American Oil and Gas Reporter - November 2016 - 49

SpecialReport: Oil & Gas Computing
tracted the minimum amplitude. Figure
5 shows weak (absolute) amplitudes in
blue and strong amplitudes in red.
The reservoir is characterized by apparent stronger amplitude than the background events, indicating hydrocarbon
accumulation. Before FWI (left), the reservoir horizon contained numerous footprint-like weak amplitude stripes (yellow
arrows), but the stripes were reduced after
FWI (right). The two dashed lines indicate
the gas/oil and oil/water contacts.
Finally, well log-based prestack P-impedance inversion was performed to evaluate the benefits of FWI. The well does
not have sonic log data, so P-velocity
data were derived using resistivity, gamma
ray and adjacent well log data.
Figure 6 shows the inverted P-impedance
with and without FWI overlaid with the
well log. The inversion result showed two
layers of the reservoir that both matched
the well log reasonably well. The P-impedance after FWI had more continuous
reservoir layers (yellow arrows) and the
structures appeared simpler (blue arrows).
A high-resolution velocity model was
generated in the East Breaks area field
using high-frequency FWI. The velocity
anomalies within a shallow MTC were
recovered, thereby improving the continuity and structural definition beneath
the MTC. A good tomography-derived
starting model allowed reflection data to
be used in FWI, increasing the update
depth to include a deep reservoir. Slower
velocities at the reservoir associated with
hydrocarbon accumulation were inverted
correctly and matched check shot data
closely at the well location.
Moreover, nonhyperbolic move-out on
migrated gathers below the shallow anomalies was reduced, resulting in a more
stable AVO response and a more geologically consistent amplitude distribution
within the reservoir. Finally, prestack Pimpedance inversion at the reservoir became more continuous and easier to interpret for quantitative analysis.

FIGURE 5
Reservoir Amplitude Before (Left) and After (Right) FWI
-1,000
-900
-800
-700
-600
-500
-400
-300

a)

FIGURE 6
Prestack Inverted P-Impedance Before (Left) and After (Right) FWI
impedance
(((ft/s)*(g/cc)

(ms)

(ms)

3,400

3,400

18,100
18,063
18,025
17,988
17,950
17,913
17,875
17,838
17,800
17,763

3,500

3,500

17,725
17,688
17,650
17,613
17,575
17,538

3,600

3,600

17,500
17,463
17,425
17,388
17,350
17,313
17,275
17,238
17,200

These imaging and amplitude improvements were obtained with minimal
preprocessing, including denoising, designaturing, debubbling and deghosting.
More detailed preprocessing, such as
footprint removal and demultiple algorithms, may further improve the images
and reduce the anomalous amplitude
stripes. In addition, Q factor compensation
(related to the petrophysical parameters
of rocks, including porosity and permeability) also is needed to compensate for
absorption effects and to achieve an even
higher image resolution.
r

Caelus Calls Alaska Well A 'World Class' Discovery
ANCHORAGE, AK.-Caelus Energy
Alaska reports its Smith Bay well is a
"world-class" oil discovery that could be
one of the largest finds in Alaska. It estimates the field, in shallow waters 50 miles
southeast of Barrow, Ak., could produce
200,000 barrels a day of light oil.
If correct, the company says that production level would make the Smith Bay
field more prolific than ConocoPhillips'
Alpine unit, which reached peak produc-

-200

b)

tion of 139,000 bbl/d in 2007.
According to Caelus Energy, the production estimates are its internal numbers
and don't indicate analysis by a thirdparty engineering firm. It adds that flow
tests were not done because of seasonal
time constraints, but "extensive sidewall
coring and subsequent lab analyses confirm
the presence of reservoir-quality sandstones
containing light oil."
❒

BING BAI is imaging supervisor
at CGG, focused on seismic data processing. Bai joined CGG in 2005 and
holds an M.S. in physics from Texas
A&M University.
GUO YU is a seismic imager at
CGG, focused on streamer marine
data processing, including tape reading, data quality control, multiple attenuation, noise removal, regularization
and interpolation data, time/depth migration, and horizon picking to create
sophisticated subsurface images. Yu
joined CGG in 2013 and holds a
Ph.D. in meteorology from Penn State
University.
SUNXI WANG is a seismic imager
at CGG, specializing in marine seismic data processing. He joined CGG
in 2013 after receiving a B.S. in materials science and engineering from
the University of Science & Technology of China, and a Ph.D. in
chemical engineering from Wayne
State University
NOVEMBER 2016 49



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