1. IPTC-18299-MS-MS
Forward Process-based Modeling of Submarine Turbiditic Environment:
BC-10 OSTRA Case Study
Javier Ferrandis, Alessandro Cantelli, Eduardo Jimenez, Shell Intl. E&P and Alejandro Girardi, Shell Brasil
Copyright 2015, International Petroleum Technology Conference
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Abstract
Some of the current methods of geological modeling cannot imitate the order and complexity that is
observed in nature. It’s been recognized among the geoscientist community how a process-based
modelling may facilitate a step change in reservoir building capabilities. The advantage in decoupling
the grid design from the facies distribution poses a tremendous opportunity to close the gap between the
static and dynamic reservoir model. A major advantage is capturing fine-scale heterogeneities (shale
drape, amalgamation, etc) which are hard to capture with pixel-based techniques. The goal of process-
based geological modeling is to simulate that order by generating rules. These rules make possible to
create high-fidelity 3D geological models representing more realistic depositional and stratigraphic
events than those that are randomly generated using conventional geostatistical techniques.
In this paper we illustrate the use of surface-based modeling within a submarine-turbiditic environment
and the procedure followed to deliver a dynamic model conditioned to production data. The procedure
follows development of physics-based methods and rule-base stacking of events to reconcile geological
complexities and uncertainties with well performance. We’ll present a process combining Design of
Experiments and gradient-based techniques to assimilate production data. The method generates
complex geometries comparable to those observed in high resolution near-surface seismic datasets. The
case under consideration is an oil field offshore Brazil developed with horizontal wells and state of the
art surveillance. The proposed workflow has delivered a simulation model that has achieved a good
history match to production data in the form of water cuts and pressures.
The Parque Das Conchas (BC-10) OSTRA reservoir
The Ostra accumulation is part of the BC-10 block, which is located 120 km offshore of Brazil, in the
northern Campos Basin (see Figure 1). The Parque das Conchas (BC-10) represents a key milestone in
the development and commercialisation of Brazil’s deep-water oil. The project consists of five fields:
Ostra, Argonauta B-West, Abalone, Argonauta O-North and Nautilus. Phase one of the project involved
the development of Abalone, Ostra and Argonauta B-West. Production from phase one began on 13 July
2009. The three fields have been developed with subsea wells and manifolds, with each field connected
to a centrally located floating production, storage and offloading (FPSO) vessel moored in around
1,780m of water. Ostra is a structurally complex Cretaceous, moderately heavy oil reservoir, in a
deepwater shallow overburden setting. The environment of deposition for the Maastrichtian reservoir is

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a highly amalgamated turbidite channel complex, deposited within a major depositional fairway. Figure
2 shows an amplitude map at regional scale, the meandering migrating channel system is clearly visible
from regional seismic extending from south-west to north-east.
Figure 1: BC-10 Resource Map
Figure 2: Regional RMS attribute map of BC-10 area
OSTRA

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The Maastrichtian reservoir sands were oil bearing as expected and were encountered very close to the
targeted depths. The quality of the sands is good with porosity and permeability on the range of 30%
porosity, 360 mD average permeability and a hydrocarbon API of 24. The field is operated by Shell with
a 50% equity share, with Joint Venture partners ONGC 27% and Qatar Petroleum International 23%.
See references [1-5] for more details. The Ostra accumulation is located on top of a salt dome and the
trap is formed by a complex faulted four-way dip closure. In addition to the two major normal faults
forming a horst block there are numerous smaller normal faults radiating from the crest (see Figure 3).
Downdip the structure there’s a large regional aquifer providing enough energy to produce under
primary depletion.
Figure 3: 3D view of the root mean squared (RMS) seismic attribute calculated for a window of 180 m
to 220 m under the Top Maastrichtian horizon and projected on the top surface.
The first five exploration wells in the BC-10 area were drilled from 1999 to 2001. Well count
optimization work with a reference case model called for completion of six producers with 600-1100 m
long horizontal sections. The field started production in October 2009. An additional, 7th, producer was
completed and started producing in June 2011. Temporary gas injection was selected as the gas solution
pending hook-up of a gas export pipeline [2].
The well layout is presented in Figure 4 together with pay zone structural top and all mapped faults.
Water breakthrough start occurring in the 2010 and by 2011 all wells were producing water. Initially the
main uncertainties associated with the Ostra field were determined to be related to (i) the effectiveness
and size of the aquifer, (ii) the sealing capacity of the main fault system dissecting the field, (iii) the rock
properties, (iv) the extensiveness of sealing features draped around at the base of individual channel
layers and related sweep efficiency, and (v) sand related issues created by the poor sorting and high fines
content of the sandstone reservoir.

