A Rule Induction Algorithm for Application to Petrophysical, Seismic, Geological and Reservoir Data
Clayton Vernon Deutsch, YuLong Xie, Alvin S. Cullick · SPE Western Regional Meeting · 2001
Abstract This paper introduces an algorithm for rule induction intended to provide new insights, improve the reliability and expedite the utilization of large petrophysical and geologic databases. Very large petrophysical, geophysical, and geological databases contain multiple data types, which must be interpreted for application in subsurface modeling. This paper presents a significant advance in discovering complex and even nontrivial data relationships from such databases. Geoscientists are often challenged to predict subsurface lithologies and properties from multivariate relationships within large databases of core, wireline, and seismic data. Many data analysis techniques are used including histograms, parametric and non-parametric regression, n-dimensional histograms, cluster analysis, discrimininant analysis, principal components analysis. This paper introduces a new algorithm that seeks to discover "rule-like" relationships within the data that can be used to make predictions. The method is loosely derived from a data mining technology of classification. Concepts of data attribute distinguishability and importance are introduced to assess the value of the data and the outcomes to predictability. The new theory, implementation details, and an application are presented. Current petrophysical, seismic, and geostatistical analysis benefit from the rule induction algorithm presented. Improved reservoir characterization and forecasting result.