From Seismic Interpretation to Reservoir Model

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From seismic interpretation to reservoir model:
an integrated study accounting for the
structural complexity of the Vienna Basin
using an unstructured reservoir grid
Caroline Milliotte1* and Stephan K. Matthäi2 show how a former structurally complex oil
reservoir that is evaluated as a geological CO2 storage complex, can be modelled at a very
high level of geological detail using a new workflow involving unstructured finite element
meshes. The resulting simulation model includes discrete representations of complex intersecting faults.
T
o mitigate climate change induced by industrial CO2
emissions, new techniques such as geological Carbon
Capture and Storage (CCS) are being investigated.
The successful implementation of CCS is challenging,
involving detailed characterization, modelling and simulation of the performance of a candidate storage complex, to
become aware of potentially adverse emergent behaviour
arising from CO2 injection – for instance: non-radial, potentially highly localized CO2 spreading, exhaustion of the
sealing capacity or breach of the confining structures (faults
and cap rock), reactivation of sealed faults, and the risk of
leakage from the storage site (cf. European Communities
Directive on the geological storage of carbon dioxide, 2010).
For these reasons, the implementation of CCS requires careful multi-disciplinary assessment, preceding any pilot or
full-scale project.
The Reservoir Engineering Institute of the Montanuniversitaet Leoben (MUL), Austria, is currently working on
the simulation and hindcasting of a CO2 injection project
conducted to enhance oil recovery from a mature reservoir.
This research is supported by the Austrian funding agency
FFG, and data is provided by an oil and gas company. This
company contracted NFR Studies to build an improved
reservoir simulation model that captured the structural complexity of the candidate storage site, and conduct property
modelling in more detail than achieved previously for field
development and production purposes.
This paper describes the main steps of the static characterization and modelling of the candidate storage site.
This is an oil reservoir depleted in the 1970s, located in the
Vienna Basin at about 1500 m deep. It has a lateral extent
of 15 km x 7.5 km and consists of a 200-m thick sequence
of Mid-Miocene siliciclastics. The deposition environment is
pro-grading deltas, and they overlie a conglomeratic layer
that is a regional active aquifer. The trap consists of a gentle
anticline structure, closed by a complex fault system on
its eastern flank. About 80 vertical wells have been drilled
through the crest of the anticline and the main stratigraphic
markers have been interpreted from logs. Outcrop studies
and lidar surveys confirmed that the fault system is active
today (Gutdeutsch and Aric, 1988; Hinsch et al., 2005b;
Beidinger and Decker, 2010). This made it necessary to
evaluate the sealing capacity of the faults, and the structural
model had to capture the full vertical extent of the faults,
from basement to ground surface.
Methodology
Starting with the seismic interpretation of the faults in the
two-way time (TWT) domain and the construction of a timedomain structural model, a velocity model was built and
used for the depth conversion. The depth domain structural
model was checked against well markers and consequently
some adjustments were made to the fault interpretation. The
resulting watertight structural model was used as input for
the creation of a fully unstructured reservoir grid, meshed
with tetrahedral aligned along faults and horizons. In addition, an innovative petrophysical technique (Ramberger and
Skolnakorn, 2001) was used to compute porosity values
from the available SP logs. The porosity was simulated in
the geological grid, using areal trends derived from a series
of regional facies maps per main stratigraphic unit. Porositypermeability correlations were used to populate permeability
values in the geological grid. Finally, the properties were
transferred from the geologic grid on to the unstructured reservoir grid. Fluid pressure was initialized in the latter, using
the CSMP++ reservoir simulator, developed by the MUL.
NFR Studies GmbH, Graz, Austria.
Institute of Reservoir Engineering, Montan University of Leoben, Leoben, Austria.
*
Corresponding Author, E-mail: [email protected]
1
2
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Figure 1 Inline section (TWT) with initial
interpretation of the five main stratigraphic
horizons: top of basement (dark green),
conglomerates (red), 1st reservoir (purple),
2nd reservoir (yellow) and shale cap rock
(light green). The 1st reservoir layer overlies
a stratigraphic unconformity and onlaps on
the conglomerates (white arrow). Faults can
be identified in the north-east (white lines).
As the reservoir was developed more than 30 years ago,
most of the available data is old; a structural framework
model was not available and the existing reservoir model did
not contain any faults. Five main stratigraphic horizons and
a regional seismic amplitude volume were made available
in the two-way time domain (Figure 1). Fault interpretation
was inexistent and some stratigraphic markers were missing
or duplicated.
ties induced by the faults were then highlighted, facilitating
the picking process of the fault sticks. While interpreting the
faults, the corresponding structural model was automatically
generated using a volume-based implicit modelling approach
and the UVT transform from Mallet et al. (2007). This
process enabled a rapid update of the fault interpretation,
gaining confidence in the geological realism of the interpreted fault contacts.
