Loading...
Search for:
south-pars-gas-field
0.006 seconds
Prediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin
, Article Geopersia ; Volume 8, Issue 1 , March , 2018 , Pages 43-60 ; 22287817 (ISSN) ; Kadkhodaie, A ; Ahmadi, B ; Ahmadi, B ; Faraji, M. A ; Sharif University of Technology
University of Tehran
2018
Abstract
Pore facies analysis plays an important role in the classification of reservoir rocks and reservoir simulation studies. The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalan formations in the South Pars gas field. In the first step, pore facieswere determined based on Mercury Injection Capillary Pressure (MICP) data in corporation with the Hierarchical Clustering Analysis (HCA) method. Each pore facies represents a specific type of pore geometry indicating the interaction between the primary rock fabric and its diagenetic overprints. In the next step, polynomial meta-models were established based...
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
Abstract
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),...
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 , September , 2014 , PP. 326-334 ; ISSN: 00167622 ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
Abstract
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),...