversion methodologies used in
evaluation of near-wellbore geometry and layer
property.
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contours are fitted with simple sinusoids that represent
planar boundaries. At high relative dip, a boundary may
be detected over several hundred feet. Over this long
interval, the boundary may not be planar nor the well
inclination constant, so a sinusoidal fit may not be
appropriate. In this case, the contours are fit with
complex sinusoids and ovals that represent curved
(nonplanar) boundaries. These are further used either by
the user or the software to define the layer geometry.
WORKFLOW STEPS
The LLM workflow includes the steps described below.
1. Log Squaring. A unique log squaring algorithm
(Shetty et al., 2012) is applied to the density log to
establish boundary positions and initial values of layer
densities. The bottom-quadrant density log, computed
from the image, is used for log squaring. This log has
the advantage of small standoff effect compared to
other quadrant readings. A measured depth shift is
applied to the bottom density and the resulting square
log to account for azimuthal effects. This ensures the
density log and other nonazimuthal measurements are
on depth. Common boundaries identified from the
density image can be applied to all measurements (i.e.,
density, neutron porosity, gamma ray, and sigma)
allowing the true layer properties to be derived for each
measurement type.
2. Contour Detection and Sinusoid Fitting. The
marching squares algorithm (Lorensen and Cline, 1987;
Liu et al., 2010) is used to detect contours in the density
image. Given a single density value (also known as an
iso-value), the algorithm rapidly detects lines of isodensity in the image. When applied to a loggingwhile-drilling (LWD) density image, the algorithm
detects various types of contours (Figure 2). Three
types of contours are of special interest for detecting the
angle between the borehole and intersecting formation
layers:
a)
Fig.2. Density images showing three types of contours
that may be detected by the marching squares
algorithm. Image color indicates density value with
blue representing low density, yellow representing
medium density, and red representing high density.
Images are orientated to top of hole. The left panel
shows type 1 open contours. The middle panel shows
type 2 closed contours. Type 3 closed contours are
shown in the right panel.
In the case of a simple sinusoid, the relative dip
between the borehole and a layer boundary is computed
from
Type 1: An open contour that extends across
the entire image. These occur when the
borehole completely crosses a layer boundary
(Figure 2, left panel).
DIPn= tan-1(An/ (Rn+DOI))
b) Type 2: A closed contour that forms a loop at
the center of the image. This feature is
commonly called a “bull’s eye”. These occur
when the boundary is present only at the
bottom of the borehole (Figure 2, middle
panel).
c)
(1)
where
DIPn is the relative dip
An is the sinusoid amplitude
Rn is the radius of the borehole
DOI is the depth of the density image
Type 3: A closed contour that exists only on
the sides of the image. This feature is
commonly called a “reverse bull’s eye”.
These features occur when the boundary is
detected only at the top of the borehole (Figure
2, right panel).
The phase of the sinusoid with respect to the center of
the image (when oriented to top of the hole) determines
the relative azimuth. The relative dip and azimuth,
combined with the inclination and azimuth of the
borehole, define the true dip and azimuth of the
formation layers.
Smooth contours are then obtained by fitting the raw
contours with sinusoids or ovals. At low relative dip,
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3. Clustering and Consolidation of Sinusoids. The
sinusoids obtained in step 2 provide unbiased dip
information, such as phase, relative dip, and location. If
an abrupt change in formation density occurs across a
boundary, the resulting steep density gradient will
provide multiple iso-density countours and hence
multiple sinusoids over a short interval. The sinusoids
are typically very similar. For clarity, a single sinusoid
and dip is preferred to represent each significant
boundary. The boundaries are derived from the log
squaring, as explained in step 1. A window is created
around each of the identified boundaries. Within each
window, sinusoids that have similar characteristics are
grouped together. Following the clustering, a single
representative sinusoid is obtained for each cluster
during an averaging process. Statistical indicators such
as standard deviation are computed for each
consolidated sinusoid to ensure that outliers were not
included during the consolidation of clustered sinusoids
and to provide an indication of the uncertainty on the
computed dip and azimuth. Figure 3 illustrates the
process of sinusoid clustering and consolidation.
strictly horizontal. This definition is more appropriate
for delineating the images based on the presence or
absence of sequential sinusoidal features. High-angle
sections are further classified as high-angle-up or highangle-down (i.e., drilling up or down section)
depending on the phase of the sinusoids. The trajectory
segmentation can be further edited or refined by the
user.
