uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
uIntroduction
uFigure
captions
uIntegrated
model
uStructural
& stratigraphic model
uModeling
of facies
uPetrophysical
modeling
uConclusion
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Figure Captions
Figure 1. Location map
of Northern Venezuela, showing the Orinoco Oil Belt (in red) and its
four main areas: Machete, Zuata, Hamaca and Cerro Negro. The SINCOR
lease, covering 500 sq. km, is located inside the Zuata area.
Figure 2. Organization
and location of the three geographical assets.
Figure 3. E-W Cross-section of a shale
object-based simulation constrained by vertical and horizontal well
data; green cells represent shales and yellow cells are sands.
Figure
4. Porosity is calculated stochastically based on most likely porosity
log values in vertical wells
To build a coherent 3D
model of a reservoir dominated by fluvial deposits is a tremendous task
that requires a multi-disciplinary approach and the effective management
of a large amount of data (i.e., outcrops, cores, logs, 3D seismic, well
tests and production data). To ease this process, the 500 sq km of the
SINCOR lease have been divided in three geographical assets regrouping
all the E&P disciplines: geophysics, geology and reservoir engineering
(Figure 2). These integrated teams share a common project database where
all the relevant static inputs required for the 3D geomodel are
officially stored. To get the best picture of the reservoir and its
associated uncertainties, stochastic modeling conditioned to well data
is performed in order to generate several realizations of the geological
parameters on which to base reservoir production simulations. Principal
steps and direct inputs (static and dynamic) required to build the 3D
reservoir model are given here below.
Geologists and
geophysicists of each asset provide top and bottom structure maps of the
fluvial interval based on 3D seismic and well data (vertical, deviated,
and horizontal wells included). Because of the low acoustic impedance
contrast between sand and shale, it is not possible to image the
internal structure of the fluvial reservoir directly on the existing 3D
seismic. Therefore, a most-likely framework of the fluvial interval is
inferred from detailed well correlation scheme based on sequence
stratigraphy. The application of this concept provides a detailed
framework that may reduce the risk of miscorrelations between different
genetic units. Sequence boundaries were defined in the vertical and the
slant wells at the base of the main channel belts (i.e., at the top of
the underlying shale where it has not been fully eroded) and correlated
at the full field scale. So far, four stratigraphic units, that form the
main reservoir architecture, have been identified in the fluvial
succession. The thin shale layers at the top of the units may act
locally as pressure barriers between overlying and underlying sandstones
and may impact the vertical communication within the reservoir. Pressure
data from wireline tester tools (SFTT) help in validating the
chronostratigraphic correlation scheme and detecting the major shale
baffles that are so far the major heterogeneity identified in the
fluvial reservoir of SINCOR. This important issue has conditioned the
choice of the different modeling techniques applied for building of the
3D reservoir grid.
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Once the geometric
framework of the reservoir section has been validated, it is populated
with the lithological characteristics of the reservoir rock according to
their spatial distribution. The facies model is built integrating a
fluvial depositional model that has been clearly identified in core
material. The present interpretation of the sedimentological setting
varies from meandering to braided according to base sea level changes
(changes in accommodation/supply ratio).
Only two facies (i.e.
shale and sand) have been defined from the logs and distributed
throughout the field. This simple facies classification was the only way
to incorporate into the modeling process the huge amount of Logging
While Drilling (LWD) data provided by more than 300 horizontal wells.
The pay and the non-pay facies are identified on all the wells
(vertical, slant and horizontal wells) by applying log cut-offs on the
Shaliness (Vsh) and the Porosity (PHIE) curves. Normalized Gamma Ray
logs have been used to ensure the consistency of the facies
identification for all the wells. This normalization is highly
recommended when such a classification is based on a non-homogenous set
of wireline and LWD logs.
