Recent advances and trends in subsea technologies and
seafloor properties characterization
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Djikpesse, Hugues, Jorge Fiori F. Sobreira, Andy Hill, Kelly
Wrobel, Ralph Stephen, Michael Fehler, Kerry Campbell, Olivier
Carrière, and Shuki Ronen. “Recent Advances and Trends in
Subsea Technologies and Seafloor Properties Characterization.”
The Leading Edge 32, no. 10 (October 2013): 1214–1220. ©
2013 by The Society of Exploration Geophysicists.
As Published
http://dx.doi.org/10.1190/tle32101214.1
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Society of Exploration Geophysicists
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Final published version
Accessed
Wed May 25 19:07:31 EDT 2016
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http://hdl.handle.net/1721.1/88175
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SPECIAL
G e o h SECTION:
a z a r dG
s e o h a z a r d s
5HFHQWDGYDQFHVDQGWUHQGVLQVXEVHDWHFKQRORJLHVDQGVHDÁRRU
properties characterization
HUGUES DJIKPESSE, Schlumberger
JORGE FIORI F. SOBREIRA, Petrobras
ANDY HILL, BP
KELLY WROBEL, ExxonMobil
RALPH STEPHEN, Woods Hole Oceanographic Institution
MICHAEL FEHLER, Massachusetts Institute of Technology
KERRY CAMPBELL, Fugro GeoConsulting
OLIVIER CARRIÈRE, Scripps Institution of Oceanography
SHUKI RONEN, Seabed Geosolutions
F
ollowing the 2012 SEG Annual Meeting in Las Vegas, the
SEG Research Committee sponsored a post-convention
research workshop on subsea technologies, in general, and
on seafloor characterization in particular. The goal of the
workshop was to share experiences in acquisition, processing
and applications of geotechnical and geophysical measurements
for seafloor property characterization. This includes:
• To help geophysicists in better understanding geotechnical
seafloor measurements, e.g., when and how they are collected as well as their actual field applications;
• To help geotechnical specialists in better understanding
geophysical seafloor measurements, how they are derived,
and their importance for accurate seismic waveform modeling and inversion;
• To discuss technology and application trends, and how the
geophysical community can participate in the fast-growing
market for subsea operations in the oil and gas industry.
The workshop was co-organized by Hugues Djikpesse
(Schlumberger-Doll Research) and Kelly Wrobel (ExxonMobil). It started with a keynote address by Ralph Stephen
(Woods Hole Oceanographic Institution) who updated the
audience of mostly geophysicists on what geotechnical seafloor measurements are and how they are typically collected.
It was succeeded by a second keynote address by Andy Hill
(BP) who presented successful applications, as well as pitfalls, in using geophysical measurements to prevent marine
geohazards issues in offshore operations in a variety of settings, shelf and deep water, from around the world. A major
issue outlined by Hill was the interpretation and extrapolation of geotechnical engineering properties from geophysical
measurements. Indeed, the two families of measurements are
often collected at different scales and for analyzing the rock
at different strain regimes (Figure 1).
Technology trends in automated underwater vehicles
(AUV) and subsea nodes were discussed by Kerry Campbell
(Fugro) and Shuki Ronen (Seabed Geosolutions), respectively. Two promising applications were identified. Fiori Sobreira
(Petrobras) presented an innovative way of relating information contained in multicomponent seismic data, elastic
parameters, and geotechnical measurements. Such relations
can help regionally predict the geotechnical behavior of the
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marine shallow section, while also being useful for full-waveform seismic modeling. The second application is the use of
Scholte waves generated at seafloor irregularities to characterize seafloor properties. It resulted from the presentations by
Michael Fehler (MIT) and Olivier Carrière (Scripps Institution of Oceanography).
To motivate a larger audience of geotechnical survey
specialists and geophysicists about subsea technologies and
seafloor properties characterization, we present a series of examples illustrating different aspects of the workshop.