Fig.4 Trajectory segmentation based on analysis of the
sinusoids.
4. Construction of Initial LLM. The layer boundary
positions and dips along with the layer density and PEF
properties are combined by the software to define the
initial polygon-based LLM within a few feet around the
wellbore. The automatically defined layer model is
fully editable to allow further adjustments of the layer
properties and dips if necessary. The density or PEF
image can be projected onto the well trajectory. This
serves as a guide to refine the layer model along
horizontal sections.
5. Fast-Forward Modeling of Density and PEF
Responses. A fast-forward model of density and
photoelectric log responses is computed as a function of
the well trajectory and the defined model (layer
geometry and property). The fast-forward model uses a
combination of first- and second-order flux sensitivity
functions for Compton scattering and photoelectric
absorption (Zhou et al., 2009), defined for the 3D grid
shown in Figure 5.
Fig.3 Property contours, sinusoid fitting, and clustering.
A marching squares algorithm identifies contours of the
same density value on the density image (left panel).
Sinusoids are then fitted to the contours (middle panel).
Clustering and averaging of the sinusoids is performed
to produce a single sinusoid for each boundary along
with statistics on dip and azimuth uncertainty (right
panel).
Based on analysis of the resulting sinusoids, the
wellbore trajectory is segmented into high-angle and
horizontal sections (Figure 4). Sections along the
wellbore where sinusoids are extracted are classified as
high angle. Sections where no simple sinusoids are
available (they are either oval or complex) are classified
as horizontal. The term “horizontal section” means
sections where the trajectory is approximately bed
parallel, even though the well inclination may not be
Fig.5 Computational grid for fast-forward modeling:
3D grid (left) and radial grid (right).
The sensitivity functions are derived from Monte Carlo
N-particle (MCNP) simulations. The 3D sensitivities
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for the first-order Compton-scattering response of longspacing and short-spacing density detectors are shown
in Figures 6 and 7, respectively. The Comptonscattering response dominates the density measurement
and provides a visual approximation of the model’s
overall spatial sensitivity. The fast-forward model is
approximately one million times faster than using
MCNP; it takes milliseconds to compute the responses
for a single log-point and sector. The forward model
has the same accuracy as the density measurement
(+/–0.015 g/cm3).
layer properties. This is also the case for very thin
layers that are below the resolution of the density
measurement or layers that are not crossed by the well
trajectory.
Figure 8 shows an example where a small adjustment to
the automatically determined LLM is required by the
user. In the upper panel, track 1 presents the recorded
density image; the modeled image is shown in track 2.
The difference between the modeled and measured
images or “misfit” is presented in track 3 for the
automatically determined model. The measured image
shows the presence of a nonplanar, dense formation
above the wellbore, likely a calcite-cemented nodule
(appears red in the image). This is also highlighted on
the misfit image in green.
To correctly model the nodule identified on the image
and also seen on the image strip projected onto the
wellbore (lowermost part of Figure 8), the user can use
graphical tools to add the nodule and adjust its
properties. The updated model and modeled image are
shown in tracks 1 and 2 in the lower part of the figure.
Note how the misfit image (track 3) shows an excellent
match between measured and modeled images.
Fig. 6 The first-order Compton sensitivity map for the
long-spacing density detector
The user decides if features such as this nodule are
petrophysically significant at the level of the reservoir.
If so, then the response can be modeled and justified, if
not then the nodule can be ignored and essentially
removed from petrophysical computations (Valdisturlo
et al., 2013).
Fig.7 The first-order Compton sensitivity map for the
short-spacing density detector.
6. Model Refinement. The simulated and measured logs
and images for density and PEF are compared through
an MCU workflow in conjunction with or separate from
the inversion processing. The LLM geometry or
properties are adjusted so that agreement is achieved
between the measured and simulated log responses. In
many cases, especially in planar beds thicker than 6-in.,
little to no manual adjustment is required. Localized
nonplanar features such as nodules, faults, or large
fractures may need user adjustment of the geometry and
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CASE STUDIES
Example 1
Figure 1-1 (Appendix 1) shows an interval from a
high-angle well drilled offshore West Africa at 67
inclination through a thinly bedded formation
consisting of sand and clay laminations. Using the
LLM methodology, the detailed formation model
(layer geometry along with density and PEF
properties) was automatically defined, and no
manual update or inversion was required from the
user. The forward modeled and measured density
images are compared in the upper log display
panel. The density modeling misfit (track 3)
highlights that the forward model sufficiently
matches the measured logs. Track 5 shows that the
modeled bottom density and the measured bottom
density are in good agreement.
