The geometry of fluvial channel bodies: Empirical characterization and implications for object-based models of the subsurface

The distribution of channel deposits in fluvial reservoirs is commonly modeled with object-based techniques, constrained on quantities describing the geometries of channel bodies. To ensure plausible simulations, it is common to define inputs to these models by referring to geologic analogs. Given their ability to reproduce complex geometries and to draw upon the analog experience, object-based models are considered inherently realistic. Yet this perceived realism has not hitherto been tested by assessing the outputs of these techniques against sedimentary architectures in the stratigraphic record. This work presents a synthesis of data on the geometry of channel bodies, derived from a sedimentologic database, with the following aims: (1) to provide tools for constraining stochastic models of fluvial reservoirs in data-poor situations, and (2) to test the intrinsic realism of object-based modeling algorithms by comparing characteristics of the modeled architectures against analogs. An empirical characterization of the geometry of fluvial channel bodies is undertaken that describes distributions in (and relationships among) channel-body thickness, cross-stream width, and planform wavelength and amplitude. Object-based models are then built running simulations conditioned on six alternative, analog-informed parameter sets, using four algorithms according to nine different approaches. Closeness of match between analogs and models is then determined on a statistical basis. Results indicate which modeling approaches return architectures that more closely resemble the organization of fluvial depositional systems known from nature and in what respect. None of the tested algorithms fully reproduce characteristics seen in natural systems, demonstrating the need for subsurface modeling methods to better incorporate geologic knowledge

Cluster defined sedimentary elements of deep-water clastic depositional systems and their 3D spatial visualization using parametrization: a case study from the Pannonian-basin

Many multivariate statistical techniques have the ability to handle large data sets or a great number of parameters. Therefore, these multivariate statistical approaches are widely used in clastic sedimentology for facies analysis. Furthermore, most of the techniques which try to separate more or less homogeneous subsets can be subjective. This subjectivity raises several questions about the significance and confidence of clustering. The goal of this study is to optimize clustering and to evaluate the proper number of clusters needed in order to describe sedimentary and lithological facies through common characteristics. Also, with the interpretation of the clusters, the parametrized geometry adds further but quasi-subjective information to a 3D geologicalmodel. Two assumptions must be met: (1) well-definable geometries must correspond to the architectural elements (2) it is assumed that exactly one sedimentary or lithological facies belongs to each structural element and the flow properties are determined by these structural elements. This approach was applied to the clastic depositional data from a Miocene hydrocarbon reservoir (Algyő field, Hungary) to demonstrate the fidelity of the clustering method yielding an optimum of five cluster facies. The revealed clusters represent lithological characteristics within a (delta fed) submarine fan system. The paper deals with two stressed clusters in particular, showing sinusoid channels which were recognizable and measureable using parametrisation.</p

Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

A large borehole lithology dataset from the shallowly buried alluvial aquifer of the Brenta River Megafan (NE Italy) is used in this paper to model hydrofacies with three classical geostatistical methods, namely the Object-Based Simulation (OBS), the Sequential Indicator Simulation (SIS), and the Truncated Gaussian Simulation (TGS), and rank alternative output models. Results show that, though compromising with geological realism and rendering a noisy picture of subsurface geology, the pixel-based TGS and SIS are better suited than OBS for their ease of conditioning to closely spaced boreholes, especially in fine-scale simulation grids. In turn, SIS appears to provide better prediction and less noisy hydrofacies models than TGS without requiring assumptions about relationship among operative facies, which makes it particularly suited for use with large borehole lithology datasets lacking detail and quality consistency. Flow simulation on a test volume constrained with numerous boreholes indicates the SIS hydrofacies models feature well-connected sands forming relatively fast flow paths as opposed to TGS models, which instead appear to carry a more dispersed flow. It is shown how such a difference primarily relates to &lsquo;noise&rsquo;, which in TGS models is so widespread to translate into a disordered spatial distribution of K and, consequently, a nearly isotropic simulated flow