Geotechnical measurements and seafloor properties
One way to estimate seabed properties in a wide range of
applications including petroleum exploration, seafloor installations, offshore drilling, seafloor mine countermeasures,
and bottom interacting ocean acoustics is the use of seismoacoustic techniques.
Although we like to think of the seafloor as a simple, flat
interface between water and sediment there are many complicating factors. The seafloor is often rough and heterogeneous
over scale lengths from centimeters to hundreds of meters.
Figure 1. Schematic illustration showing geophysical and geotechnical
soil-testing approaches corresponding to various strain levels and
various problem areas. μ is the shear modulus, μ0 is the shear modulus
or stiffness at very small strain, l is the density, VS is the shear-velocity,
and the cone resistance (qC ) is indicative of the soil strength. It is the
ratio between the total force (Q C ) acting on the cone, used during the
geotechnical standard (SPT) or cone-penetration test (CPT), and the
projected area (AC ) of the cone. From Ghose and Goudswaard, 2004.
G e o h a z a r d s
The rigidity of the seafloor supports shear and interface waves
(e.g., Scholte waves) in addition to compressional waves. The
sediments themselves can have significant porosity and in
some cases can be filled with free gas. Significant intrinsic
and effective attenuation and anisotropy have been observed.
For example, Ewing et al. (1992) reported the results of
careful interface wave experiments carried out off Martha’s
Vineyard and New Jersey in 10–20 m water depths: “We
have demonstrated that high-resolution longitudinal and
transverse mode shear data can be obtained in shallow water
Figure 2. The LOAPEX underwater acoustic projector. (Courtesy of
Alliant Techsystem (from Mercer et al., 2009)).
regions. ... Anisotropy and lateral heterogeneity are important features of sedimentary structure and processes; ... Shear
data are more difficult to obtain than compressional, and horizontally polarized shear is more difficult to obtain than vertically polarized shear.” Shear-wave speeds as slow as 25–50 m/s
were observed in the upper 2 m of the seafloor.
Dispersion, frequency-dependent sound speed, is a necessary consequence of intrinsic attenuation and this has been
observed in porous sediments and modeled over the frequency band from 100 Hz to 1 MHz (Jackson and Richardson, 2007). Vertical seismic profiling (VSP) and offset VSP
provide an important link between the fine-scale (centimeters
to meters) well logging and core measurements in boreholes
Figure 3. Conceptual diagram illustrating the Hugin 3000
autonomous underwater vehicle (AUV), which can simultaneously
collect 200 kHz multibeam echo sounder, 120 or 410 kHz side-scan
sonar, and 2–12 kHz chirp sub-bottom profiler seismic data. Inset is a
photo of an AUV about to be launched. (Courtesy of Fugro.)
Figure 4. (a) Chair-cut view of sub-bottom profiler 3D micro volume showing small faults and a gas/hydrate chimney. Approximate overall
dimensions of volume are: 1280 × 1640 ft, with approximately 130 ft of effective penetration below seafloor. (b) Sub-bottom profiler inline 102
showing details of a gas/hydrate chimney and small faults A, B, and C. Inset is similarity variance time slice 1.381 s showing location of inline.
Note that the vertical offset on fault A is less than 1 ft and is barely perceptible on the inline, but the areal extent of fault A is nonetheless clearly
shown on the time slice. (Courtesy of Fugro.)
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Figure 5. An example of a pseudo-geotechnical section (pseudo-Su, in Pa units) in the Marlim-Voador Field that was derived using the inverted
shear modulus from seismic data. A few horizons and Su logs from geotechnical boreholes are superimposed on the figure. Vertical axis is depth,
ranging from about 300 to 1300 m and the horizontal length of the section is about 5 km. (Courtesy of Petrobras.)
and the large scale (hundreds of meters to kilometers) surface
seismic surveys (Little and Stephen, 1985). There are vertical
and horizontal heterogeneities at all scales, and the effective
medium properties depend on the wavelength of the sound
used for excitation.
