1. Jun 1, 2011
PSDM Yields Accurate Pore Pressure Prediction
In a case study from the Caspian Sea, accurate and detailed velocity analysis from a
prestack depth migration was adapted to calculate accurate pressure prediction in an
undrilled area.
Hazim H. Al-Dabagh, Lukoil Overseas UK; and Norbert Van De Coevering, CGGVeritas
Drilling in frontier areas provides many challenges to drilling engineers in designing
and planning for a well at a desired location. Traditionally, engineers have relied on
information from an offset well to design and plan a new well. Unfortunately,
demand for exploration in frontier areas and deep targets must contend with the lack
of offset well data.
Modern seismic data and seismic data processing can offer high-quality velocity data
that provide pore pressure prediction in such areas. The data also offer useful
information to fill in the gaps between wells in areas of sparse well coverage. Surface
seismic velocities from compressional (P) wave energy are influenced by the
compaction of clastic sediments. Pore pressure affects the compaction; therefore,
changes in formation velocity can be calibrated to changes in pore pressure,
assuming there is no change in lithology. Velocities derived from surface seismic data
can provide an indirect way to predict pressure in the subsurface before drilling.
Two scenarios applied this concept. In Area 1, five wells with logs of varying quality
were available to calibrate the velocity from 3-D prestack depth migration; however,
pressure prediction profiles were needed closer to these wells. Area 2, approximately
36 miles (60 km) northwest of Area 1, has no available well data.
It is common to calibrate the seismic velocities to velocities from sonic data recorded
in the well bore. The calibration process can establish many parameters needed for
the appropriate pore pressure calculation methodology.

2. The seismic velocity pressure prediction compares to the log predictions in the old wells in Area 1. (Images courtesy of
Petronas)
Pressure prediction from well data
The initial step checks the behavior of the sonic log velocities encountering the pore
pressure in existing wells. Pressure predicted from the sonic velocities should
indicate that the seismic velocities would have similar behavior and predict the best
possible model. All of the available well data of interest in Area 1 were collected for
the initial pore pressure calculation. The first step was to establish the vertical stress
or overburden gradient (OBG) from integrating the available density logs. Several
wells were used to establish a composite density function because of missing sections
or bad-quality density logs. The vertical stress for the first 1,640 ft (500 m) below the
mudline was calculated using the Miller empirical formula. Pore pressure prediction
for the initial input wells was calculated using calibrated pressure models employing
the velocity-effective stress relation.
The matrix stress ratio is determined from the leak-off test measurements for
calculating the fracture gradient. These values normally are estimated from the

3. pressure-versus-time graph. This method makes calibration more difficult because of
the calculation procedure. In this project, the sonic log data were used as an
alternative.
Depth migration velocities
The final velocity model from prestack time processing was used as the initial model-
building process for the tomography inversion of the prestack depth migration. The
velocity model output of the last iteration from the tomography inversion was used
as input to the residual move-out analysis.
A high-density simultaneous velocity analysis technique was used to pick a high-
density VRMS and η (effective eta) field. This helps flatten the events to a higher
incidence angle than the output from a second-order stacking velocity correction.
This automated velocity analysis was performed using a grid of eight inlines by eight
crosslines at 300 ft by 300 ft (100 m by 100 m). An angle mute of 50 degrees was
applied on the input gathers.
Automatic velocity-picking processes generally are noisy because of high-frequency
picking, so geostatistical filtering is required for pore pressure prediction. To remove
the noise in the velocity cube, velocity volume is decomposed into two components.
The low-frequency component or trend cube should preserve the structural
component of the velocity, and a residual cube should preserve the fine-scale
variations and noise. Such decomposition has the following advantages:
 The residuals are stationary, which is suitable for the condition to perform
factorial kriging; and
 The structural component of the velocity is preserved.
Factorial kriging was performed on the residuals to remove the noise. The final
filtered velocity cube is the sum of the filtered residuals and the trend cube.

4. Seismic velocity pressure prediction is compared to the logs in the new wells in Area 2.
Interval velocity computation
The seismic velocity field (RMS) has to be converted to interval velocity to be used
for pore pressure prediction. Normally, the regularly sampled velocity field (in time)
is converted using the Dix approximation. This technique is known to produce some
instability; to preserve as much information as possible, a small sample rate has to be
used.
This new final interval velocity cube in time domain was considered as an attribute
that preserves the velocity variations due to pressure influence. It was converted to
depth using the same average velocity field from prestack depth migration that was
used for the depth conversion of the newly imaged seismic data. In this way, the
consistency of the depth information remains the same for the velocity attribute and
the seismic interpretation.

5. Velocity calibration, pressure prediction
New seismic interval velocities were cross-plotted with the velocities from the sonic
logs of the wells in Area 1. Velocities from the wells with stability problems deviate
dramatically from the regression line. The regression was improved by removing the
problem wells. To improve the correlation further, the residuals between the seismic
and well velocities were computed and kriged in 3-D. These residuals then were
added to the initial seismic velocity field. This method was tested using a kriging
radius of three and six miles (5 and 10 km). One well was dropped from the kriging
process to serve as a blind well test to give more confidence in the process. The good
correlation with the blind test was the basis for applying the kriging with a radius of
three miles.
This diagram shows the workflow to generate velocity attribute and accurate pore pressure prediction.
A calibrated Gardner relation was derived based on the well data to estimate the
density from the P-wave velocity. This new calibrated interval velocity cube was
converted to density after a first-order regression was applied in the correlation
between the wells’ density logs and the derived density. The resulting equation then

6. was applied to the “seismic” density cube to perform a residual calibration.
Subsequently, the OBG was calculated. The correlation of the OBG from seismic and
OBG from calibrated seismic density at the wells was examined. This final residual
calibration improved the fit to the OBG from the original well densities as well as the
OBG from the composite density function of the stability problem wells. This volume
was used in the final pressure volume calculations.
The normal compaction curve (NCT) was computed on the same criteria observed
from the well logs. The interval velocity cube was scanned to calibrate the NCT
curves on a defined grid and interpolated to cover the whole final volume grid. This
“scanning” process consists of extracting the velocity trace at the selected locations
and defining the best lambda parameter for the Miller equation.
Good seismic data quality combined with good data processing and understanding of
the geological model can achieve accurate pore pressure prediction in frontier areas.
A robust and powerful workflow for velocity model building for pore pressure
computation predicts pore pressure in such areas.
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