The layer boundaries derived from the density
measurement were then used as a formation
geometry model for gamma ray (GR), resistivity,
and neutron porosity. These layer properties were
manually adjusted to obtain a good match between
the modeled and measured logs. Square property
logs are presented in Figure 1-2, alongside the
measured image for reference. The good
agreement between the measured and modeled
logs is shown in tracks, 5, 6, and 7. The layer
geometry defined from the density images
removes the uncertainty associated with the
formation model geometry, and only layer
properties require adjustment.
The resulting square layer property logs are free
from geometry effects and can be used directly for
petrophysical evaluations instead of the measured
logs. This improves the accuracy of the
petrophysical models for HaHz wells. Figure 1-3
shows how using layer properties, instead of the
measured logs, changes the interpretation of the
data in this HaHz well. The boundaries between
thin layers are clearly defined, and the properties
are constant in each layer. The model clearly
delineates the clean sands and clay interbeds. The
pay sand intervals computed using a density cutoff
of 2.3 g/cm3 are presented using the measured logs
and the layer properties. The net pay is a better
reflection of the geology when computed using the
layer properties.
Fig.8. Manual adjustment of the initial LLM to model a
calcite-cemented nodule. In the top and bottom panels,
tracks 1 and 2 show the measured and modeled density
images, respectively, both oriented to the top of the
hole. Track 3 represents the density modeling misfit
image. The white color indicates good agreement
between measured and modeled image, and the red
color highlights mismatch. The LLM is presented in the
two lower panels. The black solid line is the trajectory
centerline, and the dashed lines show the borehole
walls. The color code indicates layer density. The lower
panel shows the measured density image projected onto
the well trajectory.
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Example 2
Example 3
Figure 1-4 shows data from a horizontal well
drilled in a clastic reservoir in the North Sea. The
pay zone is located between a shale layer (above)
and a low-resistivity sand (below) as depicted by
the GR and resistivity logs shown in tracks 1 and
2. Significant separation is observed between the
various propagation resistivity curves in the pay
zone. Analysis of the logs using MCU
methodology requires introduction of resistivity
anisotropy so that the modeled and measured
resistivity logs agree with each other (track 2). The
estimated vertical and horizontal resistivities of the
pay zone are 50 and 15 ohm.m, respectively.
Figure 1-5 shows data from a 50-m (measured
depth) interval drilled offshore West Africa in a
vertically heterogeneous shaly sand formation that
contains interbedded sands and clays. Analysis of
the GR and PEF logs highlights the difference
between top- and bottom-quadrant readings (tracks
1 and 2). There is considerable separation
observed among the four readings of the quadrant
density logs (track 3). The caliper log (track 1)
shows minimal hole enlargement (maximum
0.4 in.) over the intervals where log separations
are observed, thus eliminating the possibility of
borehole effect causing the difference in readings.
Analysis of the density image (track 3) using the
LLM technique shows the presence of many thin
layers in the pay zone. The bottom panel of Figure
1-4 shows the LLM, which is automatically
defined within a 2-ft radius around the trajectory
centerline. As observed, the well cuts through
many thin layers with various densities
(highlighted by the color change and also the
density square log in track 4). The observed
changes in density are likely to be related to
changes in porosity, grain size, or hydrocarbon
saturation, which also result in differing resistivity
values between layers. Thin layers of differing
resistivity cause anisotropy effects on resistivity
measurements, as observed on the propagation
resistivity logs acquired in this interval. Track 5
presents the density image modeled along the well
trajectory over the defined LLM.
Using the density image (track 5), the detailed
layer geometry and properties are defined using
the LLM methodology (shown in the middle panel
of Figure 1-5). The projection of the density image
onto the well trajectory (bottom panel of Figure 15) was used as a guide for model construction.
Comparison of the measured image projected on
the trajectory (lower panel) and the user-defined
geometry (middle panel) provides a powerful
quality check on the validity of the formation
model geometry used in explaining the measured
log responses through forward modeling.
The model shows the wellbore was drilled at the
boundary between two different beds. The upper
bed has higher clay content. There is also a porous
layer below, partially penetrated by the wellbore.
