Deepwater geohazard prediction using
prestack inversion of large offset P-wave data
and rock model
N. C. DUTTA, WesternGeco, Houston, Texas, U.S.
S
uccessful deepwater drilling is defined as reaching the
total well depth in the desired hole size safely (i.e., not
exceeding the fracture pressure) while controlling hydrocarbon or water influx and ensuring that casings are placed
across desired intervals. The oil and gas industry has succeeded in this mission thousands of time around the world
but drilling challenges abound, especially in deepwater.
These challenges include:
• Smaller tolerance between pore pressure and fracture
pressure causing narrow pressure margins while drilling
(What is the proper mud weight window?);
• Shallow-water flow hazards due to pressured aquifer
sands (How do you predict occurrence before spudding
a well?);
• Excessive casing programs/small hole at total depth or
unable to reach total depth (How do you design a proper
casing program i.e., depths and sizes?);
• Slow rate of penetration and incorrect use of bit types
combined with excessive bit wear (How do you minimize rig time without sacrificing safety?).
The industry has successfully mitigated many of these
hazards but more needs to be done when planning a well
to ensure that all appropriate and available technologies
are used properly and effectively. A case in point is the
losses due to shallow-water flow (SWF) hazards in deepwater. A 1999 conference revealed that, although the industry was spending about US$1.6 million per well in dealing
with problems associated with SWF, only about 20% of this
amount was for predrill prediction. The rest was for remediation after SWFs were encountered.
Seismic techniques for geohazard prediction (pore pressure and SWF) have come a long way since the pioneering work of Pennebaker (1968) and Reynolds (1970) who
used seismic stacking velocities for pressure prediction,
based on the fundamental analysis of overpressuring in
clastics basins by Hubbert and Rubey (1959). Great
advances in the last decade in 3-D seismic acquisition and
processing, especially in imaging and inversion technologies, allow geohazard prediction with more certainty and
clarity. Current investigations suggest that geohazard formations and their properties (i.e., velocity, density, and
rigidity) are amenable to prediction methodologies that use
constrained inversion of large-offset conventional P-wave
data in conjunction with rock physics models.
The key steps in pressure prediction using seismic
(Figure 1) involve:
• Assessing the cause of overpressure (compaction, for
example);
• Developing a rock model for relating rock attributes
(velocity, porosity, Poisson’s ratio, for example) to higher
than normal pore pressure or lower than normal effective or differential pressure (which is the stress acting
on the rock matrix); and
Figure 1. A typical flow chart showing pressure prediction methodology and highlighting the major steps in
the work flow.
Figure 2. Shale burial diagenesis and its effect on the
density and slowness of Gulf Coast shales. (a) shows the
smectite to illite conversion in a well from the Gulf
Coast (Freed 1982) created by the burial history simulation. (b) shows how density of the shales is altered when
plotted against slowness (inverse of velocity) as
obtained from wireline logs.
• Using a fit-for-purpose seismic velocity that resembles
rock velocity and not processing velocity as stated very
clearly by Al-Chalabi (1994).
Role of temperature on compaction. Although there are
multitudes of causes of geopressure, undercompaction or
compaction disequilibrium is thought the most common
in clastic basins. This is the most dominant mechanism in
deepwater. However, compaction has often been misrepresented in the literature. It is not just reduction of porosFEBRUARY 2002
THE LEADING EDGE
193
Figure 3. Bulk density versus slowness of shales from a
deepwater well in the Gulf of Mexico. The color codes
indicate depth.
Figure 4. Trends of Poisson’s ratio of water and gas saturated unconsolidated sands obtained from the work of
Prasad et al. (2000) at Stanford University.
ity and expulsion of pore water due to increasing overburden load. Burial history plays a key role—i.e., the effect
of temperature and time on the sediment compaction properties, especially that of clays in shales (Dutta, 1983).
