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The purpose of this foundation course is to provide the participants with an understanding of how petrophysics and statistics are related. Often the primary objective of reservoir petrophysics is to provide numerical transforms for total porosity, effective porosity, absolute permeability, lithology and water saturation. These numerical transforms are then used as the basis for building static 3-D models. To be a successful reservoir petrophysicists, you must understand statistical distributions, various averaging methods, smoothing, curve fitting and regression methods. Further, is necessary to understand when a dataset has statistical significance.333
Petrophysicists and geoscientists seeking to use statistics to improve their petrophysical interpretations.
Learning objectives of statistics and petrophysics include: 1. Learn basic statistics (mean, mode, distributions and standard deviation) and how these measures are applied in petrophysics 2. Present what is the significance of the quality of fit (R2) and residual analysis. 3. Present how multi-linear regression can be applied to improve the quality of fit (R2). Evaluate adding grain size, VSH, gamma ray, lithology, additional porosity devices etc. The goal is to find the best possible relationship. 4. In some reservoir systems, the relationship between porosity and permeability is non-linear and in those cases it may be necessary to use a non-parametric approach such as Kipling™ or fuzzy logic relating to existing core data. 5. Several empirical methods exist such as Coates, Timur, Wiley-Rose, SDR, SPWLA and others. Some of these methods are independent of initial water saturation, while others are not. The final product should show Winland pore throat radius, petrophysical rock type and log computed Sw. The goal is to identify what mechanism is controlling the water saturation distribution
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