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CSEM exploration in the Barents Sea: Joint CSEM & Seismic interpretation
Janniche Iren Nordskag*, Øyvind Kjøsnes, Anh Kiet Nguyen, and Ketil Hokstad, Statoil ASA
Summary
While seismic has a superior structural resolution and is
sensitive to porosity and the presence of hydrocarbons, it
has very little sensitivity to saturation. Controlled-source
electromagnetic (CSEM) is, on the other hand, sensitive to
hydrocarbon saturation. Thus, joint interpretation of
seismic and CSEM inversion results offer a great potential
for hydrocarbon saturation and volume estimations. We
describe a new workflow employing high resolution
deterministic CSEM inversion and statistical seismic AVO
inversion that applies in an exploration setting, i.e. without
prior assumptions of the background resistivities or the
existence, sizes and shapes of resistive anomalies. The
high resolution deterministic CSEM inversion has proven
better depth placements and more confined resistive
anomalies by incorporating seismic horizons into the
inversion. We use a statistical AVO inversion for lithology
and fluid predictions, called PCube, where a prior model
for the elastic properties is defined for different lithology
and fluid classes (LFCs) based on geological knowledge
and/or information from nearby wells. PCube outputs
posterior probability for each LFC specified in the prior
model. The inversion results from CSEM and seismic are
then co-interpreted utilizing their complementary
information. This offers a direct comparison between
expected hydrocarbon-filled reservoirs from seismic and
whether these reservoirs are of high or low saturation from
CSEM inversion results, and to explain resistive anomalies
not related to hydrocarbons. Applying the workflow on
Skrugard datasets, the joint interpretation clearly shows the
ability to discriminate high hydrocarbon saturated from
brine/low saturated sand reservoirs. Furthermore, the
workflow can also distinguish hydrocarbon related from
non-hydrocarbon related resistive anomalies.
Introduction
In hydrocarbon exploration, seismic has been the data of
choice for interpreters because of high spatial resolution,
its sensitivity to porosity and to the presence of
hydrocarbons, for instance in the form of direct
hydrocarbon indicators (DHIs) like flatspots. Although
seismic might be able to detect the presence of
hydrocarbons, it is not very sensitive to the fluid saturation.
Another hydrocarbon exploration tool that has been used
commercially in the last decade is controlled-source
electromagnetic, CSEM (Eidesmo et al., 2002), which is
sensitive to the saturation of saline water in the formation
pore space. Marine CSEM has since its conception evolved
immensely both on the hardware (more sensitive receivers
and powerful source), acquisition (3D grid) and processing
(3D anisotropic inversion) side towards better accuracy
and reduced interpretation ambiguity. Today, 3D CSEM
anisotropic inversions are commonly used to give a
resistivity model of the subsurface
Thus, joint interpretation of seismic and CSEM inversion
results offer a great potential for hydrocarbon saturation
and volume estimations (Harris et al., 2009 and Morten et
al., 2012). However, there are issues related to the poor
spatial and burial depth resolution of 3D CSEM inversion
results, see for example typical state-of-the-art results in
Gabrielsen et al. (2013). This is related to the low
frequency content in the source and to the fact that CSEM
is sensitive to the thickness-resistivity product, transverse
resistance (Constable and Weiss, 2006)
In seismic AVO analysis the interpreter is searching for
high porous sands and direct hydrocarbon indications, but
it might be hard to differentiate between a high and low
saturation case. Seismic interpretation may then be aided
by CSEM to look for prospects that have high saturation
and thereby large volumes.
The low resolution in CSEM inversion results complicates
joint interpretation/inversion with other geophysical
methods of much higher resolution such as seismic and
well logging. By including constraints in the CSEM
inversions, we might reduce the ambiguity and provide a
better resistivity image of the subsurface (Harris et al.,
2009 and Morten et al., 2012). However, constrained
CSEM inversions often require precise knowledge of the
background resistivities together with the size and shape of
all the anomalies to be studied. This prior information is
usually not available in an exploration setting.
Although CSEM data is sensitive to the water saturation in
subsurface formations and will give a resistive anomaly in
the case of high hydrocarbon saturation, other lithologies
might also give rise to resistivity anomalies, e.g. salt,
basalt, mature source rocks and consolidated sand. In
addition to this, CSEM data does not have the resolution to
differentiate between stacks of gas and oil saturated sand.
Joint interpretation with seismic inversion methods may
help resolving these ambiguities.
In this abstract we will present a workflow and a real data
example where high resolution CSEM inversion (Nguyen
et al., 2013) is interpreted jointly with a statistical seismic
AVO inversion (Buland and Omre, 2003), called PCube, to
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Joint CSEM & Seismic interpretation
reduce the uncertainties in the hydrocarbon volume
estimation and to discriminate between high hydrocarbon
saturated sand from other causes of resistive anomalies.
This study is a part of a larger study that includes CSEM
inversions, triaxial induction log measurements and
seismic inversions of different Skrugard datasets (Løseth et
al., 2013 and Nguyen et al., 2013).
Seismic AVO inversion, PCube
The seismic inversion is a linearized Bayesian AVO
inversion for lithology and fluid predictions called
Probability Cube (PCube) (Buland and Omre, 2003 and
Buland et al., 2008).
