Methodology for Variogram Interpretation and Modeling for Improved Reservoir Characterization
E. Gringarten, Clayton V. Deutsch
Abstract
E. Gringarten, Clayton V. Deutsch
Abstract
Abstract The variogram is a critical input to geostatistical studies. It is the most widely used tool to investigate and model spatial variability of lithofacies, porosity, and other petrophysical properties. In addition, 90% of geostatistical reservoir characterization studies use variogram-based geostatistical modeling methods. Furthermore, the variogram reflects our understanding of the geometry and continuity of reservoir properties and can have an important effect on predicted flow behavior and consequent reservoir management decisions. Yet, the practice of variogram interpretation and modeling is poorly documented and unexperienced practitioners find themselves at lost when required to provide a reliable variogram model. This often results in wrong variogram models used in subsequent geostatistical studies. Our approach is a two-step procedure similar to that used in modern well test interpretation, that is, model identification followed by parameter estimation. The total variance of the phenomenon under study is divided into variance regions. The behavior of each variance region is shown to follow clearly defined behaviors reflecting well-understood geological features. Establishing a mathematically consistent and geologically interpretable variogram model is straightforward after model recognition. The proposed methodology for variogram interpretation and modeling provides a better, more rigorous, quantification of spatial variability, which leads to improved flow models and management decisions. The theoretical background of our methodology will be presented. A number of case studies are then shown to illustrate the practical importance of the methodology.
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Abstract The variogram is a critical input to geostatistical studies. It is the most widely used tool to investigate and model spatial variability of lithofacies, porosity, and other petrophysical properties. In addition, 90% of geostatistical reservoir characterization studies use variogram-based geostatistical modeling methods. Furthermore, the variogram reflects our understanding of the geometry and continuity of reservoir properties and can have an important effect on predicted flow behavior and consequent reservoir management decisions. Yet, the practice of variogram interpretation and modeling is poorly documented and unexperienced practitioners find themselves at lost when required to provide a reliable variogram model. This often results in wrong variogram models used in subsequent geostatistical studies. Our approach is a two-step procedure similar to that used in modern well test interpretation, that is, model identification followed by parameter estimation. The total variance of the phenomenon under study is divided into variance regions. The behavior of each variance region is shown to follow clearly defined behaviors reflecting well-understood geological features. Establishing a mathematically consistent and geologically interpretable variogram model is straightforward after model recognition. The proposed methodology for variogram interpretation and modeling provides a better, more rigorous, quantification of spatial variability, which leads to improved flow models and management decisions. The theoretical background of our methodology will be presented. A number of case studies are then shown to illustrate the practical importance of the methodology.
Key concepts: Variogram, Variance (accounting), Geostatistics, Reservoir modeling, Petrophysics, Kriging, Computer science, Interpretation (philosophy)