Clays undergo burial metamorphism as they undergo
compaction (for example, the smectite-to-illite transformation within the mixed-layer clay systems in the Gulf of
Mexico). The phenomenon causes reordering of clay
platelets and redistribution of effective stress and strongly
depends on the time-temperature history of the sediment.
This implies that compaction is both time and temperature dependent and that the notion of rocks compacting
along a single “virgin” compaction curve (assuming that
porosity is a function of effective stress only) is invalid.
Porosity is a function of effective stress, the temperature
that it was subjected to as it continued along a burial path,
and the time that the rock spent in a certain temperature
window. The mechanism is kinetic in nature, similar to
194
THE LEADING EDGE
FEBRUARY 2002
kerogen conversion leading to oil generation. This also suggests that any approach that uses a single compaction
curve to describe geopressure may be questioned. Recently,
Lahann (1999) and others discussed the impact of smectite diagenesis on compaction profiles and compaction disequilibrium and suggested that two limiting compaction
curves—one for smectite, one for illite—bound the compaction profiles. His analyses were based on wireline log
and in-situ pressure data. My internal studies agree with
Lahann’s conclusions.
Figure 2a shows an imprint of smectite diagenesis on
x-ray diffraction of shales from a well in the Gulf Coast
(DOE 1). The x-ray measurements were made on fine grain
sediments (less than 5 micron) by Freed (1982). The burial metamorphic process is kinetic in nature—namely, both
temperature and geologic time control the process. Figure
2b shows a crossplot of petrophysical properties of shales
from the same well (wireline log measurements)—namely,
bulk density and slowness (inverse of velocity). The rocks
are Miocene age. The figure suggests that diagenesis causes
an increase in bulk density for a given transit time or slowness. Similar behavior is also noticed in a deepwater well
from the Gulf of Mexico (Figure 3). This suggests that wellknown relationships that relate velocity to bulk density but
ignore the effect of temperature (and time) are not valid
for geopressure estimation which uses velocity data to
extract porosity and then relates this porosity to effective
stress to compute pore pressure. The current investigation
uses a temperature-dependent compaction behavior.
Porosity is a function of the clay type, effective stress, time,
and temperature. Note that the direct effect of increasing
temperature on the velocity of a fluid-filled porous rock
is negligible; the effect on the porosity and the effective
stress is not.
Poisson’s ratio of geopressured rocks. Poor grain-to-grain
contacts in overpressured zones with low effective stress
affect the S-velocity more than the P-velocity. This has
been observed in the laboratory (Domenico, 1984) and
modeled (Xu and Keys, 1999). VP/VS (which is directly
related to Poisson’s ratio) is a key parameter that relates
to low effective pressure or high pore pressure, especially
under SWF conditions (pore pressure close to fracture
pressure). This has not received much consideration in
pore-pressure prediction methodologies because the traditional seismic approach for pressure prediction used
conventional P-velocity data only.
Figure 4 plots Poisson’s ratio versus effective stress
based on data from the Stanford Rock Physics laboratory
for two cases, sand filled with water and sand filled with
gas. Note the behavior of Poisson’s ratio at low effective
stress. For the water-filled case, the ratio increases with
increasing pressure (lowering effective stress). The opposite is true for the gas-filled case. This suggests a very different seismic signature for the two cases in the AVO
domain. The present investigation exploits this fact by
inverting CMP gathers using full waveform inversion that
allows extraction of VP/VS from large offset P-wave data.
Miley and Kessinger (1999) demonstrated how to use this
effect on shear velocities determined from mode-converted
wave reflections for subsalt pore-pressure prediction.