To compute a prior model for the elastic properties, one
defines lithology and fluid classes (LFCs) based on
geological knowledge and/or nearby well log(s). The rock
physics properties of each LFC are then specified by a
likelihood function and each LFC are given a probability
distribution, see Figure 1. Typical LFCs are different types
of shale and sand, where the sands are also defined with
different fluids (brine, oil and gas). Nearby well log(s) are
also used to estimate a proper source wavelet which quality
is essential for the inversion results.
Outputs from PCube are spatial probability cubes for each
of the LCFs defined in the prior model and an undefined
cube where the seismic data does not fit any of the
classified LFCs.
Since CSEM can typically not discriminate between oil
and gas, the posterior probability cube for oil and gas
saturated sands are merged into one cube that gives the
posterior probability for hydrocarbon saturated sandstone.
High resolution CSEM inversion
The high resolution deterministic CSEM inversion is
described in (Nguyen et al., 2013). In short, it is a
workflow including a stitched common midpoint (CMP)
anisotropic inversion utilizing seismic horizons to create
initial models for a pixel based, anisotropic 3D inversion
tool. Since no assumptions about the existence, size and
shape of reservoirs are made in the workflow, it is
applicable in a frontier exploration setting. The workflow
for the 3D inversion described by Nguyen et al. (2013)
gives better placements and confinements in depth for
resistive anomalies.
Different from the CSEM inversion presented in Nguyen et
al. (2013) for the 2011 CSEM dataset, we have here two
3D CSEM inversions, one for the southern part and one for
the northern part. The reason for this split up is the
different depth of targets in the subsurface. We use
frequencies f=1,2,4Hz and a much finer inversion and
modeling grids dx=dy=100m and dz=40m for the northern
part, due to shallow targets. Higher frequencies and denser
but smaller numerical grids ensure higher resolutions in the
inverted resistivity models. In the 3D CSEM inversion for
the northern part, we have used a smoothing regularization
down to a certain formation in order to mute receiver
imprints. For the southern part we use frequencies f = 1, 2
Hz, and the modeling and inversion grid is dx=dy=150 m
and dz=40 m. The final inverted models have data misfit
below 5%.
Joint interpretation
When the inversion results from seismic and CSEM are
satisfactory, they are co-visualized and co-interpreted
using appropriate software. By utilizing the
complementary information from PCube and 3D CSEM
inversion, we are able to discriminate between high and
low hydrocarbon saturation for a reservoir, and possibly
explain resistive anomalies not related to hydrocarbon
reservoirs.
Example
We apply the workflow on the 2011 CSEM Skrugard
dataset and a seismic multi-client dataset acquired by
WesternGeco in 2008. After running the CSEM and
seismic inversions separately, they are co-visualized for
joint interpretation.
Figure 2 shows sections of the inverted vertical resistivity
over the Skrugard discovery and the 7219/9-1 well with
residual hydrocarbon saturation, which was drilled by
Norsk Hydro in 1988. It is a clear resistive anomaly
precisely bounded by Skrugard top reservoir and the oil-
water contact. Note that no assumptions about Skrugard or
the background resistivities have been made in the
inversion. Precisely at the location of the 7219/9-1 well, on
the other hand, no resistive anomaly is found. To the right
for the 7219/9-1well, there is a resistive anomaly which
origin is at present not determined.
Figure 3 shows the PCube a posteriori probability for
hydrocarbon saturated sand over the same regions. PCube
predicts high probability for hydrocarbon saturated sand at
Skrugard. The predicted volume fits well with top reservoir
and second flat spot. However, PCube also predicts high
probability for hydrocarbons in a formation underneath the
sand formation where the Skrugard discovery was made
and the 7219/9-1 well was tested and found only residual
hydrocarbon saturation. Here, the inverted CSEM models
do not have any anomalies indicating that the hydrocarbon
saturation is low or zero.
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Joint CSEM & Seismic interpretation
On the other hand, PCube provides low/no probability for
hydrocarbon saturated sand at the location of the resistive
anomaly to the right for the 7219/9-1 well that indicates
the origin of this anomaly is perhaps not hydrocarbon
related.
Conclusion
We describe a new workflow combining deterministic high
resolution CSEM inversion and statistical seismic AVO
inversion and apply it on Skrugard CSEM and seismic
datasets. The work flow is applicable in an exploration
setting. The joint interpretation clearly shows the ability to
discriminate high hydrocarbon saturated from brine/low
saturated reservoirs. Furthermore, the workflow can also
distinguish hydrocarbon related from non-hydrocarbon
related resistive anomalies.
Acknowledgements
We thank the Skrugard Exploration Team for valuable
inputs and cooperation. We also thank Statoil and the
Skrugard license partners (Eni Norge and Petoro), and
WesternGeco for permission to publish the results.
Figure 1: Acoustic impedance versus P- and S-wave velocity ratio for the classified facies in PCube using the Skrugard well log.
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Joint CSEM & Seismic interpretation
Figure 2: Vertical resistivity section through the Skrugard well (left panel) and the 7219/9-1 well (right panel) of the 3D CSEM
inversion result overlain on depth migrated seismic.
Figure 3: Posterior hydrocarbon probability from PCube through the Skrugard well (left panel) and the 7219/9-1 well (right
panel). The bright yellow colour shows high probability for hydrocarbon saturated sand. Water surface multiples contaminate
the seismic above the solid black line invalidating the PCube results here.
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http://dx.doi.org/10.1190/segam2013-0883.1
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