Controlled acoustic sources, such as land vibroseis systems, enable well controlled sweeps, typically over 30 s. Pulselike time series are recovered by replica correlation. Although
not as loud as airguns, improved temporal and spatial resolution (27 ms in time and 40 m in range) can be achieved
(Figure 2) (Mercer et al., 2009).
Autonomous underwater vehicles (AUV) are important
survey tools for deep-water seafloor characterization (Figure 3). AUVs are unmanned and untethered, follow preprogrammed survey patterns, and carry geophysical survey
tools that provide detailed images of the seafloor and shallow
sediments. One of the AUV survey tools is a high-frequency
(2–12 kHz chirp) seismic sub-bottom profiler, which has ±1
ft vertical resolution and an effective penetration below the
seafloor that typically ranges between a few tens of feet to as
much as 375 ft, depending on local subsurface conditions.
Recent advances in AUV survey technology have made
possible applications such as 3D micro surveys of small and
precisely targeted sites (for example, the area under the tension-leg platform). Such surveys typically use line spacing of
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approximately 6.5 ft or 13 ft (2 m or 4 m). These are referred
to as AUV3Dm surveys. The result is a sub-bottom profiler 3D
seismic volume (Figure 4a) that shows foundation-zone conditions in unprecedented detail, which is critical for facilitiesengineering applications. The AUV3Dm volumes can be analyzed using conventional 3D seismic interpretation software to
provide a near-outcrop-scale characterization of stratigraphy,
geologic features, and any anomalies (Figure 4b), without the
data gaps inherent to 2D seismic data (Campbell et al., 2013).
Geotechnical characterization offshore Brazil from seismic data elastic inversion
In a pioneer study, Sobreira et al., (2010, 2011) proposed an
innovative approach to extract geotechnical information indirectly (however regionally and cost-effectively) from both
conventional and multicomponent seismic data available
from two areas offshore of Brazil (Albacora and MarlimVoador oil fields, in the Campos Basin). After an initial,
qualitative comparison, it was observed that elastic properties and moduli derived from seismic inversion (especially for
the case of shear and Young’s modulus) correlated well with
direct geotechnical information such as the geotechnical
parameters undrained shear strength (Su) and net cone resistance (qnet). While the latter is the total cone resistance of
the cone-penetration test (CPT) compensated for the vertical
G e o h a z a r d s
stress related to the total overburden pressure, the former is
essentially qnet scaled by a soil-dependent factor, specific
to each area. These results lead to a more quantitative step,
based on the crossplot between inverted elastic moduli and
geotechnical parameters. Empirical equations (valid rather
locally) enabled the quantitative extraction of geotechnical
parameters, i.e., predicted or pseudo-Su and predicted or
pseudo-qnet anywhere within the seismic volume (Figure 5).
Comparisons between pseudo-geotechnical logs thus derived
and geotechnical logs obtained directly from in-situ measurements (geotechnical boreholes) have provided promising
results (Figure 6).
From this investigation, it may be concluded that:
• Real and predicted geotechnical parameters agree well, in
the sense that major natural geotechnical contrasts can be
reproduced in spite of the intrinsic differences in strain
regimes and resolution to which seismic and geotechnical
measurements are related to (Figure 1).
• Elastic moduli related to shear wave (such as shear and
Young’s modulus) as well as the pseudo-geotechnical parameters extracted from them show a better correlation
with direct geotechnical parameters than those related
to compressional wave only (such as bulk modulus). The
former are fundamentally related to the frame’s properties
such as rigidity.
• Incorporation of elastic seismic processes (namely simultaneous and/or joint inversion) and of elastic seismic info
(namely multicomponent data, including PS-wave) provided special robustness to the results.
This is a major breakthrough. It allows the transformation
from geotechnical measurements into elastic moduli. The
latter can be used for full-waveform modeling of seismic
data acquired over unconsolidated
seabed sediments.
Scholte wave modeling for seafloor properties estimation
Scholte waves are guided by the
water-sediment interface at the
seafloor. They exhibit their highest
amplitude at the seafloor while their
amplitude decays exponentially upward into the water and downward
into the sub-bottom. The excitation of Scholte waves by sources
within the ocean is influenced by
the elastic properties of the sediments near the seafloor. If sources
are located more than several tens
of meters above the water-sediment
interface there is minimal excitation
of Scholte waves, although scattering at the seafloor also excites them.