The well also cuts through a high-angle dense
feature (interpreted as a fault or fracture with
associated deformation) at around 80 m true
horizontal length (THL), which explains the
sudden change in log readings. The corresponding
forward-modeled density image is presented in
track 6. The low values of the density modeling
misfit array (track 7) indicate that the model is a
good representation of the subsurface.
This global layer model for the deep-reading
resistivity measurements and the LLM for the
high-resolution shallow density measurements are
consistent and complementary. In this case, the
layer density information from the LLM could be
used to derive the volumetric proportion of the
layer types in the thinly bedded interval. In
conjunction with the shoulder bed corrected
vertical and horizontal resistivities derived from
analysis of the propagation resistivities in the
global layer model, a consistent anisotropic
formation evaluation can be performed.
This example highlights how the LLM approach
can be used to gain improved understanding of log
readings through detailed characterization of the
structure and layer properties proximal to the
wellbore. This is valuable for subsequent
petrophysical evaluations.
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CONCLUSION
REFERENCES
Log measurements recorded in HaHz wells are
commonly affected by geometric effects,
complicating their direct use in petrophysical
workflows. Previous work to address the most
common geometric effect on HaHz logs used a
laterally extensive layer model particularly suited
to the analysis of deep-reading resistivity logs.
This paper presents a complementary workflow
for the definition of layering in the vicinity of the
wellbore, particularly suited to the interpretation of
the shallow-reading nuclear measurements.
Griffiths R., Morriss C., Ito K., Rasmus J., and Maggs
D., 2012, Formation evaluation in high angle and
horizontal wells—A new and practical workflow, Paper
FF, Transactions, SPWLA 53rd Annual Logging
Symposium, Cartagena, Columbia, 16–20 June.
Lorensen, W.E. and Cline, H.E., 1987, Marching cubes:
A high resolution 3D surface construction algorithm,
SIGGRAPH '87, Proceedings of the 14th Annual
Conference on Computer Graphics and Interactive
Techniques, 163–169.
Liu, Z., Boonen, P., Munsell, S., and Lagrera, J.,
2010, Improved borehole image dip calculation in
irregularly shaped and curved boreholes in highangle and horizontal wells, Paper 2010-81666,
Transactions, SPWLA 51st Annual Logging
Symposium, Perth, Australia, 19–23 June.
The workflow allows semiautomatic creation of
the local layer geometry and layer properties based
on LWD density images. When layers are planar
and thicker than 6 in., little to no manual updating
or inversion is usually required. In horizontal and
nonplanar sections, the density image is projected
onto the wellbore allowing the user to rapidly
create and populate the layer properties using
simple graphical tools. With the geometry and
position of the formation layers clearly defined by the
borehole image, the layer properties become the main
unknown. The layer properties can often be extracted
directly from the borehole image or are easily refined
and justified using MCU or inversion methods.
Passey Q.R., Yin H., Rendeiro C.M., and Fitz
D.E., 2005, Overview of high-angle and horizontal
well formation evaluation: issues, learnings, and
future directions, Paper A, Transactions, SPWLA
46th Annual Logging Symposium, New Orleans,
Louisiana, USA, 26–29 June.
Shetty S., Omeragic D., Habashy T., Miles J.,
Rasmus J., Griffiths R., Morriss C., 2012, 3D
parametric inversion for interpretation of loggingwhile-drilling density images in high-angle and
horizontal wells, Paper EEE, Transactions,
SPWLA 53rd Annual Logging Symposium,
Cartagena, Columbia, 16–20 June.
The workflow continues to be developed. Future
plans include expanding the method to use
additional tool measurements, as well as the
development of inversions that fully automate the
building of the formation model.
Layer properties determined by the presented
method are free from the geometry effects
commonly observed on HaHz well logs and more
accurately reflect the true formation properties,
allowing for more accurate formation evaluation.
Valdisturlo A., Mele M., Maggs D., Lattuada S.,
and Griffiths R., 2013, Improved petrophysical
analysis in horizontal wells: From log modeling
through formation evaluation to reducing model
uncertainty—A case study, Paper SPE-164881MS, presented at the EAGE Annual Conference &
Exhibition incorporating SPE Europec, London,
UK, 10–13 June.
ACKNOWLEDGEMENTS
The authors wish to thank the operating companies
for release of the logging data from the two wells
and the many Schlumberger staff who have
contributed to the development of the product
described in this paper.
