Gas Logging Data Normalization Processing Based on Rough Set Theory

摘要

As everyone knows, gas logging data is affected by many factors, such as geological factors and drilling factors, The factors cause difference of gas logging data in different regions, and improve the difficult of gas logging data processing. So, selecting the parameters of gas logging data is very important. Aiming at the RBF neural network algorithm manages the gas logging data has instable and other faults, this thesis presentsa normalizationmethod based on rough set theory, using this method improves the training speed of RBF neural network algorithm manages the gas logging data. In order to verify the feasibility of this method , this thesis uses the gas logging data from Liao he oil field. Throughing the experimental results, thismethod can effectively improve the RBF neural network algorithm to manages the gas logging dataspeed.