Once all the wells
have been interpreted with a lithofacies log , a 3D distribution of
facies can be performed for the whole reservoir. This distribution of
sands and shale is obtained through stochastic modeling including not
only the well data but also a set of 2D Net to Gross maps as major
constraints. These Net to Gross maps are generated for each
stratigraphic unit (4) by using the well data, the information brought
by an analog outcrop study (Salt Wash Member, Morrison Formation of
Utah) and the geological knowledge of the area (mainly source and
direction of the sand supply). These maps provide also an average Net to
Gross value (facies percentages) for each stratigraphic unit that is
used as a stop criterion during the shale modeling process. Shale
objects fill a stratigraphic unit until the Net to Gross ratio reached
the value given by the stop criterion. A shale object-based (shale are
objects and sands are background) stochastic approach is applied to
SINCOR clastic reservoir to insure a better modeling of shale extensions
(potential vertical barriers) and a better conditioning of well data
including horizontal wells (Figure 3). At this stage, a huge 3D
stochastic facies model (7,300,000 cells) that honors the wells but does
not fully describe the reservoir heterogeneity is available. To enhance
the static reservoir description prior to perform any production history
match a large effort has been made to integrate all available dynamic
information from well tests, fiber optic and production data. In
practice, cells of shale (within the model) and 2D Net to Gross maps are
manually edited to match the fiber optic interpretations, the well test
interpretations, and the well producing behaviors. These adjustments,
when giving satisfactory results, are considered as a deterministic
input that conditioned the modeling (as well data) whatever the
simulation is.
The aim of a
geological reservoir model is to provide a complete set of continuous
reservoir parameters (i.e. porosity, permeability and water saturation)
for each cell of the 3D grid. Many different techniques can be used to
generate these parameters. After several attempts and several loops
between reservoir geology and reservoir engineering, some modeling
techniques have been selected and implemented.
The porosity is
determined stochastically within each lithological facies (backbone for
calculating petrophysical parameters). As porosity modeling is
concerned, no seismic attributes that may be used as a predictor have
been identified. Therefore, the 3D distribution of the porosity is based
on the vertical well profiles (Figure 4). The well data are transformed
(normal score) so they are approximately Gaussian distributed. The
Gaussian model is characterized by various statistical parameters, which
reflect the spatial variability of the porosity. A standard deviation,
to specify the local scale spatial variability and a variogram, to
specify the local scale variability, are defined. The variogram
parameters indicate to what degree the measured porosity values in a
position can impact the unobserved porosity values in a position nearby.
First, a sequential screening algorithm simulates one realization of an
unconditional Gaussian field. The grid cells that correspond to well
trajectories are each assigned to a value corresponding to the measured
upscaled well logs. The cells with the values for the well trajectories
are ‘‘merged’’ with the unconditional Gaussian field. This is done by
standard kriging techniques and results in a conditional Gaussian field
that honors the well logs, standard deviations, and variograms
specified.
Permeability is
considered to be a function of porosity. A set of equations was derived
from well tests and porosity logs and used to populate the 3D grid. This
method for deriving permeability distribution gives satisfactory results
compared to the simple kriging of the well test data.
Water Saturation is
calculated based on distributions related to porosity ranges. The full
range of porosity has been divided in five arbitrary classes (e.g.,
between 20% and 25%) in which associated Water Saturation values from
logs were statistically analyzed. It turned out that within each
porosity class, related Water Saturation data fit a log normal
distribution. These distributions condition perfectly the modeling of
the Water Saturation for each porosity class. This fast and simple
method allows the generation of a consistent water saturation
distribution that respects a realistic degree of correlation between
porosity and saturation. Besides, the method is perfectly suited for the
unconsolidated sands of SINCOR where there is no transition zone (nil
capillary pressures). With this simple technique, the distribution of
the saturation is performed in one step for all the reservoir cells that
are located above the Oil-Water Contact (OWC). The definition of this
hydrocarbon contact is complicated due to the small density contrast
between the oil (0.965) and the water, the occurrence of a flushed zone,
the heterogeneous character of the reservoir, and poor well borehole
conditions that negatively impact on the acquisition of reliable
geological data. The definition of the OWC is therefore possible by a
complete and simultaneous analysis of different types of data:
conventional logs, resonance magnetic logs, wireline tester pretests,
drill stem tests, and cores.
Building a coherent 3D geological model of a complex heavy oil field,
with 2 years of production is a tremendous task that requires a
multi-disciplinary organization and approach, by an integrated team, at
each stage of the process. To improve the description of the reservoir
heterogeneity, all the available static and dynamic inputs must be
introduced in both detailed lithological grid and petrophysical grids
that are intimately related. Any modifications to match the dynamic
constraints are discussed by the concerned team and introduced into the
fine 3D geological grid as deterministic inputs. The integration of such
inputs enhances considerably not only the static reservoir description
but also the consistency of the future reservoir model which will better
reproduce the observed field performance and facilitate at a latter
stage the production history matching process.
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