Productivity index modeling of Asmari reservoir rock using geostatistical and neural networks methods (SW Iran). Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Amanipoor, Hakimeh
• Is Part Of: Geodesy and cartography. Volume 43:Issue 4(2017)
• Page Start: 125
• Page End: 130
• Publication Date: 2017-10-02
• Subjects: carbonate reservoir -- productivity index -- artificial neural network -- geostatistics; Geodesy -- Periodicals; Geodesy -- Lithuania -- Periodicals; Cartography -- Lithuania -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Cartography; Geodesy; Surveying; Lithuania; Periodicals; 526.3
• DOI: 10.3846/20296991.2017.1371649
• Languages: English
• ISSNs: 2029-6991

Abstract

Abstract: In this study, productivity index in a carbonate reservoir was predicted using Artificial Neural Networks and geostatistical method. At first, about 518 data of productivity index based on locations of the wellbores were used for modeling and then 40 data were used for investigating the accuracy of the models. Then, the result of ANN was compared with the output of geostatistical modeling. The study shows that productivity index could be estimated with these methods with accepted accuracy. In addition, both modeling have almost the same result. However, accuracy of the geostatistical model by taking into account the spatial structure, is higher than that of neural network.