Challenges in Oil and Gas Discovery
big impact on the challenges in Oil and Gas
y. It improves t
he quality and speed of business decisions. It
prestack domain to the
nd allows the processors full visualization of pre
The Headwave tools work both on a regional scale as well as on reservoir
that the data has been thoroughly vetted and
that decisions made on this
It drives increased ROI
Challenges in Oil and Gas discovery are not new. The pressures and temperatures
experienced by drillers and producers have caused many headaches. With the increasing
Oil & Gas
Industry is facing the equivalent pressures
in finding seismic tools which work at depth
maging is struggling with
locating and viewing potential resource plays at seven to ten mile depths in the earth,
ing those images from seismic sources which are up to two miles from the
sea floor. All of this combines to make the signals sought fainter than ever before. This,
in turn, requires massive increases in processing power. Where will it be acquired?
d of a budget is that going to take?
by Headwave in
Deep Views with
Headwave has developed the first
software package. While prestack
has been long used for
been used has been arduous. With multi
gathers, interpreters have struggled to get a clear overview of what was going on. Thirty
fold gathers were barely manageable on a 24” d
isplay, but 50
200 fold gathers were
really out of the question. Headwave has changed that with the ability to volumetrically
view the prestack data. No matter how large the prestack dataset
we’re talking > 1
the user can access it fr
om the desktop.
compression delivers multi
terabyte datasets to the desktop and makes using the data
Interactively work with a terabyte dataset on a standard PC workstation. See
vast swaths of data in a manner never bef
Figure 1. Two views of the same inline and associated prestack data. On the left a gather
is displayed and a prestack probe is also shown for the inline data. On the right, the probe
has had the shader modified to make all but the trough
peaks transparent (blue = trough)
in the probe. The nature of the far offset booming is clear, and from the gather data, a
revisiting of the muting of the prestack data might be in order.
Volumetric visualization means the data i
s seen as a volume in space.
Moreover, the data
is seen in context. Each gather is associated with the poststack trace
generated from the
gather. This makes it easy to relate individual prestack traces to the poststack,
additionally gather flatness (NMO qu
seen. It’s possible to scroll though ten MCS blocks in a single afternoon on a
inline basis viewing each gather.
Users no longer need to avoid looking at a 1000 sq Km
region in prestack. The abi
lity to load such a dataset on a single workstation and do QC is
This means that full processing QC is possible in a single day.
To do so,
the poststack data is loaded into the workspace
and linked to
the associated prestack data.
Now the pr
estack data may be displayed
in many ways:
gather at a time,
or as a prestack
Even a HyperCursor can be used to see the prestack info at a
special location. In this way,
the viewer can walk th
ough the prestack volume
ll traces at a given offset, or each gather in a given volume.
Having assured oneself and the group that the data processing results are as expected,
now the power of volumetric visualization can be fully utilized.
The user can easily
adjust the transpare
ncy of the prestack data volume. In doing so,
indications can be
seen. By inserting a probe into the volume, the specific regions of interest can be viewed
in more detail.
Closer inspection can lead to selections of gathers for custom stacking.
iddle, and far stacking can be done on the fly.
Stacking can also be done on a
trace range basis.
Prestack Horizon Picking
Picking a horizon in poststack data is commonplace, it’s not so common to pick a horizon
in prestack data, and even less common to us
e a poststack horizon to seed the prestack
he picked prestack horizon can be used to map various attributes. Attributes such
as horizon curvature,
time delta (the difference from the time of the event at
the time of the event at
), trace count (how many traces of the gather contri
to the poststack trace), G
can be derived or loaded and
mapped onto the prestack horizon.
he user can step th
ough the entire poststack volume
viewing the prestack
ons associated with that inline.
At any point the attribute being
displayed may be changed and the stepping continued.
This is an example of how the
tools available in Headwave can be applied.
Contrast this to the previous methods of
viewing prestack gathe
rs on a large dataset and it’s obvious that this is a huge leap
forward for ease of use. Prior methods required either a system with memory larger than
the prestack dataset, or required a laborious loading of 2D panes of gathers, up to 10 on
the screen if
you were lucky. The major problem: the more prestack
, the less
, and forget trying to load a 100 fold gather, let alone all such
gathers related to an inline or crossline. This is not a problem
veys aren’t a problem, nor are the new circular surveys with 200 fold
Figure 2. Here are four 36 fold gathers and a prestack probe displayed together with the
poststack inline, two time horizons are displayed as well. This display is next to
mpossible in a 2D display mode.
Suppose that the prestack
ow can we apply AvO
With the computational power now available
corrections can be
on the desktop. Maybe additional filtering would help, or maybe it
needs a minor velocity tweak
a test of
or improve AvO calculations
. This is all possible.
Stacking can be done
either on the full
volume, or on a selected probe. Since the probe is a subset of the entire
stacking can be
quickly. Sometimes the prestack data won’t have
a mute applied or the applied mute
e mute parameters
can be te
They can be designed on
the prestack data
and the stacked result
As previously discussed, partial stacks are
possible, and quickly
After all the
QC, and with carefully applied local correcti
the AvO parameters
on the prestack horizons
of reservoirs and leads.
Accelerating 4D Visualization
What’s good for 3D is even better for 4D.
Full use of
prestack 4D data lets users easily
changes in a horizon for sure, but even more, changes in volumes.
Volumetric visualization gives “visual pop” to reservoir changes
response with time lapse seismic. Multiple datasets can easily be merged for
comparisons, delta calculat
ions, and more.
When serious money is spent to follow changes in the reservoir, then a serious
visualization package should give full money’s worth. If all there is to
major change in the reservoir is a single thin horizon,
the real deta
s there any other data
is there another
change that’s there? With Headwave you can
see, in detail
the full 4D results
also with all its prestack variation
. Blending of datasets
is easy, and with transparency adjustments
n the reservoir are
boundaries are clear, and more importantly,
the volume is there. If completion is
through multiple layers, then each of those layers should be clearly visible. Make sure
that the stacking that’s done optimizes the s
ignal to noise ratio, play what if, with
stacking and filtering. Drive information examination until the current state is clear and
To that exten
Headwave has also developed a Prestack Plugin for Petrel
Saving the Planet and the Budget
wave has chosen to use the power of the floating point units in graphics processor
to accelerate computations. Headwave is a leader in this technology.
Both NVIDIA and
AMD/ATI now offer graphics chip derived computational engines. They are fast and
ears that not only
, but the seamless availability of
visualization with computation provide
substantially shortened time to discovery.
The good news is that by going to this technology, the cost, whether $ per Gigaflop, or
Watts per Gigaflop,
down, way down
down by a factor of 10
30 for real
world applications in Headwave.
Headwave has developed novel technology for the Oil & Gas
ndustry based on GPU
With the advent of GPU processing on
a single graphics card or even in
interactive prestack visualization and computational tools are available today
for large, very large, and extremely large datasets. While
tools can handle
regional datasets, they are also perfect
ly suited for smaller, field
wide surveys. In both
cases, Headwave tools shorten the time to target.
Headwave’s tools provide the security to know that
data has been thoroughly
vetted and that decisions made on this data
well founded. It drives in
ROI and ROE.