Comparison of the function of ELM and RBF models for estimating the porosity of the Asmari Formation, in one of the offshore fields of the northwest Persian Gulf

Nowadays, the use of artificial intelligence is common to increase the accuracy of the study and, close to reality, is used in the oil industry to increase the accuracy of studying and understanding the relationship between various parameters. The main purpose of this study is to compare the performance of the two methods of Extreme Learning Machine (ELM) and Radial Basis Function (RBF) in porosity estimation, which is static oil modeling. The data from seven wells in the offshore field (Hendijan Oilfield) of the northwestern Persian Gulf were examined. In this regard, post-stack seismic attributes which have a significant relationship with porosity and porosity log for each well were used to compare the performance of the ELM and RBF networks under the same conditions. Eventually, it reveals that ELM is quite sensitive to the data set and needs more data points to prepare a map (quantitatively), but is better than RBF in terms of classification (qualitative). On the other hand, RBF is one of the most powerful algorithms in mapping, especially in low numbers of data points, which can be challenging for others.