Automated Analysis of Gridded Geologic Map Data

Susan M. Schrader and Robert S. Balch
New Mexico Tech Petroleum Recovery Research Center, Socorro, NM

With the advent of exploration tools such as expert systems and neural networks, the geophysical, formation and production knowledge required for making decisions about a potential prospect often requires digitized or gridded map data and automated map data analysis. In order to analyze this input data, algorithms need to be developed that can look at numerically gridded data and interpret them in the same way an experienced geologist interprets a feature map. As part of the development of expert exploration systems for two New Mexico formations, a series of algorithms were developed to interpret data from cores, well tests and production reports. The simplest of these algorithms include search algorithms for linking each prospect location to the nearest map gridpoint, finding the producing well closest to the prospect, and finding the nearest location with high predicted production. Another set of algorithms use formation tops to compute the relative dip between each prospect and the nearest producing well, and search for the nearest downdip source rock. More involved algorithms were designed to search for an updip sand pinchout near the prospect, evaluate the consistency of a feature such as formation thickness in a region around the prospect, and determine if the paleostructure in the region supports trap potential. The results of these algorithms provide valuable input for the expert system tools as well as providing a fast method to scan large amounts of gridded numerical data for values that can be linked to production potential.