Revisiting the use of seismic attributes as soft data for subseismic facies prediction: Proportions versus probabilities

Abstract

Geostatistical modeling originated within the mining industry to estimate average minable ore grade from large support volumes given samples measured on small volume support. In petroleum geostatistics, the goal is more equivocal due to several different scales of support of input data, which are often incongruent with the desired prediction scale. More specifically, the goal is to utilize indirect measurements (e.g., seismic data) from a scale larger than the prediction scale for fine-scale spatial distributions of facies and petrophysical properties grounded by undersampled point data (e.g., well-log data). (Note, volume support is a geostatistical term that describes the size or resolution of the sample or measurement.)