Improved Conditioning to Hard, Soft and Dynamic Data In Multiple-Point Geostatistical Simulation

RÉSUMÉ Dans cette dissertation, nous présentons trois méthodes visant à corriger autant de problèmes observés dans les simulations géostatistiques basées sur des statistiques multipoint (MPS). Le premier problème est le conditionnement aux données exactes (hard data) des algorithmes MPS par morceaux (patch-based). Le second problème est l’utilisation efficace de données auxiliaires (soft data) dans le MPS. Le dernier problème est la calibration des réalisations de faciès par MPS à des données dynamiques. Bien que le premier problème soit particulier au MPS par morceaux les deux autres sont communs à toutes les variantes de MPS ainsi qu’aux autres méthodes de modélisation des faciès. Dans une simulation MPS de variables catégoriques les données exactes trouvées dans le voisinage de recherche du point à simuler souvent ne correspondent à aucun des patrons disponibles dans l’image d’entrainement (TI). La solution habituellement utilisée est alors d’ignorer les points du voisinage les plus éloignés jusqu’à ce que le patron soit retrouvé dans la TI. Nous proposons plutôt l’utilisation de TI alternatives (ATI) permettant d’enrichir la base de données des patrons. Les ATIs sont obtenues par simulation non-conditionnelle (MPS par morceaux) à partir de la TI originale (OTI). Parmi toutes les ATI générées, certaines seulement sont sélectionnées en fonction des structures observées et des statistiques présentes dans ces ATI par rapport aux statistiques et aux structures des OTI. On vérifie également que chaque ATI apporte suffisamment de patrons présents dans les données exactes observées. Les ATIs qui ne sont pas assez riches en patrons observés ou qui ne sont pas statistiquement similaires à l’OTI, ou qui ont un contenu structurel différent de l’OTI sont rejetées. Les ATIs sélectionnées et l’OTI sont ensuite transmises à la boucle principale de simulation. Le nombre et la taille des ATIs sélectionnées peuvent être aussi grands que souhaité pourvu que les temps de calcul demeurent réalistes. Nous avons testé l’approche sur plusieurs TI différentes, catégoriques et continues, en 2D et en 3D. Nos résultats montrent que l’utilisation des ATIs améliore le conditionnement aux données exactes, améliore la reproduction de la texture des TI et permet de simuler sur de grandes grilles même à partir de petites OTI----------ABSTRACT In this dissertation, we present three methodologies to correct three problems observed in geostatistical simulations based on multiple-point statistics or MPS. The first problem is the conditioning to hard data of patch-based algorithms. The second problem is the efficient use of auxiliary data in patch-based MPS. The last is the calibration of facies realizations to dynamic data. The first problem is particular to patch-based MPS while the second and third are common between not only MPS approaches but also other facies modeling methods. In an MPS simulation of categorical variables, hard data found within the search neighbour-hood of simulation point often do not match exactly any of the patterns available in TI. One common solution to this problem is to drop out farther nodes until a matching pattern is found in TI. We propose instead using Alternative TIs (ATI) to enrich the pattern database. ATIs are mainly unconditional patch-based simulations based on original TI (OTI). Among the ATIs generated, some are selected based on the structures observed and their statistical features (histogram and variogram) compared with those of OTI. Their pattern databases are examined for the frequency of matching patterns with existing hard data configurations in simulation grid. ATIs that are not rich enough (as measured by number of matches for the hard data), not statistically similar to OTI, or with different structural content from OTI are discarded. The selected ATIs and OTI then are passed onto the main simulation loop. ATIs can be considered of any size and number as long as they are not computationally prohibitive for MPS simulation. We have tested the idea over several 2D and 3D TIs for categorical and continuous variables. Our test results show that using ATIs enhances the conditioning capa-bilities, improves the texture reproduction, and allows simulating over large grids even using much smaller OTIs