When Scholte waves are generated,
the amplitude and spectrum of the
excited waves are most influenced by the shear-wave velocity
of the sediments.
Numerical modeling of Scholte waves in regions where
there are strong variations in seafloor topography can be
problematic. If grid-based methods like finite differences are
used, numerical noise may appear in the simulations that has
the character of energy that is trapped at certain locations.
Boundary element methods do a much better job at simulating Scholte-wave propagation in regions where there is seafloor topography. Figure 7 shows results of two simulations
for a 2D model whose seafloor topography is taken from
the SEAM Phase I deep-water model (Fehler, 2012). Figure
7a shows the structure that consists of a water layer over a
solid sedimentary layer. Figure 7b shows the pressure on the
seafloor as simulated using a 2D finite-difference algorithm.
Figure 7c shows the seafloor pressure simulated using a 2D
boundary element approach. Various phases are labeled in the
lower panel. Both the finite-difference and boundary element
approaches show direct and head waves from the source located near the surface of the model. Scholte waves, which propagate slowly, are also seen, and it is clear that Scholte waves are
generated by scattering at locations where changes in seafloor
topography take place. However, the finite-difference simulations have stationary noise that also seems to originate at
locations where there are strong changes in seafloor topography. The Scholte wave velocity is less than the S-wave velocity
of the sediments. It is interesting to note that once Scholte
waves are excited, the dispersion characteristics of the waves
can be measured and used to infer the near-seafloor S-wave
velocity profile.
Seabed interferometry
Seismic interferometry can be used to extract sub-bottom
reflected waves from multicomponent data collected with
Figure 6. Comparison between pseudo-qnet values predicted from both inverted bulk and shear
moduli and in-situ (filtered) qnet measurements for the Albacora Field. Please note that the former
captures natural contrasts that the latter cannot. (Courtesy of Petrobras.)
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Figure 7. (a) SEAM topography profile; (b) shot gather from finitedifference modeling normalized to the maximum amplitude; (c) shot
gather by BEM normalized to the maximum amplitude.
ocean bottom seismometers (OBS). Seismic interferometry
is an emerging data processing technology to extract empirical Green’s functions (EGF) from crosscorrelations of signals
received at two OBS locations, averaged over multiple shots
or long time recording (Curtis et al., 2006). It is relevant
in most seismic sensor deployments. This seismic interferometry concept not only is attractive for active surveying,
but also provides an excellent framework for passive applications, i.e., when sensors record ambient noise (earthquakes,
microseisms, shipping noise, fracturing, etc.). The crosscorrelation creates a virtual source everywhere an actual sensor is found, resulting in a model-independent redatuming.
This redatuming process actually removes the need of precise
knowledge of source geometry or shooting times.
The nature of imaged waves essentially depends on the
sensors: PP reflections emerge from hydrophone or vertical
geophone crosscorrelations, while the introduction of one
or two horizontal geophones in the crosscorrelations enables
the imaging of PS or SS reflections. Surface waves, such as
Scholte, can be retrieved as well. Here we focus on its application with ocean bottom seismometers (OBS). 4C-OBS
combine a hydrophone and a three-component geophone.
They can be released from the surface or deployed by a remotely operated vehicle and record data for long periods of
time. Figure 8 shows an example of PP interferometric processing, using active survey data acquired on an OBS array
deployed at the Woolsey Mound, Northern Gulf of Mexico,
in April 2011. A complete description of the interferometric
processing applied to this data set is available in Carrière and
Gerstoft (2013). Upgoing/downgoing wavefield separation
is performed by combining collocated pressure and particle
velocity sensors and provides an efficient wavelet signature
deconvolution approach (Backus et al., 2006). The survey geometry is illustrated in Figure 8a, while an example of a shot
recording is shown in Figure 8b. Interferometric processing
was restricted to closely spaced sensors (OBS 01–OBS 11).
