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  • 1
    Monograph available for loan
    Monograph available for loan
    GSLIB : Geostatistical Software Library and user's guide (1998)
    New York [u.a.] : Oxford Univ. Press
    Details Availability
    Call number: 19/M 02.0006 ; AWI S2-02-0077
    Type of Medium: Monograph available for loan
    Pages: X, 369 S. + 1 CD-ROM
    Edition: 2nd ed.
    ISBN: 0195100158
    Series Statement: Applied geostatistics series
    Classification:
    Mathematical Geology
    Language: English
    Location: Reading room
    Location: Reading room
    Branch Library: GFZ Library
    Branch Library: AWI Library
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  • 2
    Monograph available for loan
    Monograph available for loan
    Geostatistical reservoir modeling (2002)
    Oxford : Oxford University Press
    Details Availability
    Call number: M 11.0189
    Description / Table of Contents: Contents:Preliminary Statistical Concepts; Gridding Reservoir Layers; Quantifying Spatial Correlation; Preliminary Mapping Concepts; Cell-Based Facies Modelling; Object-Based Facies Modelling; Porosity and Permeability Modelling; Simulated Annealing; Uncertainty Management; Special Topics.
    Type of Medium: Monograph available for loan
    Pages: XI, 376 S.
    ISBN: 9780195138061
    Series Statement: Applied geostatistics series
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 3
    Electronic Resource
    Electronic Resource
    Challenges in reservoir forecasting (1996)
    Springer
    Mathematical geology 28 (1996), S. 829-842 
    Details
    ISSN: 1573-8868
    Keywords: geostatistics ; flow simulation ; streamtubes ; scale up ; gridding ; accuracy and precision ; modeling uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract The combination of geostatistics-based numerical geological models and finite difference flow simulation has improved our ability to predict reservoir performance. The main contribution of geostatistical modeling has been more realistic representations of reservoir heterogeneity. Our understanding of the physics of fluid flow in porous media is reasonably captured by flow simulators in common usage. Notwithstanding the increasing application and success of geostatistics and flow simulation there remain many important challenges in reservoir forecasting. This application has alerted geoscientists and physicists that geostatistical/flow models in many respects, are, engineering approximations to thereal spatial distribution andreal flow processes. This paper reviews current research directions and presents some new ideas of where reserach could be focused to improve our ability to model geological features, model flow processes, and, ultimately, improve reservoir performance predictions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02066003
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  • 4
    Electronic Resource
    Electronic Resource
    Hierarchical object-based stochastic modeling of fluvial reservoirs (1996)
    Springer
    Mathematical geology 28 (1996), S. 857-880 
    Details
    ISSN: 1573-8868
    Keywords: marked point processes ; Boolean modeling ; geostatistical reservoir modeling ; coordinate transformation ; data conditioning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract This paper describes a novel approach to modeling braided stream fluvial reservoirs. The approach is based on a hierarchical set of coordinate transformations involving relative straingraphic coordinates, translations, rotations, and straightening functions. The emphasis is placed on geologically sound geometric concepts and realistically-attainable conditioning statistics including areal and vertical facies proportions. Modeling proceeds in a hierarchical fashion, that is (1) a stratigraphic coordinate system is established for each reservoir layer, (2) a number of channel complexes are positioned within each layer, and then (3) channels are positioned within each channel complex. The geometric specification of each sand-filled channel within the background of floodplain shales is a marked point process. Each channel is marked with a starting location, size parameters, and sinuosity parameters. We present the hierarchy of eight coordinate transformations, introduce an analytical expression for the channel cross-section shape, describe the simulation algorithm, and demonstrate how the realizations are made to honor local conditioning data from wells and global conditioning data such as areal and vertical proportions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02066005
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  • 5
    Electronic Resource
    Electronic Resource
    Deriving Constraints on Small-Scale Variograms due to Variograms of Large-Scale Data (1998)
    Springer
    Mathematical geology 30 (1998), S. 837-852 
    Details
    ISSN: 1573-8868
    Keywords: coregionalization model ; variogram inference ; cosimulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract The application of kriging-based geostatistical algorithms to integrate large-scale seismic data calls for direct and cross variograms of the seismic variable and primary variable (e.g., porosity) at the modeling scale, which is typically much smaller than the seismic data resolution. In order to ensure positive definiteness of the cokriging matrix, a licit small-scale coregionalization model has to be built. Since there are no small-scale secondary data, an analytical method is presented to infer small-scale seismic variograms. The method is applied to estimate the 3-D porosity distribution of a West Texas oil field given seismic data and porosity data at 62 wells.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1021726609413
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  • 6
    Electronic Resource
    Electronic Resource
    Foreword to Special Issue: Modeling Subsurface Flow (1999)
    Springer
    Mathematical geology 31 (1999), S. 747-748 
    Details
    ISSN: 1573-8868
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007535715379