Zhou, T., Miles, J., Case, C., Chiaramonte, J., and
Ellis, D., 2009, A second-order fast-forward model
for a gamma-gamma density logging tool, Paper
SPE-124193-MS, presented at the SPE Annual
Technical Conference and Exhibition, New
Orleans, Louisiana, USA, 4–7 October.
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
1985, he has been involved with developments for
various software projects, ranging from acoustic
data acquisition, well placement, and reservoir
simulation to formation evaluation in HAHZ
wells. In recent years, he has led interpretation
engineering projects leveraging multidisciplinary
principles in geoscience and engineering on
industry-leading
formation
evaluation
and
reservoir characterization software platforms. He
holds a BS degree in mechanical engineering from
the University of Tokyo in Japan and an MS
degree in industrial mechanical engineering from
the University of Tokyo.
ABOUT THE AUTHORS
Mohammad Taghi Salehi is a petrophysics
product analyst responsible for the developments
of some Techlog* wellbore software platform
modules including the Techlog 3D Petrophysics.
He started his oil industry career as a
Schlumberger LWD field engineer in 2004. While
being permanently based in Iran, he delivered
LWD services in several locations of the Middle
East and Asia. After finishing the field assignment
in 2007, Mohammad worked as well placement
engineer and then as LWD Domain Champion
responsible for the technical sales and support of
logging-while-drilling technologies. He joined the
Techlog
platform
development
team
in
Montpellier, France in January 2014.
David Maggs received his Masters in mechanical
engineering from the University of Southampton,
England, in 1988. He has almost 27 years of
experience in the oil industry, all with
Schlumberger. He started as a wireline field
engineer in South America for 5 years followed by
a further 3 years in the Southern North Sea. David
then moved to support LWD and well placement
in the Gulf of Mexico. He was then transferred to
the Data & Consulting Services segment as
operations manager for Continental Europe, and
later Latin America South. In 2004, David
returned to Drilling & Measurements (D&M) as an
LWD Domain Champion, completing assignments
in Malaysia, Venezuela, and Saudi Arabia, where
he was responsible for the technical sales and
support of a wide range of LWD technologies. In
2011, David moved to the Schlumberger
Information
Services
(SIS)
segment
as
Petrophysics Product Champion, based in
Montpellier, France. Working on the Techlog
software platform he was responsible for the
development and implementation of several
petrophysics modules, in particular the Techlog
3D Petrophysics module for high-angle and
horizontal well interpretation. He has been the
Petrophysics Domain Head for D&M since
summer 2014. David is a member of SPWLA and
SPE.
Joan Abadie is the lead developer for the Techlog
3D Petrophysics module. He has worked for 15
years as a software engineer and manager in
different
domains,
including
the
telecommunications, land planning, virtual reality,
and geographic information system industries.
Shahzad Asif is the software architect for
modeling and inversion of LWD measurements in
Techlog 3D Petrophysics and works with the
formation evaluation domain experts based in
Sugar Land, Texas. He received his Masters in
computer engineering from University of Houston
and has worked in the software engineering field
for 18 years. He has worked at various roles in the
engineering of commercial applications for
fracturing, cementing, and coiled-tubing data
acquisition systems and formation evaluation
systems for wireline and LWD measurements.
Prior to joining Schlumberger, he worked as
technical lead and project manager for software
engineering teams in the oil & gas, healthcare,
telecommunications, and defense verticals.
Koji Ito is currently a software project architect
responsible for designing interpretation workflows
and applications for formation evaluation in highangle and horizontal wells at Houston Formation
Evaluation Center of Schlumberger located in
Sugar Land, Texas. Since joining the company in
*
Chris Morriss received a B.Sc. (Hons) degree in
civil engineering from the University of Aston,
England, in 1975. Since joining Schlumberger in
1978, he has been involved with the interpretation
development of numerous wireline and LWD
measurements. He is currently involved with the
interpretation of LWD resistivity and nuclear logs
in high-angle and horizontal wells.
