This document summarizes seismic reservoir characterization services offered by Arcis Corporation, including computing coherence and curvature volumes, AVO/LMR analysis, seismic facies classification, and multi-attribute analysis to characterize reservoirs. Arcis uses advanced techniques like thin-bed reflectivity inversion and rock physics analysis to enhance mapping and differentiate lithology and fluids. The services aim to improve seismic interpretation and understanding of reservoir geometry and physical property distribution.

1. RESERVOIR
SERVICES
ASSISTING OUR CLIENTS IN THE DEFINITION OF RESERVOIR GEOMETRY, THE DISTRIBUTION OF PHYSICAL
PROPERTY CHARACTERISTICS WITHIN A RESERVOIR, AND IN THE CORRELATION OF ALL AVAILABLE DETAILS,
ARCIS OFFERS A STATE-OF-THE-ART SUITE OF SEISMIC RESERVOIR CHARACTERIZATION SERVICES.
Curvature volume computation
Coherence volume computation
AVO/LMR analysis
Azimuthal AVO
Thin-bed reflectivity inversion
Impedance inversion
Spectral decomposition
Seismic facies classification
Multi-attribute analysis
Rock physics analysis

2. 2 Km
Coherence attributes
Curvature attributes
Coherence computes a measure of similarity between adjacent traces. Identification and mapping of channel edges, reefs,
faults and fracture systems in 3D volumes becomes so much easier with the coherence volume. Structural and stratigraphic
interpretation of seismic data get facilitated by using a coherence volume. Arcis offers coherence computation services
based on Energy Ratio algorithm (a modified eigen-decomposition of covariant matrices approach). This algorithm has
generated results better than those from the standard semblance as well as eigen-decomposition based algorithms.
Curvature attributes measure the degree of bending of seismic reflections along a surface or in a volume and help to
improve interpretation and structural understanding of 3D seismic data volumes. Different curvature attributes identify
subtle faults, fractures, and other features better than other attribute applications. Arcis offers volume computation of
curvature, together with their spectral estimates.
Reservoir Services
Coherence using
semblance without
dip-steering option
Horizon slice
through the
coherence
volume
Coherence using
Energy Ratio Horizon slice
through the
most-positive
curvature
volume
Horizon slice
through the
most-negative
curvature volume
Color stack of
coherence,
most-positive
curvature, and
most-negative
curvature
LOW
HIGH
NEG
POS
NEG
POS
LOW
HIGH
NEG
POS
HIGHLOW HIGHLOW
Coherence using
semblance
Coherence
(Energy Ratio)
after PC-filtering
HIGHLOW
HIGHLOW
2 Km2 Km
2 Km
Data courtesy: Arcis Corporation, Calgary
www.arcis.com

3. Multi-spectral estimates of curvatures
Fracture analysis using curvature attributes
Interpretation of lineaments corresponding to subtle faults and trends can be carried out on the most-positive
or most-negative curvature horizon slices as shown. The interpreted lineaments are then converted into a
rose diagram which can be compared with a similar rose diagram available from image logs.
Reservoir Services
NEG
POS
Time slices 1160 ms
Most-positive curvature
Rose Diagram
90270
0
180
Coherence
Most-positive
(long-wave)
Most-positive
(short-wave)
Interpretation on curvature displays
www.arcis.com

4. Visualization of attributes
Thin-bed reflectivity
Arcis offers thin-bed reflectivity inversion using a spectral inversion technique that produces ultra-high
resolution seismic data that enhances mapping. Thin-bed reflectivity removes the deleterious effects of
the seismic wavelet that cause degraded resolution. Thin-bed reflectivity is performed without using well
data, requires no a priori model, no interpreted horizons and no assumed reflectivity spectra.
Reservoir Services
A stratal fault skeleton from the most-positive curvature attribute
being correlated with seismic data volume in the increasing inline
direction as indicated with the white arrow
Strat-cube from
the most-positive
curvature attribute
co-rendered with
coherence seen here
in a 3D chair view
Strat-cube from
the most-negative
curvature attribute
co-rendered with
coherence seen here
in a 3D chair view
Time slice
comparison before
(left) and after
(right) frequency
enhancement
Seismic
Input seismic Reflectivity convolved with bandpass wavelet
Reflectivity
www.arcis.com