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Figure 4: Top Map OSTRA. Wells PH1, PH2, PH3 paths can be observed on west side of the dome.
Wells PH4, PH5 and PH6 can be observed on north side of the dome
Introduction to CCT (Channel Center-line Trajectory) model
The Channel Center-line Trajectory (CCT) modeling used in this work is a method to generate three-
dimensional geomorphologic and stratigraphic models of submarine channel-levee systems. This
method generates complex geometries comparable to those observed in high-resolution near-surface
seismic datasets. The software code has been developed by Shell’s Clastic research team and is used as
the state of the art modle building approach for turbiditic channelized environemnt. We note that when
simulated at the highest resolution the models can generate features resembling channel architectures
observed in outcrops.
The first step of the workflow consists of extracting center-lines from a seismic attribute. These center-
lines are the building patterns used in rule-based modeling. The basic assumptions will be explained
below and requires the interpretations to be performed with depth converted seismic. A seismic
impedance volume was used to generate a collection of RMS attribute maps for each one of the intra-
reservoir horizons as well as for the top and bottom horizons. In order to generate the RMS maps for
each horizon a window of 20 ms around the horizon was used. Figure 5 shows examples of the resulting
maps. To some extent channels and channel belts can be seismically mapped. A dominant channel
orientation can be observed on the RMS maps. In addition to the seismic, well-logs were used to
manually guide the channels and levees optimal locations.
The CCT method relies on a number of basic key concepts and assumptions:
• Submarine channel plan view evolution results from migrating channel center-lines, similar to
fluvial channels. Channel center-lines migrate gradually except where they form meander cutoffs or
where avulsion occurs updip. Random placement of channels is not realistic.

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• A channel centerpoint trajectory (CCT) defines the amount of aggradation or incision during each
time step. A single vertical CCT component is used for most modeling purposes as the CCT is
assumed to vary gradually along the channel length.
• The channel cross-sectional form relates to the characteristic discharge of the system that does not
change significantly during channel evolution.
• Long-term valley widening takes place through the cumulative effects of bank erosion by turbiditic
currents and bank collapse.
• Channels are concave-up, u-shaped surfaces with fixed widths and depths.
• Channel surfaces aggrade, sweep and swing during deposition. A single continuous channel is
present on the sea floor at each time step.
• Levees form wedges that thin away from channel margins and drape pre-existing morphology.
• The final stratigraphic architecture results from the cumulative effects of deposition and erosion
along a slowly changing topographic surface.
Figure 5: RMS attribute maps of the OSTRA field and examples of the trajectories of the interpreted
channel Center-lines
Observations from seafloor images and high-resolution seismic data suggest that submarine channel-
levee systems form through the relatively continuous migration of a single channel form. Formation of
large valley fills does not require a switch from large, erosional flows to much smaller, depositional
currents. Summarized are the main features of the results of CCT modeling:
• The CCT (channel center-line trajectory) model generates complex three-dimensional channel-levee
geometries that resemble geometries observed in seismic data and in outcrops.
• Submarine channel-levee systems can be understood and modeled using a single incision-
aggradation cycle, with no major change in flow properties.

