petrel英语教程1.ppt

Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Z(Xo) = Value at location Xo. If the weights (lambda) approaches zero, the mean gets more and more influence. For Simple Kriging the mean is a global mean (more stable algorithm than the Ordinary Kriging). Overview - Chapter 1 * Ordinary Kriging – re-estimates the mean at each location, therefore it is a local mean. It is more unstable than Simple Kriging, but can be good when there are many wells. Can be suitable for data points that show a trend. Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * In general the Exponential and Spherical gives similar results. Overview - Chapter 1 * Hint/recommendation – Range should always be larger than the average spacing of data points. Overview - Chapter 1 * Overview - Chapter 1 * Note: The example to the right shows a nugget of 1, meaning that Kriging produces the overall mean. Overview - Chapter 1 * Note: The kriging algorithm is not fast compared to other algorithms like the Convergent Interpolation. Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * During the simulation local highs and lows will be generated between input data locations which honor the variogram. The positions of these highs and lows will be determined by a random number supplied by the user or the software.?Because of this, multiple representations are recommended to gain an understanding of uncertainty. In the absence of other information the input distribution will be given by the input data.? In this case the result will not give values above the maximum or below the minimum of the input data. Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Chapter 1 * Overview - Ch