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PATTERN‐BASED GEOLOGICAL MODELING OF DEEP‐WATER
CHANNEL DEPOSITS IN THE MOLASSE BASIN, UPPER AUSTRIA
Lisa Stright
Graduate Program in Earth, Energy and Environmental Sciences, Stanford University,
Stanford, CA 94305, USA
ABSTRACT Recent advances in reservoir modeling have made it possible to include
interpretative geologic information into reservoir models, thereby generating
more geologically realistic models conditional to hard (well log and core) and
soft (seismic) data. In particular, pattern‐based modeling algorithms borrow
patterns from a training image and place them in the model space at locations
determined by local data. Not only does the training image provide the avenue
to include more interpretative geologic information into geologic models, it also
allows added control in areas of sparse and low quality data.
A workflow of a pattern‐based modeling study is presented for the
Puchkirchen field in the Molasses Basin in Austria. Multiple alternative
numerical models are generated for this channelized deep‐water depositional
setting, utilizing exploration‐scale seismic. Results show successful integration
of local data (well and seismic) with patterns from a training image reflecting an
extensive sedimentological study in the basin
INTRODUCTION The goal of the geological modeling process is to build numerical models
that will reliably predict reservoir properties for well planning and/or fluid flow
performance prediction. A typical modeling workflow will attempt to convert a
geologist’s conceptual image of the reservoir to a numerical representation that
honors both hard (well and core) and soft (seismic and production) data collected
from the reservoir. However, historically available tools such as covariance‐
based (2‐point), Gaussian‐based, geostatistical methods and Boolean methods,
limit the modeler’s ability to include realistic interpretive geologic information
into reservoir models while honoring hard and soft data. Traditional 2‐point
geostatistical simulation approaches are excellent at integrating multiple types of
diverse data, such as well and seismic data. However, given that the spatial
distribution is controlled only by a variogram model and local probabilities are
derived from low resolution seismic, these methods are unable to reproduce the
geometry of complex geologic structures and their spatial relationship, much less
the sub‐seismic scale geologic patterns. The results from these methods often
lack realistic geologic spatial relationships and appearance. Object‐based
methods generate models that contain complex geologic structures, but cannot
integrate diverse data and the parameterization and programming of the object
relationships are often difficult.
Advances in pattern‐based modeling algorithms (Strebelle, 2000; Arpat,
2005; Zhang, 2006) have made it possible to reproduce the geometry of complex
geologic structures while conditioning to the diverse suite of soft (seismic) and
hard (well log and core) data typically encountered in petroleum reservoirs.
Pattern‐based modeling techniques provide the avenue to include qualitative
interpretative information into reservoir models through training images, thus
allowing the geologist more control to include important descriptive
information. The training image is the basis of a shifting modeling paradigm;
from covariance‐based statistical methods toward integration of descriptive
geological interpretations with numeric field data.
A demonstration of a pattern‐based modeling workflow is presented for
the Puchkirchen field in the Molasse Basin in Austria. This paper proposes a
workflow that combines coarse‐scale seismic attributes defining interpretive
regions and local probabilities, with pattern‐based interpretation of well data
defining the sub‐seismic scale facies. The algorithms produce multiple
alternative numerical models of channel fills, all drawing spatial facies
distributions from the training image and all locally constrained to well, core and
seismic data.
The long term goal of this research project is to leverage data from areas
sampled by well and core data to predict, away from well control, the facies
distribution using the seismic data as a local anchor. The components of this
research project are patterned facies interpretation in wells, quantitative and
qualitative seismic interpretation defining regions and facies probabilities from
rock physics analysis, and the building of a training image depicting 3D pattern
shapes and relationship. This paper presents a workflow to tie these three
components together into a comprehensive numerical model that honors locally
the available data while incorporating expert geologic interpretation.
THE PUCHKIRCHEN FIELD, MOLASSE BASIN The Molasse Basin is one of the largest hydrocarbon producing basins in
Austria. The basin contains a large deep‐water channel belt (3‐6 km wide by
more than 100 km long) which is confined within an elongate foreland trough
(DeRuig and Hubbard, 2006). Multiple gas fields have been discovered in
association with the channel belt. The Puchkirchen field is one such discovery
(Figure 1), owned and operated by Rohöl‐Aufsuchungs A.G. (RAG). It has
produced 30 Billion m3 (~1 TCF) of gas since the late 1960’s.