Trend-line versus effective-stress approach. It has been
common to predict pore pressure before drilling from conventional seismic stacking velocities with a normal compaction trend analysis using the well-known Eaton
approach (Eaton, 1972). Velocities that appear to be slower
propagation velocities” (Al-Chalabi,
1994). Second, these velocities lack resolution in depth and may not be very useful for drilling applications. Third, in
deepwater, sediment loading has often
been so fast that pressures in these sediments are above the hydrostatic pressure
right below the mud line (rocks are geopressured). This prevents development of
a normal compaction trend. Even when a
velocity trend is constructed (with depth
as a variable) by curve-fitting pressure
data from an analog well, these trends
often violate the fundamental bounds of
rock physics. The velocity neither
approaches the correct sediment velocity
just below mud line nor approaches a limFigure 5. High frequency velocity, bulk density, and Poisson’s ratio
iting velocity appropriate for very low
obtained from the prestack full waveform inversion of a CMP gather
porosity rocks at very high overburden
shown on the left. The uncertainties are shown in yellow.
pressure (Dutta, 1997).
Approaches based on the effective-stress concept are
always preferred to other approaches that use depth as a
variable in describing compaction behavior. Pore pressure
is defined as the difference between overburden stress and
effective stress. Effective stress affects the grain-to-grain
contacts of clastic, sedimentary rock, and consequently, the
velocities of seismic waves propagating through such rock.
The rock model has various components: relations between
porosity, lithology and velocity, clay dehydration, and
transformations relating both density and Poisson’s ratios
of the sediments to effective stresses acting on the matrix
frame. The key inputs that drive the rock model are velocities (P and S) obtained from a variety of velocity tools.
Iterative velocity analysis, calibration, and interpretation
are key and essential steps to ensure that the velocity fields
are within the realm of expected rock velocities.
Figure 6. Pore pressure in 3-D from analysis of every
CMP and every time sample of a 3-D data set covering ~
650 km2 using the generalized velocity inversion technique of Mao et al. (2000).
Figure 7. Pore pressure analysis using velocities from
tomographic and prestack depth migration analysis.
These velocities were conditioned using constraints
based on rock physics and those discussed by AlChalabi (1994).
than normal compaction velocities are indicative of overpressure, which then is quantified using an empirical equation. There are several problems with this approach. First,
conventional seismic stacking velocities are totally unsuitable for pressure prediction because they are not “rock or
Velocities from inversion of prestack gathers (traveltime
and amplitude). Velocities from seismic data are the key
input to any seismic-based pressure prediction methodology. They are derived from inversion of prestack seismic data, from either traveltime or amplitudes or some
combination of both. Although there is a myriad of seismic velocities, most are not fit for pressure analysis. The
main reason has been discussed above. Most of these velocities resemble some sort of processing or imaging velocities and not rock velocity. Further, they must be subjected
to constraints based on the basic principles of rock physics,
such as the close relationship between VP and VS and that
between VP and density for a given rock type. In a recent
paper, I discussed various seismic velocity tools that can
be used for pore-pressure evaluation and how to impose
rock physics constraints on velocity analysis. Some commonly used velocity tools that have had considerable success for pore-pressure imaging (PPI) are:
• Semblance velocity analysis (based on Dix-inversion)
• Horizon-based velocity analysis at every CMP and at
every time sample
• Tomography and prestack depth migration
• Velocities from poststack inversion
• Velocities from prestack full-waveform inversion
Most success in PPI is achieved using a fit-for-purpose
toolkit for velocity analysis. The goal is to derive velocities close to rock velocities—i.e., velocities that are related
to effective stress and pore pressure. The key steps in the
FEBRUARY 2002
THE LEADING EDGE
195
Comparison of observed and synthetic angle gathers
tions. Further, prestack full waveform
inversion provides estimates of shear
velocity in addition to a high resolution
P-velocity. The genetic algorithm (GA)
inversion methodology discussed by
Mallick (1999) is an optimization algorithm used to find a suitable P- and Svelocity earth model by minimizing the
misfit between observed angle gathers
and synthetic ones. The process is very
computer intensive; however, it is the
only method that yields density and
Poisson’s ratio from prestack data. AVO
techniques merely approximate these
variables within the confines of the linear inversion scheme. Density is used to
estimate overburden pressure, whereas
Poisson’s ratio is used for fracture pressure computation and shallow water flow
hazard identification (Dutta, 2001).