Crosscorrelations of segmented signals were averaged to give
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Figure 8. Interferometric processing of active survey OBS data
in the Gulf of Mexico. (a) A gas-injection gun was towed along a
5.5-km track near the surface (stars), above the OBS (squares), in
900-m water depth. OBS 12, 13, 14, and 15 were 500 m apart. (b)
Normalized pressure (red) and vertical particle velocity (blue) signals
on the OBS, shot number 51. The source is near vertical incidence
for OBS 13 [red star in (a)]. The first water multiple is distinguished
1 s after the direct arrival. (c) Stacked empirical Green’s functions
extracted from pressure signal crosscorrelations, involving downgoing
wavelet deconvolution, common midpoint stacking and normal
moveout (NMO) correction. (d) Conventional upgoing/downgoing
seismic processing, involving downgoing wavelet deconvolution, NMO
correction and common depth-point conversion. For reference, several
reflection events (dotted) and principal interfaces (arrows), identified
with conventional upgoing/downgoing seismic processing, are shown.
the EGF for each OBS pair. Stacking EGF at the common
midpoint results in a horizontal resolution of half the receiver
spacing (12.5 m, Figure 8c). Conventional upgoing/downgoing seismic processing, as developed in Backus et al. (2006),
was applied to the same data set for comparison (Figure 8d).
The three main interfaces are identified, with consistent polarity and relative amplitudes, although interferences from
a distant seismic survey perturb the processing. The second
interface (0.2 s) has a negative polarity, which might indicate
the base of the hydrate stability zone. The third reflector corresponds most likely to the top of the salt dome identified in
deep seismic data acquired on a larger zone at the same location. No relevant reflections were detected below the third
reflector.
G e o h a z a r d s
Most commercial OBS surveys are deployed on the seabed by ROVs (Figure 9). The nodes record reflections of air
gun sources near the surface. Data are imaged (Ronen et al.,
2012) and provide sharp graphical descriptions of shallow gas
(Figure 10).
Conclusion
This workshop discussed some of the geophysical survey and
geotechnical tools, technologies, and methods being used to
characterize seafloor and shallow soil conditions and geohazards for offshore production infrastructure siting and foundation design.
Looking into the future, transforming 3D micro seismic volumes into Earth volumes from which one would be
able to directly extract materials properties is an important
and achievable goal. Although researchers have made some
progress toward this goal, and efforts are increasing on several
fronts, the remaining challenge is significant. Provided that
advanced Scholte-wave signals detection, modeling and inversion methods are made available, the use of Scholte waves
to estimate seafloor properties seems promising. Another area
of possible interest is the analysis of drill bit samples to infer
elastic properties of unconsolidated sediments beneath the
seafloor. More innovation is expected with autonomous marine vehicles, such as the Wave Glider (Figure 11) that can
operate offshore for up to a year, for autonomous data collection and subsea surveillance.
Global subsea capital expenditures are forecast to more than
double in size over the next five years. Most of this growth is subsea production-oriented.The fast-growing number of deep-water
Figure 9. Remotely operated vehicle retrieving ocean-bottom nodes in
the Forties Field. (Courtesy of Apache.)
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Figure 10. Inline and crossline through a 3D image of shallow
hazards under a platform in the Forties Field. (Courtesy of Apache.)
wells and the expected emergence of large production of methane hydrates can only increase interest in exploration subsea
technologies and seafloor properties characterization.
References
Backus, M. M., P. E. Murray, B. A. Hardage, and R. J. Graebner,
2006, High-resolution multicomponent seismic imaging of deepwater gas-hydrate systems: The Leading Edge, 25, no. 5, 578–596,
http://dx.doi.org/10.1190/1.2202662.
Campbell, K. J., S. Smith, and C. Pastor, 2013, AUV3Dm: Detailed
characterization of shallow soil strata and geohazards using AUV
sub-bottom profiler 3D micro volumes: Offshore Technology
Conference paper OTC 23950.