Figure 1 Molasse Basin showing the Puchkirchen and Atzbach gas fields (modified from Hubbard, 2006). Red outline shows location of the seismic map shown in Figure 3.
The channel belt in the Puchkirchen field area (in the Upper Puchkirchen
formation) is approximately 1000 meters thick and contains four zones; A1 to A4
of the Late Oligocene and early Miocene (Figure 2). The A1 is unconformably
overlaid by the basal Hall formation and the A4 lies unconformably above the
lower Puchkirchen formation. The channel belt extends into the lower
Puchkirchen formation. The channel belt is composed of turbiditic
conglomerate and sandstone deposits, as well as slump and debris‐flow deposits.
Extensive drilling over the last 30+ years has focused on the structural
highs along the channel belt. Exploration focuses now on more subtle
stratigraphically trapped reservoir sands along the channel belt and associated
overbank deposits. Sedimentological studies aid in this exploration. For
example, Hubbard (2006) found that the thin gas sands reservoirs are most
commonly encountered at the top of thick, fining‐upward conglomeratic channel
fill sequences structurally trapped within the channel belt.
Figure 2 Stratigraphy of the Upper Austrian Molasse basin. Focus of this study is on the A1 and A2 reservoir zones. (modified by Hubbard 2006 after Zweigel at al. 1998)
The Puchkirchen field, one of the largest gas reservoirs in the Molasse
basin, is a structurally trapped sandstone reservoir bounded by a stratigraphic
“shale‐out” to the north and an aquifer to the south (Figure 3). The reservoir
sands in the Puchkirchen field are located in the A1 and A2 zones and are on
average 1‐20 meters thick in the A1 zone and 1‐5 meters thick in the A2 zone.
The A2 zone wells watered out after a short period of gas production. In 1982,
the Puchkirchen field in the Upper Puchkirchen Formation was converted to gas
storage.
Figure 3 Outline of the Puchkirchen field (area shown in Figure 1) on a coherency map showing the Puchkirchen Channel Belt, after DeRuig and Hubbard, 2006. Vertical line shows cross section of modeling area.
Hubbard and DeRuig (2006) characterized the facies associations
throughout the Molasse basin to better understand the sedimentological and
stratigraphical distribution of deposits associated the seismically mapped
channel belt. They utilized extensive seismic, well log and core data to interpret
four main depositional facies; channel belt thalweg, overbank wedge, overbank
lobe and tributary channel (Figure 4). Hubbard (2006) further defined facies and
facies associations.
Figure 4 Schematic depositional model of the Molasse Basin during deposition of the Upper Puchkirchen Formation showing the distribution of depositional elements defined by De Ruig and Hubbard (2006). The axial channel belt is 3‐5 km wide.
Challenges in Reservoir Characterization The regional‐scale channel belt is seismically mappable due to the strong
impedance contrast between the conglomerate channel fill and surrounding non‐
channel facies. However, due to the overwhelming amplitude signature of the
channel‐fill conglomerates and the thin‐bedded nature (<1.5m thick) of the
reservoir sands, the internal reservoir sandstones cannot be deterministically
identified from the seismic volume. The sands are not only below seismic
resolution, but they have similar impedances to shales and siltstones. The
delineation of gas sands is difficult because their impedance values are similar to
soft shales.
Traditional well log correlation is challenging even with abundant well
data due to the chaotic nature of the intra‐channel deposits. Interwell variability
limits the reliability of this deterministic approach for understanding reservoir
distribution and connectivity. Geostatistical modeling is often used to address
these challenges; however, previous studies within the Molasse Basin have
shown that there is little statistical correlation between reservoir facies and
seismic data. Sedimentologically‐based studies have historically yielded more
predictive results than seismic‐based geostatistical methods (van Alebeek, 2000).
Study Goals Extensive sedimentalogical studies by Hubbard (2006) and Hubbard and
DeRuig (2006) characterized regional‐scale seismic facies calibrated to core and
well logs within the framework of a sedimentological analysis. The focus of this
study is on reservoir‐scale, sub‐seismic facies and it will be performed in
collaboration with Anne Bernhardt (Bernhardt, 2006) from the Stanford Project
on Deep‐water Depositional Systems (SPODDS) research consortium. Bernhardt
will perform a detailed sedimentologic characterization of the Puchkirchen
(channel belt deposits) and Atzbach (overbank deposits) fields from which
predictable and systematic stacking patterns will be determined. Sediment
distribution and lateral extension of reservoir facies will be investigated in detail
with the help of numerous core, well logs and seismic data.