Seismic interpretation of the major faults
Velocity model and time-to-depth conversion
The main faults were identified on the available horizon
interpretations, especially at the top of the sequence. In the
northeast, fault arrays represent the main structure (Figure 2).
To interpret the subvertical faults, seismic amplitude was
converted into a semblance volume. The signal discontinui-
A time domain geological grid was created for the velocity
modelling. From the available 80 wells, only three wells had
check-shot data and only one could be used after review of
the check-shot quality. For this well, pseudo average velocities were computed, from: i) the known depth of the main
stratigraphic well markers and ii) the TWT value, interpolated from the vertical projection of the well markers on to
the corresponding two-way time stratigraphic horizons. The
pseudo velocity was then calculated, using the following
equation:
Vavg , pseudo =
Figure 2 Top view of shallowest horizon interpretation: complex fault arrays
are highlighted (red arrows) in the northeast. Straight features oriented
NE-SW are faults with a relatively small throw (perpendicular to the white
arrows). The new interpretation includes all of these features.
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Depthwell marker
TWTprojected well marker
x 2 x1000
where 2 x 1000 represents the conversion factor from milli­
seconds TWT into seconds.
The pseudo velocity was then transferred on to the time
domain geological grid and interpolated. This procedure
ensures that the interpolation of pseudo velocity honours
the sharp discontinuities induced by the faults and the stratigraphic unconformity. Finally, pseudo velocity was calibrated
with the check-shot data and the corresponding correction
factor was interpolated in the grid and applied to the entire
model. The corrected final velocity was used to perform the
time-to-depth conversion of the structural model, meaning
that faults, horizons and the geological grid were converted
in one single operation.
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Depth structural model – fit to well markers
The depth-converted structural model did not match all the
well markers. Some adjustments of the fault interpretations
were made, getting problematic well markers to be located
on the correct side of fault planes. Additionally, some
inconsistencies in the original interpretation of the well
markers were highlighted by applying the UVT transform
procedure: where the first reservoir layer was onlapping on
to the conglomerates (0 thickness unit), some of the corresponding well markers indicated a thickness of several
tens of metres for this reservoir unit. The inconsistent well
markers were assigned to a shallower stratigraphic horizon
and the unconformity was preserved in the final depth
domain model.
The tops of reservoir markers were missing for most of
the wells. Using spontaneous potential (SP) logs acting as
good indicators for the shale content of clastic intervals,
new markers were interpreted and introduced into 19 wells.
In order to simulate CO2 injection and potential viscous
or heterogeneity-induced fingering, the lateral extent of the
model was increased. The model boundaries are now located
several tens of kilometres away from the injection well, also
allowing an assessment of the impact of injection on fluid
pressure via simulation.
Geologic grid
Using the integrated volumetric modelling approach of
Mallet et al. (2007), implemented in the Paradigm SKUA volume-based modelling system, as soon as a structural model
is created, a corresponding geocellular grid can be obtained
through contouring of the UVT function. Only the cell size
has to be specified. The geological grid conforms with geological time and honours structural and stratigraphic discontinuities, along which cells are being offset according to fault
throws, see result section (Figure 6).
Property modelling
Out of the 80 wells, only 19 have well logs. In particular,
spontaneous potential (SP) logs are available. The methodology of Ramberger and Skolnakorn (2001) uses SP and resistivity logs to determine the thickness of sand intervals and
their porosity. First, the resolution of the SP logs is enhanced
using a forward modelling filter, calibrated with standard SP
correction charts; SP values are normalized. Then, an inverse
filter is applied to generate a neural network for automatic
recognition of correlation patterns. Using the inverted SP
log and the resistivity data, the actual sand/shale ratio is
estimated. This method targets layered sandstones and is
applicable to the prograding deltaic sequence of the Vienna
basin reservoir.
From the new logs, porosity histograms and variograms
are generated – one for each stratigraphic unit and reservoir
rock type. These statistics serve as input for a sequential
© 2014 EAGE www.firstbreak.org
Gaussian simulation of porosity on the geological grid.
Regional facies deposition maps from Strauss et al. (2006)
define areal trends, capturing the orientation and size of
the fluvial system deposits (conglomerates) and those of the
deltaic systems (reservoir layers). These enter the property
modelling in the form of kriging weights.
