Prediction of sour gas compressibility factor using an intelligent approach

By Arash Kamari, A. Hemmati-Sarapardeh, S. Mirabbasi, M. Nikookar, A. Mohammadi. 2013

96 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/2f6c0f703217a75073f7a33dd7019b2a45ed8e27

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. A. Bahadori
2. Arash Kamari
3. A. Hemmati-Sarapardeh
4. Mohammadhadi Shateri
5. A. Mohammadi
6. Sohrab Zendehboudi
7. S. Ghorbani
8. A. Mohammadi

Citations in the corpus

• Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations (2014, methodology): “To achieve a reliable predictive model, the applicability and accuracy is directly associated with the validity of the data set used for its development (Eriksson et al., 2000; Kamari et al., 2013a; Kamari et al., 2013b; Kamari et al., 2014).”
• Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor (2015, background): “As a matter of fact, ompressibility factor is one of the most significant parameters of etroleum and gas engineering calculations [2,4].”