The application of block kriging estimation in porosity simulation

Because porosity is an important parameter for the evaluation of transport properties in many scientific and engineering fields, some interpolation or extrapolation methods are used to estimate or predict the unknown porosity for better simulated results. However, it is quite difficult to predict the unknown porosity values only by some sparse conditional data in the process of simulation based on current popular interpolation methods. Any numerical modeling should incorporate all relevant information from different scale data including coarse scale support data (block data) and fine scale support data (point data) to improve interpolation accuracy. Block kriging estimation (BKRIG) is built on a combination of kriging using both point and block data. Under the rather severe restriction, block data are linear averages of their constituent point values. This method allows reproducing both block and point data at their locations. The experimental results demonstrate that BKRIG is practical in porosity simulation.