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  • 7
    Electronic Resource
    Electronic Resource
    Kriging in a finite domain (1993)
    Springer
    Mathematical geology 25 (1993), S. 41-52 
    Details
    ISSN: 1573-8868
    Keywords: kriging ; ergodicity ; stratigraphic limits ; finite domain
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Adopting a random function model {Z(u),u ε study areaA} and using the normal equations (kriging) for estimation amounts to assume that the study areaA is embedded within a infinite domain. At first glance, this assumption has no inherent limitations since all locations outsideA are of no interest and simply not considered. However, there is an interesting and practically important consequence that is reflected in the kriging weights assigned to data contiguously aligned along finite strings; the weights assigned to the end points of a string are large since the end points inform the infinite half-space beyond the string. These large weights are inappropriate when the finite string has been created by either stratigraphic/geological limits or a finite search neighborhood. This problem will be demonstrated with numerical examples and some partial solutions will be proposed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00890674
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  • 8
    Electronic Resource
    Electronic Resource
    Entropy and spatial disorder (1993)
    Springer
    Mathematical geology 25 (1993), S. 329-355 
    Details
    ISSN: 1573-8868
    Keywords: spatial entropy ; Gaussian model ; simulation ; fluid flow
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract The majority of geostatistical estimation and simulation algorithms rely on a covariance model as the sole characteristic of the spatial distribution of the attribute under study. The limitation to a single covariance implicitly calls for a multivariate Gaussian model for either the attribute itself or for its normal scores transform. The Gaussian model could be justified on the basis that it is both analytically simple and it is a maximum entropy model, i.e., a model that minimizes unwarranted structural properties. As a consequence, the Gaussian model also maximizes spatial disorder (beyond the imposed covariance) which can cause flow simulation results performed on multiple stochastic images to be very similar; thus, the space of response uncertainty could be too narrow entailing a misleading sense of safety. The ability of the sole covariance to adequately describe spatial distributions for flow studies, and the assumption that maximum spatial disorder amounts to either no additional information or a safe prior hypothesis are questioned. This paper attempts to clarify the link between entropy and spatial disorder and to provide, through a detailed case study, an appreciation for the impact of entropy of prior random function models on the resulting response distributions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00901422
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  • 9
    Electronic Resource
    Electronic Resource
    Practical considerations in the application of simulated annealing to stochastic simulation (1994)
    Springer
    Mathematical geology 26 (1994), S. 67-82 
    Details
    ISSN: 1573-8868
    Keywords: conditional simulation ; cosimulation ; edge effects ; objective functions ; simulated annealing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Realizations generated by conditional simulation techniques must honor as much data as possible to be reliable numerical models of the attribute under study. The application of optimization methods such as simulated annealing to stochastic simulation has the potential to honor more data than conventional geostatistical simulation techniques. The essential feature of this approach is the formulation of stochastic imaging as an optimization problem with some specified objective function. The data to be honored by the stochastic images are coded as components in a global objective function. This paper describes the basic algorithm and then addresses a number of practical questions: (1) what are the criteria for adding a component to the global objective function? (2) what perturbation mechanism should be employed in the annealing simulation? (3) when should the temperature be lowered in the annealing procedure? (4) how are edge/border nodes handled? (5) how are local conditioning data handled? and (6) how are multiple components weighted in the global objective function?
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02065876
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  • 10
    Electronic Resource
    Electronic Resource
    Kriging with strings of data (1994)
    Springer
    Mathematical geology 26 (1994), S. 623-638 
    Details
    ISSN: 1573-8868
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract The concept of a random function and, consequently, the application of kriging cells for the implicit assumption that the data locations are embedded within an infinite domain. An implication of this assumption is that, all else being equal, outlying data locations will receive greater weight because they are seen as less redundant, hence, more informative of the infinite domain. A two- step kriging procedure is proposed for correcting this siring effect. The first step is to establish the total kriging weight attributable to each string. The distribution of that total weight to the samples in the string is accomplished by a second stage of kriging. In the second stage, a spatial redundancy measure r(n) is used in place of the covariance measure in the data-data kriging matrix. This measure is constructed such that each datum has the same redundancy with the (n)data of the string to which it belongs. This paper documents the problem of kriging with strings of data, develops the redundancy measure r(n),and presents a number of examples.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02089245
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