Bernhardt’s conceptual architecture of stacking patterns and lateral
correlations of the sub‐seismic channel deposits will be translated into spatial
distributions of 3d patterns in the form of numerical training images. These
patterns collected as 3d templates will be used in pattern‐based geostatistical
simulation where they will be patched into numerical models anchored to
available local data. The resulting numerical models should match well (log and
core), seismic and production data to the extent that these data inform the
reservoir model. The final models can be used as a foundation for forward
seismic modeling to predict channel belt deposits away from the well control and
to aid in discovery of new exploration opportunities.
The Puchkirchen field offers a unique opportunity to perform this study
due to the abundance of available data of diverse types. Within the Puchkirchen
field, twenty‐six wells have been drilled, all of which have been logged. 318
meters of core (in the A1 – A3 intervals) have been collected in thirteen of the
wells through reservoir and non‐reservoir intervals. A full 3D seismic survey
covers the Puchkirchen field.
Prior to incorporating Bernhardt’s ongoing research, the initial
methodology for reservoir modeling will be based upon Hubbard’s (2006)
interpretation.
MODELING THE PUCHKIRCHEN CHANNEL BELT DEPOSITS Due to the sub‐seismic nature of the channel fill facies, training images
must be used to integrate sub‐seismic conceptual interpretations into the
reservoir models. The following initial steps aim at developing the framework
and methodologies based on existing data and interpretations. These will be
updated as new interpretations become available. A 2‐dimensional section, the
location of which is shown in Figure 3, is used to illustrate the proposed
workflow. The input to this workflow are the depositional interpretation
through the training image and the conditioning well (hard) and seismic data (as
hard regions and soft probabilities).
A Channel‐Fill Training Image The training image can come from a prior geologic concept, analog
reservoirs, and/or analog outcrop information (Strebelle, 2000). This geologic
concept is qualitative in nature and can be obtained from interpreted
sedimentological and stratigraphical facies relationships. The training image
attempts to incorporate the relative shape, dimensions and spatial association
between facies into a numerical model depicting the distributions of facies
deemed prevalent in the actual reservoir.
The basis for a training image for the Puchkirchen channel belt deposits is
shown in Figure 5 where Hubbard suggest two orders of channels and overbank
deposits within the channel belt; inner ribbon channels and inner levees. The
ribbon channels are characterized by upward fining sequences with a basal
conglomerate, intermediate sandstone and capping shale. The inner levee is
characterized by thinly bedded sand/shale sequences. Finally, the background
facies are a product of mass transport complexes (debris flows, slumps and
slides) and are non‐reservoir. Generally, the reservoir facies are the sands
contained in the upward fining sequences.
Figure 5 Vertically exaggerated schematic cross‐section through the Puchkirchen channel belt showing the hierarchical organization of channel and inner levee elements. Diagram not to scale: channel belt width ~ 5 km and overbank wedge height ~ 250 m. Interpretive model used as a basis for the Puchkirchen field training image. (Hubbard 2006)
A numerical training image model was generated based on this
conceptual framework. The channel‐levee training image model was generated
using SBEDTM, a surface‐based modeling package. The Training Image, Figure 6,
was generated attempting to depict the inner channel‐levee model shown in
Figure 5. This channel‐levee model was generated using the following
parameter ranges:
Channel width: 1 – 2 km
Channel thickness: 40 – 60 m
Amplitude: 0.5 – 1.5 km
Wavelength: 16 – 22 km
NTG Ratio 30 – 40%
Figure 6 Example training image generated in SBEDTM
Because a training image is purely conceptual, the facies in a training
image need not be locally accurate. Matching of the data is obtained during the
pattern‐based simulation where shapes and structures are drawn and patched
onto the reservoir model such as to match local data. The training image allows
added control in environments of sparse and low‐quality data. Additionally,
due to interpretation uncertainty associated with limited sampling, alternative
training images should be generated to account for multiple geological
interpretations.
Conditioning Data
Seismic Data Seismic attributes are often used to locate facies within reservoir models,
once a statistical correlation between hard data (wells and core) and the seismic
data has been established. This correlation is converted to local probability of
occurrence, for example, the probability of sand as derived from acoustic
impedance. Statistical rock physics analysis is used to discern the seismic scale
and sub‐seismic scale relationships between facies and acoustic properties. This
work is currently being undertaken and is further discussed in the future
research section. Until these attributes are available, seismic data was included
by transferring Hubbard’s channel belt interpretation (Figure 7) to a numerical
region model. Interpretive probabilities for ribbon channels, inner levees and
background facies were also generated.