Unstructured reservoir simulation grid
The detailed geological grid created on the basis of the UVT
Transform (Mallet et al., 2007), only has a representation in
parametric space. It cannot be outputted directly to a reservoir simulator. If a corner-point grid representation is used,
oblique features must be converted into a series of stair stepping cells to achieve the required regularization. If standard
first-order FD stencils are employed in the simulation, the
original oblique connectivity is lost in the stair steps (cf.,
Matthäi et al., 2007). Furthermore, corner-point cell distortions needed to get the grid to conform to curved boundaries
and Y-intersections provoke large discretization errors (e.g.,
King et al., 2006). In the planned assessment of the CO2 leakage risk, the active faults must be captured accurately. Hence,
corner-point discretization is not an option.
Considering alternative discretization approaches, it is
important to note that, due to the fault and unconformity
offsets, the UVT-based geocellular grid is not conforming
(i.e., node matching) across such boundaries. However,
recently it has become possible to create and output nodematching triangulated fault, horizon, and unconformity
surfaces from SKUA. Such boundary representations (BREP)
of the structural framework model are also watertight, i.e.,
the surfaces partition the model volume into unique and fully
closed sub-volumes. A node-matching and water-tight BREP
permits the creation of fully unstructured volumetric finiteelement meshes, using tools that are standard in other civil
engineering disciplines, like in the automotive industry (cf.,
Matthai et al., 2007, Paluszny et al., 2007). Following the
recommendations of Matthäi et al. (2007), a fully unstructured mesh was created, once the water-tight structural depth
model had been built for the Vienna basin reservoir. This
unstructured grid consists of tetrahedra, triangles, and line
elements. The latter represent the well completions embedded into the unstructured mesh. To capture the large-aspect
ratio faults and reservoir layers, the grid was adaptively
refined. Second only to the ability to discretize arbitrarily
shaped geologic objects, this spatially adaptive grid refinement is decisive for the construction of a geologically realistic
simulation model as it allows concentrating computational
effort and accuracy in the regions of interest.
There is an important difference between finite-difference (including corner-point) grids and finite-element
meshes: The former is a point-based material property
discretization. Since there are no discrete material boundaries, transmissibility multipliers are needed to establish flow
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connections crossing implicit boundaries existing between
material points. The finite-element method and derived
hybrid FEM-node centered finite volume methods used in
reservoir simulation (Kim and Deo, 1999, Matthai et al.,
2007, Geiger et al., 2009, and Matthai et al., 2012), are
based on a piecewise volumetric integration over elements.
The elements directly conform with material boundaries.
For material property transfer from the geocellular
(point-based) grid used in G&G tools in conjunction with
BREP, to the unstructured mesh-node matching the BREP,
this difference leads to two key requirements: 1) the property
mapping must honour the BREP volume decomposition,
and 2) point-based material property values obtained by
property modelling on fine regular grids in the G&G tools
need to be mapped to the spatially adaptively refined meshes,
retaining their statistics, spatial correlation structure and
integral values (PV, OIP etc.).
Property mapping – transfer between
the geological and the simulation grids
Modelled porosity and permeability values are stored at
the cell centre points of the geocellular grid. For each
stratigraphic layer, these points with property attributes
were exported to a corresponding ASCII file block with
the following structure: layer name, X, Y, Z, porosity,
and permeability. Using the structured-to-unstructured grid
property mapping algorithm of Mosser (2013), these values
were mapped onto the unstructured mesh for simulation
with the CSMP++ reservoir simulator developed at the
Montanuniversitaet Leoben, Austria. This mapping honours
stratigraphic boundaries, preventing spill-over of property
values. Where multiple values fall into a single tetrahedron,
area-weighted arithmetic averaging is performed for porosity, while harmonic averaging is applied for permeability.
Where the point spacing is smaller than the element size,
this algorithm interpolates property values from elements in
the neighbourhood using a Laplace equation-based, smooth
non-oscillatory interpolation algorithm (cf., Sambridge et al.,
1995). To map properties from the point grid to triangulated
surfaces when a lower-dimensional fault representation is
Figure 3 TWT structural interpretation of the storage complex. Lowest reflector represents the basement; top = ground surface. Note the newly interpreted fault sticks constraining 40 individual faults.
Figure 4 TWT water-tight BREP of the storage complex (see text). Fault arrays
are captured and clean contacts (X and Y shapes) modelled; vertical NE-SW
striking faults are also captured. The unconformity (purple) at the base of the
reservoir horizon is preserved.
