بینش جدید به تعیین تخلخل با استفاده از تکنیک های هوش مصنوعی برای مخازن کربناته

The results obtained showed that ANN and ANFIS can be used to estimate the reservoir porosity based on log data with a high correlation coefficient (R) and low average absolute percentage error (AAPE). The main inputs required for porosity estimation are bulk density, neutron porosity, and sonic compressional time. The developed mathematical equation based on the weights and bias of the ANN model can be used to predict the reservoir porosity based on log data with a correlation coefficient of 0.98 and an AAPE less than 8%. The advantage of this work is that we extracted the mathematical model from the ANN that can be used directly to determine the porosity without the need for training and testing the data. The porosity estimation from the neutron-density crossplots, which is the current technique used by the industry, yielded 14.7% error.