Figure 7 Channel migration and overbank lobe deposits in the Puchkirchen Formation. Line‐drawing interpretation overlain on seismic cross‐section, red box outlines the study area. The interpretation was made by overlying the wireline logs on top of seismic line and using prominent seismic reflectors to guide correlations (after Hubbard, 2006).
Figure 8 shows the interpretive seismic regions with dark yellow
signifying the active region and gray the inactive region. During the pattern‐
based simulation, patterns from the training image are simulated only in the
active region. The inactive region represents a hiatus in deposition of ribbon
channels and inner levees and therefore no pattern simulation is performed in
this region.
Figure 8 Interpretive seismic regions for geostatistical simulation. Dark yellow is an active region for the ribbon channel fill. Grey is an inactive region.
Seismic attributes are then used at a smaller scale to control where, within
the active regions, individual facies are to be simulated. Figure 9 shows
probabilities for ribbon channel, inner levee and background facies. These
probabilities were generated by digitizing a centerline of ribbon channel
deposition and migration within the active regions and consistent with the
interpreted well data (next section). The digitized centerlines were then
smoothed, manually edited and scaled until the mean probabilities were roughly
equal to the desired proportion of facies (e.g. mean ribbon channel probability
was 20%, inner levee probability 30% and background was 50%; equal to the
desired proportions).
Currently, as described, these probabilities are interpretive and synthetic,
and were generated to demonstrate the modeling process. However, these
probabilities will be generated in the future directly from the actual seismic data.
Figure 9 Synthetic Facies Probabilities used to demonstrate the workflow. Probabilities of A)
ribbon channel, B) background shale, and C) thin‐bedded levee facies.
A
B
C
Well Data Two wells, shown in Figure 10, were used for this modeling
demonstration. These wells are actual wells (Reichering 1, left, and Wegsheid
002, right) that were selected due to their proximity to Hubbard’s interpreted
section. Ribbon channel (upward fining sequences), levee (thin‐bedded
sand/shale sequences) and background (shale) facies were interpreted in these
two wells from Gamma Ray wireline logs and the interpreted values are shown
in the log on the right in Figure 10 while the log on the left is the Gamma Ray
log.
Figure 10 Hard Data from two wells for pattern‐based modeling with Gamma Ray log shown on the left and interpreted ribbon channel (upward fining sequences), levee (thin‐bedded sand/shale sequences) and background (shale) on the right.
Workflow The interpretative seismic information as active/inactive regions (Figure 8)
and ribbon channel, inner levee and background probabilities (Figure 9) are used
as a framework for the pattern‐based simulation. The pattern‐based simulation
process anchors patterns lifted from the training image to locations indicated by
the well data with guidance given by the seismic regions and local probabilities.
The results herein show alternative (stochastic) models obtained from a single
REICH1 W002
training image (Figure 6). The Filtersim algorithm (Zhang, 2006) was used to this
purpose.
The Filtersim workflow (Figure 11) for generating a pattern‐based
simulation is to first break the training image into many smaller pieces based on
a given input template size. For example, given a training image with 150x150
cells, an input template 15x15 could be used to pre‐scan that training image for
all unique 15x15 patterns. Pre‐set or user defined custom filters (Figure 12) are
applied to these template‐sized patterns and patterns are grouped into categories
based on their filter scores. All of the patterns within a category are combined to
create a single prototype that is representative of that category.
Once the training prototypes are obtained, a random path is generated
which visits all of the nodes of the numerical model. The center node of the
template is placed at the first node in the random path. Any data within that
template (previous simulated nodes, hard data, and/or soft data) define a data
event. This data event is then compared to each prototype to find the prototype
that is most similar to the data event. A pattern from the prototype category is
then randomly chosen and pasted onto the simulation grid. The process is
repeated by stepping to the next node in the random path.
Figure 11 Filtersim workflow (after Zhang 2006)
Figure 12 Six 2D directional filters (after Zhang, 2006).
