Open AccessSynthesis Porosity logs based on fuzzy logic in Buzrgun Member at Fauqi Oil Field Southeastern Iraq
Author(s) -
Huda F. Al-Saad,
Muwafaq F. Al-Shahwan
Publication year - 2019
Publication title -
mağallaẗ al-buḥūṯ wa-al-dirāsāt al-nafṭiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2710-1096
pISSN - 2220-5381
DOI - 10.52716/jprs.v9i2.290
Subject(s) - fuzzy logic , statistic , missing data , data mining , computer science , correlation coefficient , field (mathematics) , oil field , well logging , porosity , statistics , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , machine learning , engineering , petroleum engineering , geotechnical engineering , pure mathematics
In this paper, the artificial intelligent technique represents fuzzy logic, had been employed to generate missing well logs for three common logs sonic, neutron and density. A total Input data of 432 readings represent both depth and porosity, which belong to Buzurgun member were used to build fuzzy model. Input data were divided into two groups including training data 308 data points, which represent FQ-6, FQ-7, FQ-20 and FQ-21wells; and testing data 124 data points which represent FQ-15. Performance of model were evaluated by using two statistic aspects such as root mean square error and correlation coefficient. The results show that this technique offer more accurate and reliable missing logs. Fuzzy logic is used as a powerful tool in oil industry in Iraq because it is more flexible and realistic for evaluation, by employing the sufficient data for training and testing