Figure 5 Inline section through the TWT structural
model, intersecting the fault arrays. Faults have a
vertical extent from basement to ground surface.
The anticline structure is honoured, as well as the
non-uniform thickness of the first reservoir layer
(between purple and red horizons).
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Figure 6 Detail of the geological grid (depth domain), showing the cells being
cut and displayed at fault intersections as well as at the stratigraphic unconformity (purple layer).
used (cf., Juanes et al., 2000), property values are first projected on to the triangular elements, discretizing the faults
before the same algorithm is applied in the fault plane.
Link with reservoir simulation
Once populated with properties, the unstructured reservoir
simulation model lends support to multi-physics simulations,
using, for instance, the hybrid Finite Element – Finite Volume
(FEFV) approach implemented using CSMP++ (Matthäi et
al., 2007). However, since the properties of the faults are
a function of the effective stress, fluid pressure has to be
initialized first, taking into account initial saturations, see
Milliotte et al. (2013). The permeability of the fault zones is
modelled, taking into account available measurements of the
in situ stress. Multi-phase flow simulation of the gas-water(oil) system is now possible. Since the Vienna basin field case
is an exploited reservoir, history matching with dynamic
data can lend further support to the new simulation model
before the analysis of the different CO2 injection scenarios
is carried out.
Results
Forty faults were retained in the final structural model
(Figures 3-5), preserving the full complexity of their crosscutting relationships: most of the contacts had an X or Y
shape. According to Beidinger and Decker (2010), the fault
system in the north east of our model corresponds to a negative flower structure, developed in the Miocene and reactivated in the Quaternary. They interpreted the fault arrays as
Riedel-type splay faults branching on a main NE-SW fault
plane. Their interpretation was made mainly from a series of
2D seismic amplitude crosslines (12) and inlines (3), together
with four 2D time slices. Our 3D fault interpretation con-
© 2014 EAGE www.firstbreak.org
Figure 7 Porosity (dark blue curve), derived from SP (red curve) and resistivity
(light blue curve) logs, using method of Ramberger and Skolnakorn (2001).
Upper reservoir layer (shale-sand intercalations) in well from the central part
of the planned storage complex.
firms that the local extension is accommodated by a series
of normal faults. Yet, we have not been able to identify any
symmetry in the fault arrays. We would rather interpret the
structure as a consequence of extension locally induced by
the active sinistral Vienna Basin strike-slip fault.
While most of the faults were identified as normal
faults, one reverse fault was also interpreted. The final fault
interpretation fits nicely with Hölzel’s (2009) review of the
main structural components of the Vienna Basin.
The geological grid resolution was chosen as follows:
horizontal cell dimension is 50 m; same vertical cell size
in the non-reservoir layers and 25 m in the reservoir units.
Figure 6 shows a detailed view of the UVT gridding, honouring faults and the stratigraphic unconformity.
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Figure 8 Unstructured reservoir mesh. (Top) fault
and boundary surfaces (grey); top of basement
(horizontal green surface) and wells (lines).
(Bottom) tetrahedra in the basement layer and
triangulated surfaces for the stratigraphic layers
(green, beige and red horizontal surfaces). The
mesh is refined around the fault surfaces, wells
and reservoir layers, while a coarse mesh is used
in the basement.
Figure 9 Fluid pressure initialization of unstructured reservoir simulation model (rainbow colour
scheme). Only the reservoir layer is displayed
volumetrically. Fluid pressure is also shown for the
faults, reaching maximum values at the basement
contact.
In the reservoir sequence, the porosity log was calculated
from the normalized and resolution-enhanced SP logs combined with the resistivity logs. The calculation worked best
for the stacked sandstone beds intercalated with shales. Its
results are displayed for a well interval between 1000 and
1600 m deep in Figure 7. Porosity ranges around 8% in
the shales and around 15-20% in the sands. Local porosity
values reach 25%. Permeability has been derived from the
newly calculated porosity, using regional correlations.
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The final reservoir model has an area of 32 km x 13 km
and stands 6 km tall, delimited by the basement and the
ground surface. It contains 12 million elements. The faults,
represented by triangulated surfaces, are part of the mesh.
Tetrahedra discreting adjacent layers conform with the layer
boundaries and are node-matched along them (Figure 8). The
wells have a discrete representation by line elements, again
node-matched with volumetric elements that are refined
around them.
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Reservoir Monitoring
Property modelling was performed in the geologic grid
and followed by mapping onto the unstructured mesh.
