FUZZY MODELING FOR RESERVE ESTIMATION BASED ON SPATIAL VARIABILITY

Abstract

This article addresses a new reserve estimation method which uses fuzzy modeling algorithms and estimates the reserve parameters based on spatial variability. The proposed fuzzy modeling approach has three stages: (1) Structure identification and preliminary clustering, (2) Variogram analysis, and (3) Clustering based rule system. A new clustering index approach and a new spatial measure function (point semimadogram) are proposed in the paper. The developed methodology uses spatial variability in each step and takes the fuzzy rules from input-output data. The model has been tested using both simulated and real data sets. The performance evaluation indicates that the new methodology can be applied in reserve estimation and similar modeling problems