Figure 8. A comparison of the synthetic seismogram (shown on the right)
The prestack inversion begins with
created using the GA algorithm as discussed by Mallick (1999) with the
an automatic NMO-correction, yielding
observed gather shown on the left. The data are from a deepwater well in
an initial rms velocity. Next, interval
the Gulf of Mexico.
velocities are derived from rms velocities
and a background density is determined
as a function of depth from the interval
velocity field. In addition to the moveout
information, the GA inversion then uses
AVO data from the NMO-corrected
prestack gathers to determine initial density and Poisson’s ratio values. Once the
initial model is selected, GAgenerates random earth models (full waveform synthetic seismic), say at every 20 ms within
certain search bounds. It then uses standard GAsearch algorithms to find the optimum earth model—the one that generates
synthetic seismic data, which matches the
input seismic data using an exact wave
equation based on forward modeling. This
exact modeling method calculates all primary, multiple, and mode-converted
reflections. Consequently, all interference
or tuning effects due to thin layers and
transmission effects due to velocity gradients are accurately modeled in the inversion. Because GA uses a statistical
optimization process to search for the elastic model, it also generates a measure of
Figure 9. A comparison of the inverted P-velocity and Poisson’s ratio from
the uncertainties in estimation of the elasthe GA inversion with those from the actual well logs for the CMP data
tic model as an additional output.
shown in Figure 8. The uncertainties are shown in yellow.
Figure 5 shows an example of the VP,
density, and Poisson’s ratio derived from
process involve velocity analysis that includes residual
this process. Note the high frequency nature of the velocNMO correction, higher order moveout effects, analysis
ities predicted from the prestack inversion process. These
of velocity anomalies, accounting for raypath bending,
velocities are more appropriate for drilling applications
anisotropy correction, and velocity interpretation to honor
than those based on conventional velocity analysis.
the local geology. Extracting velocities from prestack fullHowever, inversion is inherently nonunique. Two key
waveform inversion is an emerging technology that is parsteps minimize problems associated with nonuniqueness—
ticularly suited for drilling applications.
using the largest feasible offset data and imposing rockphysics-based constraints on the inversion. A distinct
Prestack full-waveform inversion. Most velocity analyadvantage of this method is that it is not as severely depensis tools mentioned above provide velocities in the lowdent on the quality of the reflection data as AVO inversion
frequency regime and they may not be useful for drilling
or poststack inversion. Because the GA procedure allows
applications where mud programs and casing point locafor “hidden layers” in the inversion scheme, it can identions require more resolution. In this regard, full-waveform
tify pressure cells for which no associated reflections exist.
prestack inversions of velocities from seismic amplitudes
are very useful and especially suited for drilling applica196
THE LEADING EDGE
FEBRUARY 2002
ramping is over 2 ppg. Because of the
complexity of geology, tomographic
inversion and depth migration were
essential in this case. But, although
prestack time migration revealed the
pressure ramping, the magnitude of pressure and the depth at which point the
ramping started were wrong. A casing
program based on the prestack time
migration would have resulted in unsafe
drilling conditions.
Figures 8-11 show applications of
prestack and full waveform inversion
(GA procedure). Figure 8 shows a single
CMP gather (angle gather) at a deepwater well (water depth greater than 5000
ft) obtained from the GA inversion prior
to drilling. Agreement between the GAFigure 10. Results of pressure prediction for a well in real time using the GA derived result and the actual CMP angle
inversion technique and its comparison with the actual pressure data (RFT)
gather is good. Figure 9 compares preobtained after drilling the well. The pressure profile is appropriate for the
dicted velocity logs (P and S) to those
inversion and the velocity results shown in Figures 8 and 9, respectively.