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• Inner levees grade laterally into both outer levees and channels, resulting in better connectivity than
expected from widely used cut-and-fill models.
• Three-dimensional geometries of submarine channel-levee systems are extremely complicated, even
within a single incision-aggradation cycle, without any re-incisions.
• Multiple time-transgressive erosional surfaces of different sizes are present in two-dimensional cross
sections.
• The channel architectures in the CCT model are markedly different from reservoir models with a
stochastic placement of channels. Due to the near-axial connectedness of the channel migration
deposits, connectivity of such reservoirs is generally higher than the connectivity of stochastic
models, even if the overall N/G is low.
Further work is needed to (a) calibrate the model with results from numerical modeling and outcrops,
and (b) test it through application to an actual reservoir. Furthermore, new frameworks for seismic
interpretation, reservoir characterization, and well placement, will be possible using the CCT model.
The modeling seems to be particularly appropriate to the geology observed in the Ostra Field.
Stratigraphic input elements
The CCT workflow requires a series of quantitative parameters that are obtained by seismic, well-logs
and analogues from different dataset of similar depositional environment. Key input elements are:
center-line trajectories defined above, top and bottom reservoir surfaces, channels dimensions and
channel properties.
Top and bottom reservoir surfaces: We used previously interpreted top and bottom reservoir horizons
to generate a collection of intra-reservoir horizons.
Channels dimensions: The regional seismic meander amplitude (Figure 2) indicates approximately 5
km and from this channel range Shell analogues and several literature papers shows possible dimensions
for width equal to 600-1000m and depth equal to 15-35m were considered.
Properties: sediment properties are related to channel topography. An elevation-dependent facies
model, which assumes decreasing grain size as height above channel thalweg increases, is a simple
method used here to illustrate the large-scale distribution of facies. Maximum and minimum
permeability and porosity are obtained from the Ostra wells.
Once these four input parameters/elements are obtained from the data, multiple realizations can be
generated using CCTs in a very short amount of time. Figure 6 shows two models with medium
connectivity (bottom) and high connectivity (top) obtained with the same set of center-lines but varying
the channels dimensions and properties. The petrophysical properties (porosity and permeability) range
from negligible values in shales (purple) to high values in the channels (red).

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Figure 6: Medium (a) and High (b) Connectivity scenarios
The example of the two models shown in Figure 6 highlights the variability that can be generated with
this method. Both models originate from equally realistic quantitative parameter ranges and yet display a
completely different connectivity and volumetric arquitecture (Figure 6).
Stratigraphic Grid Faulting
The main output of our in-house code is an unfaulted stratigraphic grid honoring the interpreted center
lines and the forward modelling rules present in turbiditic currents. This grid has to be further modified
in order to complete the faulting and property modelling process. This step of the workflow is currently
managed in Petrel and is one of the main potential areas of improvement.
A collection of surfaces are imported into the Petrel’s Horizon Modeling process, which are used to
generate an equivalent grid. The interpreted faults are then utilised to generate an associated faulted grid,
an example can be observed in Figure 7.