Ten simulations were generated using Filtersim and the previously
described workflow. Figure 13 show ten alternative models all matching the
hard conditioning data. Target proportions were 20% for the ribbon‐channel,
30% for the inner levee, and 50% for the background facies. In general, the inner
levee facies proportion was under simulated and the ribbon channel facies over
simulated. Referring back to Figure 10, the proportions in the original well data
(55% for the ribbon‐channel, 30% for the inner levee, and 15% for the background
facies) may be skewing the simulation results. In this particular case, honoring
target proportions exactly is not imperative, as the global proportion of each
facies is a significant unknown in the reservoir due to preferential sampling of
ribbon channels.
Figure 13 Results from ten alternative Filtersim Pattern‐based simulations. Note the consistency with the hard data and the final model proportions. Target proportions were: Ribbon‐channel 20%, Inner Levee 30%, and Background 50%.
SUMMARY This paper outlines a process for adding interpretative sedimentology into
pattern‐based reservoir modeling. A preliminary conceptual training image
model is proposed and an outline for the modeling process explained. Multiple
equally probable simulated realizations were generated from Filtersim to
demonstrate the modeling process.
FUTURE WORK The modeling framework presented in this paper lays the groundwork for
future work. A simple training image was presented and used in a pattern‐based
simulation anchored to local seismic attributes and well data. Future work will
include training image expansion to allow for the inclusion of multiple
interpretations, a more detailed statistical rock physics analysis to determine
depositional processes at the seismic‐scale. The aim is to quantitatively frame
seismic‐scale facies modeling down to sub‐seismic, reservoir‐scale, facies based
on stacking patterns from core and outcrop observations.
Training Image Expansion Due to the uncertain nature of our interpretation of reservoir deposits
based on incomplete sets of information, multiple interpretations with varying
levels of complexity should be generated. These alternative interpretations are
then included in the pattern‐based modeling process through multiple training
images with varying levels of complexity. The example training image presented
in Figure 6 is preliminary and very simple, and additional depositional elements
might exist within the channel belt. When attempting to predict reservoir
characteristics (sand distribution and associated reservoir trap) within the
channel belt, it is important to include depositional elements that control
potential reservoir deposits. These deposits are a result of the deepwater
depositional processes associated with the channel belt. In this case, controlling
processes are turbidity currents and mass transport events (debris flows, slumps
and slides). This section outlines some of these processes.
Inner Lobes: Terminal, Transient and Overbank As turbidity currents transport sediment they may spill over the inner
levee of the ribbon channel creating an overbank lobe, a terminal lobe as the flow
wanes, or, if there are topographic lows along the floor of the channel belt, a
transient lobe might be generated as sediment drops out of suspension
(Adeogba, 2005). To account for such processes, three types of potential inner
lobes training images could be included; Terminal, Overbank and Transient
lobes as shown in Figure 14A, B, and C, respectively, where the ribbon channel is
shown in yellow and the lobe position and geometry shown in green.
Figure 14 Types of Inner lobes to add to training image. A) Terminal Lobe, B) Overbank Lobe, and C) Transient Lobe
To include these types of facies in a geostatistical simulation, ribbon
channels could be simulated first and their locations frozen. Next, lobes are
simulated attached to these channel locations. This concept of hierarchical
simulation was introduced by Maharaja (2004) and allows large flexibility in the
simulation of multi‐scale objects.
Debris Flows, Slumps and Slides Other training image elements controlling where the ribbon‐channels and
therefore reservoir facies are deposited, are debris flows, slumps and slides
(DeRuig and Hubbard, 2006). Each of these events could interrupt and
potentially even divert the ribbon channel deposition. Debris flows could
potentially span the entire width of the channel belt and blanket existing ribbon‐
channel incision. A new ribbon‐channel would then re‐establish itself over the
debrite (deposit from a debris flow). Slumps and slides could be of varying size
and shape, but they are subject to rules in relation to the inner channel and inner
overbank deposits. That is, the slumps and slides would 1) erode into previously
deposited channel belt facies, 2) control the location of the deposition of future
channel belt facies deposition, and 3) be eroded by future channel belt
deposition. Uncertainties associated with slumps and slides are, size, location,
amount they erode when deposited and how much they are eroded after they are
deposited. These uncertainties can be investigated by stochastic modeling
process.
Ribbon‐Channel Orientation The ribbon‐channel orientation changes within the channel belt as the
channel migrates and fills the basin with time. To capture such orientation
change, a ribbon‐channel orientation cube can be used to control the orientation
of the training image and therefore the pattern placement during simulation. An
example of such a cube is shown in Figure 15. The main axis of the channel belt
is shown transitioning from N35E degrees up to N15E. This volume was
generated from an interpretation of the channel belt location across the volume at
reservoir levels A1 and A2 (top to bottom).