obtained after drilling. Overall agreement
between pseudologs and actual logs is
striking. A comparison of the predicted
pressure profile using VP from the GA
inversion with those from drilling data
(Figures 10-11) indicates that predicted
pressures agree with measured values to
within 0.5 ppg. Prestack inversion
revealed pressure ramping at the deeper
depth. This was a concern prior to
drilling. The low-frequency model based
on velocity analysis did not indicate its
presence; it happened in a lithologic column devoid of reflections (shaly interval). Check shot data prior to ramping
allowed calibration of velocity data from
the inversion. Then a look-ahead GA
inversion provided correct velocities
ahead of the bit that can be used to predict pressure profile while drilling.
Planned downtime on the rig due to casFigure 11. A comparison of the predicted versus the actual pressure data in
ing provided an ideal opportunity for
the same well for which prestack full waveform results were presented in
this evaluation. The results increased the
Figures 8-10.
confidence of the drillers, provided a correct casing location, and eliminated an
Examples of pressure images from seismic inversion.
extra casing and provided a proper mud weight window
Figures 6-7 show pore-pressure images using low-frefor drilling ahead without exceeding fracture pressure.
quency velocity models discussed above. Figure 6 is an
In our example, we observed the influence of effective
example of semblance-based velocity analysis on 3-D data.
stresses on the shear modulus in-situ. Figure 12 shows this
It analyzes velocities along interpreted geologic horizons
modulus determined from log measurements as a funcfor every CMP and at every time sample. It does not use
tion of depth and shale content (gray scale). Looking at
the conventional Dix-model, but a generalized inversion
the (light colored) shales in the shallower two thirds of the
process that computes interval velocities from rms velocdata, we observe a continuous increase of the shear modity functions (Mao et al., 2000). The figure shows a presulus with depth, indicating progressive compaction.
sure cube in 3-D. The velocity analysis enables us to see
Deeper in the zone where the pressure ramp is encounthe pressure image in 3-D in significantly greater clarity
tered, the abnormally low effective stress leads to a prothan with other methods. High-pressure pockets (presnounced decrease in the shear modulus. The 15% increase
sure approximately 16 ppg) are clearly visible.
of the pore-pressure gradient in this zone leads to an
Figure 7 is an example of the use of tomography and
approximately 25% decrease in the shale shear modulus
prestack depth migration for pressure analysis. Before
over the same interval. This is remarkable and suggests
depth migration, velocity model building utilized reflecthat both shear-wave data and very large offset P-wave
tion (grid) tomography that accounts for raypath bending.
data may be very good indicators of high pore pressure.
The process used traveltime computation and several iterIt also suggests that Poisson’s ratio versus depth predications to construct velocities consistent with local geology
tion using prestack inversion could be a powerful tool to
and rock physics constraints. This figure dramatically
estimate this important attribute prior to drilling—pershows a clear transition from low to high pressure. Pressure
haps more meaningful than those obtained using conFEBRUARY 2002
THE LEADING EDGE
197
computed pressure profiles. These uncertainties result
from very important factors (velocities, rock parameters
relating velocities to effective stress, and compaction trend
analyses) that need to be considered. Third, and perhaps
most challenging, is the cultural gap between the seismic
community (the provider of the technology) and the
drilling community (the user). Much remains to be done.
As a practitioner for more than 15 years of the seismic
technologies discussed here and having applied these pressure estimations in basins throughout the world, I would
like to close this article with a very positive note. The
future is bright. The availability and ever declining cost of
high-speed computers will make it possible to invert 3-D
seismic volumes in the prestack domain, using full waveform inversion technologies, to obtain pressure images
with unprecedented detail and clarity. The evolution of this
technology will be like the advent and use of 3-D prestack
depth imaging.
Figure 12. Shear modulus versus depth for a deepwater
well in the Gulf of Mexico. The data were obtained
using shear velocity and density logs.
ventional AVO (small offset, linear inversion).