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Figure 7: Faulted Stratigraphic Grid generated in Petrel.
Property Modeling
Porosity and Permeability logs from all wells were used to determine the upper and lower limits of the
property histograms. These limits were enforced during the surface based forward modelling process.
Therefore on a large scale the limits imposed by the well logs are indirectly enforced. Forward
modelling predicts certain lateral distributions of porosity and permeability laterally across channels
(Figure 6). The property modelling is completed in Petrel by generating a Net to Gross (NTG) 3D model
based on the forward modelled permeability and porosity grids. Finally, these property grids are mapped
into the faulted grid using Petrel’s upscaling property process (Figure 7).
Log Conditioning
All geostatistical techniques extrapolate properties beyond the well locations while honoring core and
log data. By contrast the surface-based model presented here starts from seismic data and uses the
center-lines to constrain the location of the channels. A disadvantage is that the logs are not honoured
from the start and the degree of agreement of the forward property model with the well logs has to be
tested a posteriori.
The method followed by the authors to best honour the well logs in the prior model is manual. It consists
of visual inspection on the agreement between the well log facies and the forward property model using
well correlation panels (Figure 8). Since the center-line interpretation is approximate there is freedom to
slightly adjust the location of the center-lines. This can have a substantial effect on the well correlation
panels especially for horizontal wells. This degree of freedom was utilised to optimize the agreement
with the well logs for each one of the wells. Usually only a few of the interpreted center-lines will have
an impact on the correlation panels, those that correspond to channels that intersect with the wells. If
there is a channel that intersects with more than one well we observe an impact on more than one
correlation panel simultaneously.
By using this simple visual trial and error process it was possible to generate, after a few iterations, a
model with a reasonable well log agreement from the reservoir engineering perspective and from the
geological perspective. Once a reasonable agreement was reached with the forward model, the upscaled

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values of the logs only in those gridblocks directly intersecting with the well trajectories were utilised.
The clastic research team is actively working on the design of an algorithm that perturbs a given center-
line location until an objective function that measures the agreement between well log and forward
property model is optimized. The resulting Petrel model is exported into the reservoir simulator with no
additional grid upscaling.
Figure 8: Well Correlation Panels comparing well logs and forward property model. Each well panel
displays: perforations, permeability log, facies log, upscaled permeability log and forward model
permeability.
The Dynamic Model
A number of global parameters were identified as the most crucial uncertainty parameters and used
during the posterior history matching exercise (Table 1). These include: aquifer strength, kv/kh ratios for
sand and shale, shale layer effective permeability, and sand permeability multiplier. From all these, the
aquifer model is critical. Based on previous regional seismic studies of potential aquifer geometries the
AOR (aquifer to oil volume ratio) was estimated to be about 30 (see Ref [1]). Considering the set up of
the regional aquifer and the timings of the water breaktrough, we decided to model the aquifer as two
separate finite-linear aquifers with two independent aquifer strengths which gave us an additional
parameter to help match the data.

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Parameters Min Max Best
Sand Perm Multiplier 0.9 1 1.2
Shale Effective Permeability 0.1md 1md 20md
Aquifer Strength 105
106
107
Sand Kv/Kh 0.6 0.7 0.8
Shale Kv/Kh 0.1 0.2 0.4
Table 1: Uncertainty Variables
Property mapping to a homogenous pillar based Grid
In order to avoid the possible appearance of numerical discretization errors caused by severely distorted
grid-cells, we mapped both the surface-based facies and property model into an orthogonal faulted grid
(left panel in Figure 9). The main objective here was to preserve all the depositional and arquitectural
features of the surface based model. A comparions is presented in Figure11, note how the agradational
and amalgamated features of the stacked channels are captured by the pillar-base grid. It’s also
important to mention, that the mapping of these features is best suited for systems with high net-to-gross
and sandstone proportions where shale drapping is of second order effects.
Figure 9: Mapping of properties from a stratigraphic based grid to a Petrel generated pilar based
homogenous grid.
History Matching and geological realizations
The parameters that drove our HM process were: reinjected gas breakthrough time, bottom-hole pressure
and water rates. The model updates were carried out in three separate stages following a hierarchical
approach. The first stage was manually changing the center-lines to ensure a ballpark agreement
between the simulated and measured BHP. With the first batch of exploratory runs we found that large
pressure differences were mostly caused by an insufficient agreement between the mapped facies model
and the well logs. This was particularly the case, for perforated zones not coinciding with sand blocks
where the disagreement was too severe and some well had issues maintaining pressure. This step was

