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000780778 019__ $$a982144280$$a982222937$$a982348054$$a982428702$$a982505718$$a982814257
000780778 020__ $$a9783319532011$$q(electronic book)
000780778 020__ $$a3319532014$$q(electronic book)
000780778 020__ $$z9783319532004
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000780778 050_4 $$aTN870.53
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000780778 1001_ $$aAzev, Leonardo,$$eauthor.
000780778 24510 $$aGeostatistical methods for reservoir geophysics /$$cLeonardo Azevedo, Amílcar Soares.
000780778 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2017]
000780778 300__ $$a1 online resource.
000780778 336__ $$atext$$btxt$$2rdacontent
000780778 337__ $$acomputer$$bc$$2rdamedia
000780778 338__ $$aonline resource$$bcr$$2rdacarrier
000780778 4901_ $$aAdvances in oil and gas exploration & production
000780778 504__ $$aIncludes bibliographical references and index.
000780778 5050_ $$aPreface; Contents; List of Abbreviations and MathematicalSymbols; List of Figures; List of Tables; 1 Introduction-Geostatistical Methods for Integrating Seismic Reflection Data into Subsurface Earth Models; 1.1 Spatial Resolution Gap; 1.2 Seismic Inversion; 2 Fundamental Geostatistical Tools for Data Integration; 2.1 Spatial Continuity Patterns Analysis and Modeling; 2.1.1 Bi-point Statistics; 2.1.2 Complex Morphologic Patterns: Auxiliary and Reference Images; 2.1.3 Spatial Random Fields; 2.1.4 Variograms and Spatial Covariances; 2.1.5 Spatial Representativeness of the Variogram
000780778 5058_ $$a2.1.6 Spatial Continuity for Multivariate Systems2.1.7 Variogram Modeling Workflow; 2.1.8 Theoretical Variogram Models; 2.1.9 Linear Combinations of Variogram Models: Imbricated Structures; 2.1.10 Co-regionalized Models of Multivariate Systems; 2.2 Estimation Models; 2.2.1 Linear Estimation of Local Statistics; 2.2.2 Probabilistic Model of the Geostatistical Linear Estimator; 2.3 Kriging Estimate; 2.3.1 Kriging System Resolution; 2.4 Linear Estimation of Non-stationary Phenomena: Simple Kriging; 2.5 Co-kriging Estimate
000780778 5058_ $$a2.6 Co-estimation with a Secondary Variable in a Much Denser Sample Grid: Collocated Co-kriging2.7 Estimation of Local Probability Distribution Functions; 2.7.1 Gaussian Transform of the Experimental Data; 2.8 Estimation of Categorical Variables; 3 Simulation Models of Physical Phenomena in Earth Sciences; 3.1 Stochastic Simulation Models; 3.2 Sequential Simulation Models; 3.3 Sequential Gaussian Simulation; 3.4 Direct Sequential Simulation from Experimental Distributions; 3.4.1 Direct Sequential Simulation; 3.4.2 Direct Sequential Co-simulation
000780778 5058_ $$a3.4.3 Stochastic Sequential Co-simulation with Joint Probability Distributions3.4.4 Stochastic Simulation with Uncertain Data: DSS with Point Probability Distributions; 3.5 Simulation of Categorical Variables; 3.5.1 Indicator Simulation; 3.5.2 Alternative Simulation Methods for Categorical Variables; 3.5.3 High-Order Stochastic Simulation of Categorical Variables; 4 Integration of Geophysical Data for Reservoir Modeling and Characterization; 4.1 Seismic Inversion; 4.2 Bayesian Framework for Integrating Seismic Reflection Data into Subsurface Earth Models
000780778 5058_ $$a4.3 Iterative Geostatistical Seismic Inversion Methodologies4.3.1 Frequency Domain of Geostatistical Seismic Inversion; 4.3.2 Trace-by-Trace Geostatistical Seismic Inversion Methodologies; 4.3.3 Global Geostatistical Seismic Inversion Methodologies; 4.3.4 Global Geostatistical Acoustic Inversion; 4.3.5 Global Geostatistical Elastic Inversion; 4.3.6 Geostatistical Seismic AVA Inversion; 4.3.7 Application Example with Geostatistical Seismic AVA Inversion; 4.3.8 Seismic Inversion with Structural Local Models
000780778 506__ $$aAccess limited to authorized users.
000780778 520__ $$aThis book presents a geostatistical framework for data integration into subsurface Earth modeling. It offers extensive geostatistical background information, including detailed descriptions of the main geostatistical tools traditionally used in Earth related sciences to infer the spatial distribution of a given property of interest. This framework is then directly linked with applications in the oil and gas industry and how it can be used as the basis to simultaneously integrate geophysical data (e.g. seismic reflection data) and well-log data into reservoir modeling and characterization. All of the cutting-edge methodologies presented here are first approached from a theoretical point of view and then supplemented by sample applications from real case studies involving different geological scenarios and different challenges. The book offers a valuable resource for students who are interested in learning more about the fascinating world of geostatistics and reservoir modeling and characterization. It offers them a deeper understanding of the main geostatistical concepts and how geostatistics can be used to achieve better data integration and reservoir modeling.
000780778 588__ $$aVendor-supplied metadata.
000780778 650_0 $$aPetroleum$$xGeology$$xStatistical methods.
000780778 650_0 $$aPetroleum$$xGeology$$xMathematical models.
000780778 650_0 $$aProspecting$$xGeophysical methods.
000780778 650_0 $$aGas reservoirs.
000780778 650_0 $$aOil industries$$vStatistics.
000780778 650_0 $$aGas industry$$vStatistics.
000780778 7001_ $$aSoares, Amilcar,$$eauthor.
000780778 77608 $$iPrint version:$$z3319532006$$z9783319532004$$w(OCoLC)967561341
000780778 830_0 $$aAdvances in oil and gas exploration & production.
000780778 852__ $$bebk
000780778 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-53201-1$$zOnline Access$$91397441.1
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000780778 980__ $$aEBOOK
000780778 980__ $$aBIB
000780778 982__ $$aEbook
000780778 983__ $$aOnline
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