Carrière, O. and P. Gerstoft, 2013, Deep-water subsurface imaging
using OBS interferometry: Geophysics, 78, no. 2, Q15–Q24,
http://dx.doi.org/10.1190/geo2012-0241.1.
Curtis, A., P. Gerstoft, H. Sato, R. Snieder, and K. Wapenaar, 2006,
Seismic interferometry - turning noise into signal: The Leading
Edge, 25, no. 9, 1082–1092, http://dx.doi.org/10.1190/1.2349814.
Ewing, J., J. A. Carter, G. H. Sutton, and N. Barstow, 1992, Shallow
water sediment properties derived from high-frequency shear and
interface waves: Journal of Geophysical Research, 97, B4, 4739–
4762, http://dx.doi.org/10.1029/92JB00180.
Fehler, M., 2012, SEAM update: elastic simulations: The Leading
Edge, 30, no. 1, 24–25, http://dx.doi.org/10.1190/1.3679323.
Ghose, R. and J. Goudswaard, 2004, Integrating S-wave seismicreflection data and cone-penetration-test data using a multiangle
multiscale approach: Geophysics, 69, no. 2, 440–459, http://
dx.doi.org/10.1190/1.1707064.
Jackson, D. R. and M. D. Richardson, 2007, High-frequency seafloor
acoustics, in R. R. Goodman, H. P. Bucker, and I. Dyer (eds.),
Underwater Acoustics, Springer.
Little, S. A. and R. A. Stephen, 1985, Costa Rica Rift borehole seismic
1220
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October 2013
Figure 11. A Wave Glider, the world’s first wave-powered selfpropelled autonomous marine vehicle. It can collect measurements
offshore for up to a year without requiring a crew, fuel, or a dedicated
support vessel during its mission. (Courtesy of Liquid Robotics Oil and
Gas, a joint venture between Liquid Robotics and Schlumberger.)
experiment, Deep Sea Drilling Project Hole 504B, Leg 92: Initial
Reports. DSDP, 83, 517–528.
Mercer, J. A., J. A. Colosi, B. M. Howe, M. A. Dzieciuch, R. Stephen,
and P. F. Worcester, 2009, LOAPEX: The long-range ocean acoustic propagation experiment: IEEE Journal of Oceanic Engineering,
34, no. 1, 1–11, http://dx.doi.org/10.1109/JOE.2008.2010656.
Ronen, S., A. Rokkan, R. Bouraly, G. Valsvik, L. Larson, E. Ostensvig, J. Paillet, A. Dynia, A. Maltosz, S. Brown, S. Drummie, J.
Holden, K. Koster, D. Monk, and M. Swanson, 2012, Imaging
shallow gas drilling hazards under three Forties oil field platforms
using ocean bottom nodes: The Leading Edge, 31, no. 4, 465–469,
http://dx.doi.org/10.1190/tle31040465.1.
Sobreira, J. F. F., M. Lipski, A. A. Carvalho, and E. Marquez, 2010,
Geotechnical characterization based on seismic data approaches
applied in Campos Basin, Southeastern Brazilian Margin: The
Leading Edge, 7, 936–940.
Sobreira, J. F. F., M. Lipski, A. A. Carvalho, and F. A. V. Artola, 2011,
Extraction of geotechnical attributes from seismic data: towards a
quantitative approach: Presented at 12th International Congress of
the Brazilian Geophysical Society.
Acknowledgments: The authors thank P. Gerstoft (Scripps Institution of Oceanography), M. Kucera, S. Smith, C. Pastor (Fugro),
Y. Zheng, X. Fang (MIT), and H. Berg (Schlumberger) for their
invaluable contributions. They also thank BP, ExxonMobil, Fugro,
MIT, Scripps Institution of Oceanography, Seabed Geosolutions,
Schlumberger, and Woods Hole Oceanographic Institution for allowing the publication of this work. Special thanks to C. E. Krohn
of the SEG Research Committee.
Corresponding author: HDjikpesse@slb.com