Search for: south-pars-field
Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods, Article Journal of the Geological Society of India ; Vol. 84, issue. 3 , Sep , 2014 , p. 326-334 ; ISSN: 00167622 ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),...
Article Soil Dynamics and Earthquake Engineering ; Volume 39 , August , 2012 , Pages 61-77 ; 02677261 (ISSN) ; Azarbakht, A ; Tabandeh, A ; Akbar Golafshani, A ; Sharif University of Technology
The exact and two approximate conditional spectra are compared in this manuscript as a target spectrum for the purpose of ground motion selection. The considered site is a real offshore site located at South Pars Gas Field in the Persian Gulf region. This case study site is influenced by four major seismic area sources in which the deaggregation results confirm that many comparable seismic scenarios can be taken into account. Therefore, an alternative to the conventional approximate conditional spectrum is proposed that has a small deviation from the exact solution. In addition, the use of different conditioning status of the probabilistic seismic hazard deaggregation (i.e., occurrence...
Article Oil and Gas Science and Technology ; Volume 66, Issue 6 , September , 2011 , Pages 1025-1033 ; 12944475 (ISSN) ; Crespo, F ; Sharif University of Technology
Equations of State EOS are vastly being used to predict the phase behavior of reservoir fluids. The accuracy of EOS modeling technique over conventional correlation models would benefit an improved property prediction of these fluids. Once the crude oil or gas condensate fluid system has been probably characterized using limited laboratory tests, its PVT behavior under a variety of conditions can be easily studied. In this paper, the PVT behavior of gas condensate from a reservoir in South Pars retrograde gas field in Iran was modeled using the three-parameter Patel and Teja Equation of State. The multi-sample characterization method is used to arrive at one consistent model for retrograde...