Conclusions. This work suggests that formation pore pressure could be predicted from seismic data provided care
is taken to condition the seismic velocities and ensure that
rock velocities are extracted from seismic velocities. The
analysis and the examples show that the largest feasible
offset data set should be used for PPI. A fit-for-purpose
velocity tool should also be used. Tomography and
prestack depth migration should be used when significant
imaging problems, lateral velocity gradients, and raypath
bending effects are encountered. In most cases, prestack
time migration may suffice. Further, the rock model relating velocity to effective stress must include geologic variables relevant to that basin. An example is the role of
temperature in the Clastic Tertiary Basin of the Gulf of
Mexico.
Prestack full waveform inversion yields a pore pressure versus depth profile with more vertical resolution
than possible from conventional velocity analysis. Further,
the resulting Poisson’s ratios can be used for fracture gradient and lithology prediction. High-frequency pressure
variations from inversion make it an attractive technology
for drilling applications. Nonetheless, the inherent
nonuniqueness of inversion results requires imposing rock
physics constraints.
Are there challenges ahead? Indeed. First, an understanding of rock physics is a must for a reliable prediction
of pore pressure. Unfortunately, the industry relies heavily on empirical formulations in this area. Such approaches
may be defended in a given area but do not allow knowledge transfer from one basin to the other. Second, many
practitioners routinely ignore uncertainty estimates on
198
THE LEADING EDGE
FEBRUARY 2002
Suggested reading. “Seismic velocity—A critique,” by AlChalabi (First Break, 1994). “Shale compaction and abnormal
pore pressures,” by Dutta (SEG 1983 Expanded Abstracts).
“Pressure prediction from seismic data: Implications for seal
distribution and hydrocarbon exploitation in deepwater Gulf
of Mexico,” by Dutta (Norwegian Society of Petroleum Special
Publication No. 7, edited by Moller-Pederson and Koestler,
Elsevier Press). “Graphical method predicts pressure worldwide,” by Eaton (World Oil, 1972). “Some practical aspects of
prestack waveform inversion,” by Mallick (GEOPHYSICS, 1999).
“Rock lithology and porosity determination from shear and
compressional wave velocity,” by Domenico (GEOPHYSICS,
1984). “Overpressure prediction using converted mode reflections from base of salt,” by Miley et al (SEG 1999 Expanded
Abstracts). “Study of coupled effect of pressure, frequency, and
fluid content on P- and S-velocities,” by Xu et al ( SEG 1999
Expanded Abstracts). “Impact of Smectite diagenesis on compaction equilibrium,” by Lahann (1999 Del Lago Geopressure
conference proceedings). “Clay diagenesis and abnormally
high fluid pressure,” by Freed (SEG 1982 Expanded Abstracts).
“ Spatially continuous velocity analysis and automated velocity model building,” by Mao et al (SEG 2000 Expanded
Abstracts). LE
Nader Dutta has a PhD in physics. He joined the oil industry about 26
years ago with Shell Oil. In 1986, he joined ARCO’s Technology Center
to direct the Geoseismic Interpretation Group and three years later
joined the Deepwater Group of BP in Houston. Dutta joined Baker
Hughes’ Inteq Division as senior science advisor in 1999. Dutta was
Strategic Business Development Manager and Worldwide Operations’
Manager of Lithology, Fluid and Pressure Imaging area in WesternGeco’s
Seismic Reservoir Services Division (WesternGeco is a joint venture
between Schlumberger and Baker Hughes). On 1 November 2001, he
was appointed chief geoscientist at WesternGeco with responsibility for
all technology-related activities underlying seismic reservoir services.
Dutta has worked in various aspects of seismic wave propagation,
including borehole geophysics, rock physics, basin modeling, seismic
while drilling, and pore-pressure technology.
Corresponding author: Ndutta@houston.westerngeco.slb.com