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ArticleJPT_Nov2006-Yarus-Chambers - DISTINGUISHED AUTHOR...

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78 JPT • NOVEMBER 2006 Abstract Some engineers are skeptical of statistical, let alone geosta- tistical, methods. Geostatistical analysis in reservoir charac- terization necessitates an understanding of a new and often unintuitive vocabulary. Statistical approaches for measuring uncertainty in reservoirs is indeed a rapidly growing part of the best-practice set of methodologies for many companies. For those already familiar with the basic concepts of geosta- tistics, it is hoped that this overview will be a useful refresher and perhaps clarify some concepts. For others, this overview is intended to provide a basic understanding and a new level of comfort with a technology that may be useful to them in the very near future. Introduction Geoscientists and geological engineers have been making maps of the subsurface since the late 18th century. The evo- lution of our ability to predict structure beneath the surface of the Earth has been a complex interaction between quanti- tative analysis and qualitative judgment. Geostatistics com- bines the empirical conceptual ideas that are implicitly sub- ject to degrees of uncertainty with the rigor of mathematics and formal statistical analysis. It has found its way into the field of reservoir characterization and dynamic flow simula- tion for a variety of reasons including its ability to success- fully analyze and integrate different types of data, provide meaningful results for model building, and quantitatively assess uncertainty for risk management. Additionally, from a management point of view, its methodologies are applicable for both geoscientists and engineers, thereby lending itself to a shared Earth model and a multidisciplinary workforce. Why Geostatistics? Fig. 1 depicts two images of hypothetical 2D distribution patterns of porosity. Fig. 1a shows a random distribution of porosity values, while Fig. 1b is highly organized, showing a preferred northwest/southeast direction of continuity. While this difference is obvious to the eye, the classical descriptive- summary statistics suggest that the two images are the same. That is, the number of red, green, yellow, and blue pixels in each image is the same, as are the univariate statistical summaries such as the mean, median, mode, variance, and standard deviation (Fig. 1c). Intuitively, as scientists and engineers dealing with Earth properties, we know that the geological features of reservoirs are not randomly distrib- uted in a spatial context. The reservoirs are heterogeneous and have directions of continuity in both 2D and 3D space and are products of specific depositional, structural, and diagenetic histories. Strangely, that these two images would appear identical in a classical statistical analysis is the basis of a fundamental problem inherent in all sciences dealing with spatially organized data. Classical statistical analysis inad- equately describes phenomena that are both spatially con- tinuous and heterogeneous. Thus, use of classical statistical
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