Figure 15 Main channel belt rotation cube (left) based on an interpretation of the channel belt migration from the A2 to the A1 channel level from the seismic data (right).
Rock Physics Modeling Characterization of reservoir facies in the Puchkirchen channel belt is
challenging due to the overwhelming amplitude signature of the non‐reservoir
channel‐fill conglomerates and the thin‐bedded nature (<1.5m thick) of the
reservoir sands. The internal reservoir sandstones cannot be deterministically
interpreted from the seismic volume because they are below seismic resolution.
Additionally, as shown in Figure 16, the reservoir sands (gas sands in pink and
water sands in yellow) have similar impedance contrasts as shales and siltstones
from the conglomerates. The delineation of reservoir sand is further complicated
because their impedance signature do not discriminate gas sand from soft shale
(shown in red).
-35
-15
Figure 16 Vp/Vs vs. P‐wave Acoustic Impedance with representative cores of a) muddy matrix conglomerate, b) sandstone, c) basal conglomerate transitioning upward into a muddy matrix conglomerate (potential slurry flow), and d) clast supported conglomerate.
However, all conglomerates are not the same and it may be possible to
differentiate reservoir versus non‐reservoir associated conglomerates in the
seismic data. P‐Impedance histograms in Figure 16 show that there may be
multiple populations of conglomerates lumped into the single category
“conglomerate” (green) as evidenced by a potential tri‐modal histogram
character. Additionally, the shale histogram (blue) shows a potential bi‐modal
distribution. The current hypothesis is that three populations of conglomerates
are expressed in the statistical distributions; clast supported sandy matrix
conglomerates (deposits from high density turbidity currents), muddy matrix
conglomerates (debris flow conglomerates), and transitional conglomerates
(slurry flow deposits) (Lowe, 1982). Clast supported, sandy matrix
conglomerates could have spatially linked clean sands due to the depositional
process of high density turbidity currents. For example, as described by
Hubbard, the ribbon channels are delineated by a basal conglomerate, sandstone
and shale upward fining sequence. This basal conglomerate would most likely
be a clast supported, sandy matrix conglomerate.
Figure 17 Ip and Is Histograms for the A1 reservoir Interval with overburden and underlying conglomeratic units
Finally, Figure 16 and Figure 18 both show a potential transition of
conglomerates type from sandy to shaley with decreasing porosity. Figure 16
shows a decrease in acoustic impedance from conglomerates to shale, potentially
signaling a transition from clast‐supported to muddy‐matrix conglomerate.
Figure 18 reveals that indeed there is a separation between shales, sands and
conglomerates and that the conglomerate population overlaps both sand and
shale populations.
Figure 18 P‐wave velocity (Vp) versus total porosity for the Puchkirchen color coded by Facies (left) and theoretical cementing and sorting trends for comparison (Mavko, 2006) (right).
Sub‐Seismic Facies Modeling The modeling presented herein is at the limits of the seismic scale. Active
ribbon‐channel depositional regions were interpretively generated and potential
use of soft probabilities for ribbon‐channel placement discussed. The focus of
this type of study is on reservoir facies and not on the larger scale depositional
elements. The depositional elements can, however, be a stepping stone to reach
the reservoir scale facies. For example, Figure 19 shows the concept of core
interpretation being superimposed on the pattern‐based simulation model within
the channel belt. Statistics, in the form of probability of stacking patterns and
histograms of bed thickness by facies, might be used to generate a finer scale
training image to be used for sub‐seismic pattern simulation. The Puchkirchen
field offers a unique opportunity to perform this study due to the abundance of
core data in reservoir and non‐reservoir facies.
Figure 19 Illustration of modeling sub‐seismic facies from vertical stacking patterns from core. Superimposed core interpretations are purely illustrative and do not correspond to the two wells shown.
ACKNOWLEDGEMENTS
We would like to thank Rohöl‐Aufsuchungs A.G. (RAG) for financial and
logistical support of this research, specifically, Richard Derksen for his
mentorship and valuable research insights. This work is continually improved
by review and personal communication with Dr. Stephen Hubbard, Dr. Stephen
Graham, Dr. Andre Journel, Dr. Tapan Mukerji, Dr. Gary Mavko and Dr. Don
Lowe.
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