Mark of Schlumberger
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
Luca Ortenzi is an LWD and Well Placement
Principal
for
Schlumberger
Drilling
and
Measurements. He started his career in 1993 as a
wireline field engineer. Since then, he has covered
various positions as technical expert in Europe,
Asia, and Africa, and in the Clamart (France) and
Sugar Land (US) engineering centers. Since 2011,
he has been in charge of technical support of the
operations in the Sub-Saharian region. He obtained
his Master’s degrees in geology from University of
Perugia (Italy) in 1992, and in petroleum
engineering from Heriot-Watt University (UK) in
2012. He is a member of SPWLA and SPE.
John C. Rasmus is an Advisor-Reservoir
Characterization in the Schlumberger LWD
product line based in Sugar Land, Texas. Current
duties include LWD interpretation field and client
support, resistivity and nuclear interpretation
support, and special projects. He has held various
interpretation positions developing new and
innovative interpretation techniques for secondary
porosity in carbonates, geosteering of horizontal
wells, geopressure quantification in undercompacted shales, downhole motor optimization,
and HAHZ well petrophysics. John holds a BS
degree in mechanical engineering from Iowa State
University in Ames, USA; and an MS degree in
petroleum engineering from the University of
Houston. John is a member of SPWLA, SPE, and
AAPG and is a registered professional petroleum
engineer in Texas as well as a registered
professional geoscientist.
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Roger Griffiths is Measurements Advisor for
D&M, providing guidance for LWD tool and
answer product development. He has held various
field, management, engineering, and technical
positions supporting wireline and LWD services
since joining Schlumberger in 1987. Roger has
published 15 technical papers and two books (Well
Placement Fundamentals and the User’s Guide for
the Schlumberger multifunction logging-whiledrilling service) and holds 14 patents.
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
APPENDIX 1 CASE STUDY EXAMPLES
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Fig.1-1. Automatic formation model construction in a high-angle well. Tracks 1 and 2 show the measured and
modeled density images, respectively. The density modeling misfit presented in track 3 indicates how accurately the
formation model is able to explain the measured responses. Track 4 shows the square density log (black) and the
bottom density log shifted to account for azimuthal effects (red). The measured (red) and modeled (blue) bottom
density logs are compared in track 5. The LLM defined within a radius of 2 ft around the trajectory centerline is
presented in the curtain section shown in the lower panel. Layer density is indicated by layer color in both the
curtain section and image tracks.
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
Fig.1-2. Application of density-image-derived layer geometry to other formation properties. Track 1 displays
the density square log and the measured density image. Using the layer positions and dips identified from the
density image, square logs were automatically extracted for GR (track 2), vertical and horizontal resistivities
(track 3), and neutron porosity (track 4). These were then verified by forward-modeling the log responses and
comparing the modeled response to the measured logs as shown in tracks 5, 6, and 7. The close match indicates
that the formation model provides a good explanation for the log responses and is therefore representative of
the subsurface geometry and property distribution.
Fig.1-3. The net pay intervals calculated for both the measured logs (left panel) and modeled logs (right
panel) using a density cutoff of 2.3 g/cm3. The LLM model clearly delineates the thin sand/clay laminations.
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
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Fig. 1-4. Local layer modeling for shallow high-resolution density images complements the global layer modeling
for deep resistivity readings. Tracks 1 and 2 display the measured and square GR and resistivity logs from the global
layer modeling approach. Track 2 shows the good agreement between forward-modeled and corresponding
measured resistivity logs. Track 3 shows the measured density image used as an input to the LLM workflow. The
resulting density square log and modeled density image are shown in tracks 4 and 5, respectively. The bottom panel
displays the LLM that was defined for a 2-ft radius around the trajectory centerline. The LLM is superimposed on
the global layer model used for the resistivity measurement interpretation.
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SPWLA 56th Annual Logging Symposium, July 18-22, 2015
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Fig.1-5. Representation of complex geometries. The bottom-quadrant GR and PEF logs (red) are displayed
together with corresponding upper-quadrant readings (green) in tracks 1 and 2. Track 1 also displays the
caliper log. Track 3 displays the density logs from the four quadrants. The quadrant logs show variation
between top and bottom of the borehole. An abrupt change in the logs occurs at approximately 80 m THL.
This is apparent from the abrupt change seen on the resistivity and density image logs presented in tracks 4
and 5, respectively. The density image projection onto the well trajectory is presented in the bottom panel.
The image projection was used to manually define the layer geometry and properties presented in the
curtain section panel (middle panel). Layer color indicates the layer density. The modeled density image
and corresponding misfit are presented in tracks 6 and 7. Track 8 highlights the good match between the
measured (red) and modeled (blue) bottom density logs. The square density log (black) is also shown in
track 8 for reference.
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