PATTERN BASED GEOLOGICAL MODELING OF DEEP WATER CHANNEL DEPOSITS IN THE MOLASSE BASIN, UPPER AUSTRIA. Lisa Stright

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1 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

2 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 covariancebased (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

3 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

4 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 m 3 (~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

5 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

6 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, 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.

7 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 nonchannel 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.

8 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

9 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

10 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

11 using SBED TM, 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: Channel thickness: Amplitude: Wavelength: 1 2 km m km 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

12 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.

13 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.

14 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.

15 A B C Figure 9 Synthetic Facies Probabilities used to demonstrate the workflow. Probabilities of A) ribbon channel, B) background shale, and C) thin bedded levee facies.

16 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. REICH1 W002 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

17 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.

18 Figure 11 Filtersim workflow (after Zhang 2006) Figure 12 Six 2D directional filters (after Zhang, 2006).

19 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.

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21 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%.

22 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

23 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

24 potentially even divert the ribbon channel deposition. Debris flows could potentially span the entire width of the channel belt and blanket existing ribbonchannel 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).

25 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).

26 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

27 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

28 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.

29 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. REFERENCES Adeogba, A. A. A., 2005, Transient fan architecture and depositional controls from near surface 3 D seismic data, Niger Delta continental slope: AAPG bulletin, v. 89, p Arpat, G., 2005, Sequential Simulation with Patterns, PhD thesis, Stanford University, 166p. Bernhardt, A., and Lowe, D. R., 2006, Facies architecture of deep water deposits in the Upper Austrian Molasse Basin: Channel and overbank deposits in

30 the Puchkirchen and Atzbach gas fields: A preview., Stanford Project on Deep Water Depositional Systems Report Caers, J., Strebelle, S., and Payrazyan, K., 2003, Stochastic integration of seismic and geological scenarios: A submarine channel saga, The Leading Edge, pages DeRuig, M. J. and Hubbard, S. M., 2006, Seismic facies and reservoir characteristics of a deep marine channel belt in the Molasse foreland basin, Puchkirchen Formation, Austria: AAPG Bulletin, v. 90, no. 5 (May 2006), pp Hubbard, S.M., 2006, Deep Sea foreland basin axial channels and associated sediment gravity flow deposits, Oligocene Molasse Basin, Upper Austria, and Cretaceous Magellanes Basin, Chile, PhD Thesis, Stanford University, 204p. Lowe, D., 1982, Sediment gravity flows; II, Depositional models with special reference to the deposits of high density turbidity currents: Journal of Sedimentary Petrology, v. 52, p Maharaja, A., 2004, Hierarchical Simulation Of Multiple Facies Reservoirs Using Multiple Point Geostatistics, MS Thesis, Stanford Univserity, 32p. Mavko, G., 2006, Introduction to Rock Physics, Course Notes, Stanford Univerisity. Strebelle, S.. Sequential Simulation Drawing Structures from Training Images. PhD thesis, Stanford University, van Alebeek, H., 2000, (Geo)statistics on thin turbidite sandstones in the Upper Austrian molasse basin: Sediment 2000, Mitteilungen der Gesellschaft der Geologie und Bergbaustudenten in Oesterreich, vol. 23, p

31 Zhang, T., Switzer, P., and Journel, A., 2006, Filter based classification of training image patterns for spatial simulation, Mathematical Geology, vol. 38, no.1, pp