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done in a trial an error fashion perturbing the center-lines and is an instant improvement area. Our vision
is to parameterize the center lines and an exploit high performing computing to properly condition the
log data. Altough this trial and error step is not desirable the ballpark agreement on flowing and static
pressures was deemed acceptable (Figure 10).
Figure 10: Bottom Hole Pressure (BHP) for a typical producing well.
In this first stage the reinjected gas breakthrough time was also used as a conditioning measurement. All
produced gas is reinjected into the aquifer zone but after two years of production gas broke through into
one of the flank wells. The location of the injector relative to the closest producer implied a large degree
of connectivity in between the channels (Figure 11). Several scenarios of channel density were tested
until an approximate gas breakthrough time was achieved.
Figure 11: Simulation of Final Saturation Map displaying the reinjected gas path (green) through the
aquifer zone (blue) into the oil bearing zone (red) towards a flank well.

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The second stage in the history match involved examining other global uncertainty parameters which
included the aquifer strength, kv/kh ratios for sand and shale, shale layer effective permeability and a
sand permeability multiplier. These global model updates were carried out using Desing of Experiments
(DOE) and MCMC. Our in-house simulator has a standard suite of DOE algorithms which can be linked
to any parameter in the simulation deck. The main observables considered in the proxy definition for
each well were the water breaktrough time and the cumulative water at 2012. The main goal in this stage
was to get a rough agreement between the simulated and produced water volumes and reproduce the
water breaktorugh times within 6 months of occurrence. Figure 15 shows the history match obtained
with the global updates. Black lines are the measurement and green lines the simulated responses.
Altough the field water shows an acceptable match (bottom right) almost all the wells required a fine
tuning in both the arrival water times and overall profiles.
Figure 12: Water Rates for the best AHM match delivered by the use of DoE and Monte Carlo methods.
The last step of our AHM process consisted of a method to apply local updates to the permeability field
using a gradient based technique algorithm available in our in-house simulator. The choosen method
relied on solving the adjoint flow equations at multiple timesteps to derive the search gradienst to update
the properties. The adjoint-based techniques are well known in the field of AHM (Assisted History
Matching) [8-10] and have been studied previously using our in-house simulator [10]. For our
application the method delivers, through a series of successive iterations and backward simulations, a
final model with virtually a perfect match of water rates (Figure 16). In order to preserve the mapped
facies model, the permeability updates were constrained in order to respect the histogram bounds. The
final difference property map is an indication of where the geological model needs further improvement.
In future applications it’s envisioned to close this loop by guiding the geological iteration by modifying
some of the parameters that control the forward based modelling and repeating the whole process until
the outcome of the DoE part is more satisfactory.

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Figure 13: Water Rates of the successive iterations executed by the ad joint method.
Conclusions and Remaining Issues
Constructing reservoir models by modeling the fundamental processes that formed them has been shown
to generate a valid reservoir simulator model for submarine turbidite channels. Our proposal relies on
surface-based modeling to comprise the fundamental processes that shaped channel features.
The building of process-based models for reservoir simulation is nevertheless challenging. Some of the
main challenges that still need to be addressed are: 1) effective conditioning of log and seismic data 2)
the entire fault structural model needs to be included in the surface-based gridding 3) we presented a
practical alternative to numerical discretization errors by mapping the stratigraphic grid into an
orthogonal pillar-based grid.
The history match carried in separate stages provides a quality assurance process where the integration
of subsurface disciplines is enabled. This is a far more effective process than the conventional discipline
silos in the multiple stages of both static and dynamic modelling.
References
1. “Parque Das Conchas BC-10: Subsurface Challenges in Developing a deepwater shallow
geologically complex field ” , L. Stockwell, J.H. Van Konijnenburg, G. Holmes, G. Stewart, S.
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2. “Parque Das Conchas BC-10: Delivery of deepwater Extended Reach Wells in a Low Fracture
Gradient Setting” , W. Bode MME et al. (2010, Offshore Technology Conference)
3. “Design and Operability considerations of the gas flowline at the Parque Das Conchas BC-10 Ostra
Field” , Z. Atakan, D. Chin, Peter Lang, Sada Iyer (2010, Offshore Technology Conference)

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