5. Correction for spurious phase via estimation
of non-minimum phase wavelet Rock physics analysis
For a better understanding of lithology and fluid differentiation, Arcis emphasizes the importance of rock
physics analysis and integrating that with the AVO/LMR processing flow. Arcis uses an informative and
straight forward approach to exhibit the elastic and petrophysical properties of reservoir rocks.
Deconvolution with minimum phase wavelet assumption usually leaves the data with spurious phase. The phase
correction required to remove the spurious phase is done by a simple parameterization of the underlying mixed phase
wavelet, which involves estimation of an all-pass operator coefficients via cumulant matching technique. The approach
involves setting up a cost function with unknown shape and using a simulated annealing algorithm for optimization.
Reservoir Services
Original data Phase corrected data
Original data. The inserted curve is the
P-velocity log
Phase corrected data.
The inserted curve is the P-velocity log
Vp against Vs
P-impedance against S-impedance
Lambda-Rho against Mu-Rho
Vp against Vp/Vs
Vp against Poisson’s ratio
Lambda-Rho against Lambda/Mu
www.arcis.com

6. AVO/LMR analysis
Seismic/VSP modeling
Arcis has the capability and innovativeness to accomplish AVO/LMR analysis including AVO friendly processing,
QC and interpretation.
Reservoir Services
Overlay of angle information on offset gathers
Lambda-Rho
Mu-Rho
Input gathers Reconstructed gathers Difference
NEG
POS
LOW HIGH
3D Cross-plotting of
Lambda-Rho,
Mu-Rho and Fluid
Stack. Clusters
associated with gas
anomalies (yellow)
separated out
Velocity model
Shot record
Snapshots of the propagating wavefront
m/sec
Velocity model derived from well log
Shot record velocity model with
20 m segmentation interval
Shot record velocity model with
5 m segmentation interval
www.arcis.com

7. Geobody tracking and volume estimation
Spectral decomposition
Seismic facies classification
Seismic waveforms in the broad zone of interest are categorized and related to different depositional facies. Arcis offers
both unsupervised (statistical) as well as neural network (deterministic) methods for arriving at meaningful and convincing
facies analysis.
Spectral decomposition allows utilization of the discrete frequency components of the seismic bandwidth to interpret
and understand the subtle details of subsurface stratigraphy. Besides the traditional Fourier transform, Arcis
offers the CWT (Continuous Watvelet Transform), Matching Pursuit Decomposition (MPD) and Exponential Pursuit
Decomposition (EPD) methods for transformation of data to frequency domain. The latter two techniques offer more
accurate analysis.
Geobodies corresponding to porous sandstone accumulations, or the channel-fill within drainage system,
or a salt body can be tracked by selecting an amplitude or attribute value as a ‘seed.’ Some of these tracked
geobodies help in the assessment of hydrocarbon reserves in place in a given prospect.
Reservoir Services
Phase corrected data
High porosity zone shown tracked by using the ‘seed’ shown in red
Horizon slice at 70 Hz showing channel features with their enhanced definition
www.arcis.com

8. Impedance inversion
Several different methodologies are available for performing acoustic inversion.
• Recursive
• Model-based
• Sparse-spike
• Simultaneous inversion
Multi-attribute analysis
Arcis offers neural network or cubic-b spline based multi-attribute analysis. Such an analysis is beneficial if the 3D volume
has a reasonably good well control and the wells are representative of the geology in a lateral sense. Application of
Probabilistic Neural Network (PNN) to estimate the P-impedance volume provides more detailed information compared to
the conventional model-based P-impedance inversion. As seen in the examples below, the PNN application for acoustic
impedance shows better correlation with the impedance log than the conventional P-impedance inversion.
P-impedance obtained from post-stack inversion. The inserted black curve is the P-impedance log
P-impedance estimated with probabilistic neural network. The inserted black curve is the P-impedance log
Impedance
(m/s² g/cc)
Arcis offers the energy industry a unique, integrated complement
of seismic services including participation “spec” surveys, data
processing, data marketing, geotechnical services and access
to an extensive data library. Arcis offers one of the most current
seismic data libraries of predominantly 3D for the Western
Canadian Sedimentary Basin, including North East British
Columbia. We are committed to exceptional customer service,
superior data quality, innovation, and integrity; while maintaining a
focus on health, safety, and environmental stewardship.
We think different. We think seismic.
www.arcis.com Reservoir Services

9. 2600, 111 - 5TH
Avenue SW Calgary, Alberta, Canada T2P 3Y6 403.781.1700 or 888.269.6840 www.arcis.com