Figure 9 shows a fluid pressure initialization based on a
hydrostatic pressure gradient and atmospheric pressure at
the model top.
Geiger, S., Matthai, S. K., Niessner, J. and Helmig, R. [2009] Black-oil
Conclusion
Hinsch, R., Decker, K. and Peresson, H. [2005] 3-D seismic interpretation
simulations for three-component, three-phase flow in fractured porous
media. SPE Journal, 14 (2) 338–354.
Gutdeutsch, R. and Aric, K. [1988] Seismicity and neotectonics of the East
Alpine-Carpathian and Pannonian area. American Association of
Petroleum Geologists, Memoir 45, 183–194.
In order to assess the performance of a potential CO2 storage complex in an exploited oil reservoir of the Vienna
basin, by EU-directive compliant simulation, a detailed
structural and geological model was built, using new
volume-based modelling tools and an unstructured simulation grid (=mesh). The structural complexity of an array of
40 intersecting, partially active faults was captured including X and Y contact shapes. A stratigraphic unconformity
was also captured by the new structural framework model.
The innovative and multi-disciplinary workflow presented
in this paper involves the creation of a fully unstructured
reservoir grid for supporting geomechanical calculations,
and an assessment of the sealing capacity of the faults.
The next step of this project will be history matching of
the new model with historic production data, followed by
multi-phase simulations, comparing a range of potential gas
injection scenarios.
The conforming, adaptively refined reservoir simulation
mesh allows multi-physics simulations with hybrid finite element – finite volume codes. This will permit tight coupling of
the multi-phase flow with geomechanics and reactive transport simulations, achieving the objectives of the performance
assessment according to the EU directive. Such simulations
are currently being implemented at the Montanuniversitaet
Leoben (cf., Mindel and Manasipov, 2013) in the framework
of the FFG-funded GEOCCS project.
and structural modelling in the Vienna basin: implications for Miocene
to recent kinematics. Austrian Journal of Earth Sciences, 97, 38-50.
Hölzel, M. [2009] Quantification of tectonic movement in the Vienna
Basin. PhD thesis, Universität Wien.
Juanes, R., Sampier, J. and Molinero, J. [2002] A general and efficient
formulation of fractures and boundary conditions in the finite element
method. International Journal for Numerical Methods in Engineering,
54 (12), 1–25.
Kim, J. G. and Deo, M. [1999] Comparison of the performance of a discrete
fracture multiphase model with those using conventional methods. SPE
Reservoir Simulation Symposium, SPE-51928-MS.
Mallet, J.L., Arpat, B., Cognot, R., Deny, L., Dulac, J.C., Gringarten, E.,
Jayr, S. and Levy, B. [2007] Beyond Stratigraphic Grids: Changing the
Paradigm. International Forum on Reservoir Simulation, 49.
Matthai, S. K., Bazr-Afkan, S., Lang, P. and Milliotte, C. [2012] Numerical
Prediction of Relative Permeability in Water-Wet Naturally Fractured
Reservoir Rocks. ECMOR XIII – 13th European Conference on
Mathematics of Oil Recovery, Extended Abstracts.
Matthäi, S. K., Geiger, S.,Roberts, S.G., Paluszny, A., Belayneh, M., Burri,
A., Mezentsev, A., Lu, H., Coumou, D., Driesner, T. and Heinrich, C.A.
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[2013] Exploring a new characterization, modeling, and simulation
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Acknowledgements
The authors would like to acknowledge the support of the
Austrian FFG research council in the framework of the
GEOCCS Bridge 1 project. We would also like to thank the
oil and gas company that co-sponsored this study for the
provision of field data and for allowing publication of this
workflow study. The authors would also thank Paradigm for
providing structural interpretation and property modelling
software tools and Rudolf Ramberger for his help with the
petrophysical interpretation.
EUROPEC 2013 Conference & Exhibition, Extended Abstracts.
Mindel, J., Manasipov, R. and Matthäi, S.K. [2013] Combined FiniteElement, Finite-Volume, and Discrete Event Simulation of structurally complex hydrocarbon reservoirs. SIAM 2013 Annual Meeting,
Abstracts.
Mosser, L. J. [2013] Mapping geo-cellular models to unstructured simulation grids. SPE Annual Technical Conference and Exhibition, SPE
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Ramberger, R. and Skolnakorn, J. [2001] Inverting SP logs using artificial
neural networks and the application in reservoir characterisation. In: P.
Wong et al. (Ed.) Soft Computing for Reservoir Characterization and
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