Porosity estimation with data fusion approach (Bayesian theory) in wells of Azadegan oil field, Iran

Porosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs، containes some errors and uncertainty. This porosity is influenced by different factors، such as temperature، pressure، fluid type، clay content and the and amount of hydrocarbons. One of the best، and yet most practical ways to reduce the amount of uncertainty in porosity measurement is using various sources of data and data fusion techniques. Data fusion increase certainty and confidence and reduce risk and error in decision making. In this research، the porosity is estimated in 4 wells of Azadegan oil field، with data fusion method (Bayesian theory). To check the ability of generalization of the method، the porosity was also estimated in one other well of this field. A maximum of 7 input variables were used to estimate porosity in this new approach. The results showed that data fusion technique is more powerfull than traditional tecniques for porosity estimation. According to the results، this method has higher credibility than traditional techniques that show 0. 7 to 0. 8 regressions with log data but data fusion technique showed solidarity over 0. 9 with log data.