1. Attributes in Search of Hydrocarbons
Need to convert acoustic properties, to physical attributes which
are closely related to engineering properties, such as fluid
saturation and porosity.

2. From Attributes to Rock Physics
! To perform seismic property characterization it is required
to calibrate seismic data to known physical property
value
! Obtain known property values from analysis of borehole
information
! Need to integrate seismic attributes with measurements
made through wireline logging methods
! Seismic attributes have evolved from mathematical
transforms to essential elements deriving geologic and
engineering properties from seismic data.

3. Rock Property and Hydrocarbons
The key to successful hydrocarbon search is finding a rock/ fluid
property model which honors both seismic and log data.
• There can be derived analogies between seismic attributes and wireline logs.
• Seismic attributes are mathematical derived values used to measure various
seismic properties.
• Each seismic attribute responds separately to describe differences in
subsurface geology.
• Employing various electrical, acoustic, and magnetic borehole logging tools
to describe differences in subsurface geology where each tool responds
differently to various properties such as lithology and saturation.
• Marrying the seismic attributes and borehole logging values could provide us
with a valuable forecasting tool to obtain knowledge about rock properties
and potential hydrocarbon content/ distribution

4. Quantitative Seismic Interpretation
To perform this we need to;
• Detect only those attributes which are specific and important to the task in hand.
• Analyze as many attribute datasets at the same time as required to find a statistical reliable
attribute.
We could use either a CLASSIFICATION or a CAILIBRATION approach to perform this.
Classification is a statistical process combining several attribute data-sets together to yield a new
attribute, based upon the discriminating features of the individual attributes. Such statistical
methods are data-driven, and detects anomalies within seismic data. Most use one or more forms
of neural network or pattern recognition algorithms to locate and separate seismic facies based
on attribute response.
Calibration is a more rigorous process, and requires introduction of external data sets to quantify
the seismic attribute values, as the seismic values yields only relative values.
To calibrate attributes to represent qualitative rock and fluid values, seismic data needs to be
integrated with attributes derived from borehole information.

5. Obtain the required Attributes
How to detect only those attributes which are
specific and important to the task in hand?
• there is the matter of misuse of attributes. Modern
workstations provide almost limitless flexibility in scaling and
coloring displays.
• in spite of the many advantages of 3D seismic surveys,
the distortions of phase and frequency produced by
smashing together a number signals arriving from several
different directions and sometimes from anything but a
common reflecting point disagree with some of the basic
attribute assumptions.
Increasing the bandwidth of seismic signals
that would go farthest toward solving the
greatest number of technical problems facing
seismic interpreters and oil and gas hunters.
Match the seismic resolution with the well data

6. Unravel the rock and fluid properties from extended bandwidth seismic
Quantitative information regarding key properties such as lithology, porosity, clay content, and
net-to-gross, along with information regarding fluid types and saturations of potential reservoirs.
Revealing these key properties would be possible through improved low frequency content
allowing for inversion based mainly on seismic data and less dependent on a low frequency
background assumption based model.
With extended seismic low frequency content, the dependency on the well information will
become increasingly less important. Therefore the results of the seismic inversion (post and/or pre-
stack) should be more accurate away from the wells. Increased high frequencies provide optimal
vertical seismic resolution throughout the available depth range and more importantly at the
reservoir level allowing scientists to reveal as much detail as possible.

7. Bandwidth importance in estimating the absolute elastic properties
Theoretical amplitude spectra for
conventional and broad
bandwidth streamer data. The
extended bandwidth of the broad
bandwidth streamer data has a
significant effect on the low end of
the amplitude spectrum without
suffering any losses at the high end
of the spectrum.

8. Contradictions
Given what we stated in slide 5 about the potential distortions of phase and frequency in
seismic data for various reasons, any attribute generated from this seismic dataset should be
considered full of potential pitfalls and errors, which will lead to wrong decision foundation.
Any analysis of 2D or 3D seismic data attributes, even if calibrated and matched towards well
property values could lead to erroneous data predictions away from well locations.
What to do?
Enhance the seismic data seems to be the only solution. Enhance the mathematical
assumptions used in acquisition and processing of the data, both pre- and post-stack .
Result?
We cannot trust the seismic data to its full, and still we have to assume certain risks of any
lead, prospect or field quantitative seismic interpretations projects, and include these in our
risk assessment in a better way than we do today.
Live with the risks?
If we better understand the limitations of seismic data in our risk process and include this in our
reservoir risk element in the risk process, we might change priorities on what leads/ prospects
we will drill, or how we pursue development of a field in question.

9. Involve Seismic Quality/Control in all risk elements within the Risk Analysis
This requires the lead/prospect and field risk assessors to understand Seismic
Data Quality and Control and what aspects of data quality and control affects
which risk factor and to what degree.

10. Increase the success rate of E&P
wells
Reduce the risks of dry wells by finding the right match of attributes
to deduce the rock/fluid properties in seismic data
Reservoir delineation and 3D-geometry detection could be
significantly improved due to increased signal to noise ratio and
broader bandwidth. The extended bandwidth, especially at the low
frequency side of the spectrum represents a key in the lithology-fluid
prediction and seismic reservoir property prediction.
The need for a priori information for inversion data representation is
could be reduced by relying more on the data and less on a assumed
and priori made low frequency background model.

11. Author
Stig-Arne Kristoffersen is a Corporate exec with substantial corporate experience.
Stig-Arne provide preemptive support in German or English, with basic skill set in
Russian.
Kristoffersen focus on Knowledge Based Information processes and systems within
oil and gas industry, contract drafting, asset negotiations within real estate and
energy sectors. Stig Arne has a broad experience in all aspects of Geo-science.
Direct experience with energy business, technical consulting and venture capital.
Stig has extensive experience in play development and prospect generation in
various basins globally. Stig Arne has performed a large variation of risk
assessment as part of prospect maturation with HI-end tools from various vendors
including Petrel and SMT..
Stig Arne has participated in multiple projects with efficient Exploration and
Production of oil and gas resources, and experience in making quick turnaround
from resource to reserves. Utilizing acceptable international renown techniques
to achieve the goal of the projects are always the
Stig Arne Kristoffersen has experience in farm-in and -out negotiations, asset
management, strategy decisions within oil and gas as well as information
technology matters. Hands on experience in asset evaluation as well as
exploration strategy and portfolio management.