Open AccessPRODUCTIVITY INDEX MODELING OF ASMARI RESERVOIR ROCK USING GEOSTATISTICAL AND NEURAL NETWORKS METHODS (SW IRAN)
Author(s) -
Hakimeh Amanipoor
Publication year - 2017
Publication title -
geodesy and cartography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.33
H-Index - 11
eISSN - 2029-6991
pISSN - 2029-7009
DOI - 10.3846/20296991.2017.1371649
Subject(s) - artificial neural network , geostatistics , index (typography) , productivity , index method , computer science , data mining , geology , production (economics) , statistics , artificial intelligence , spatial variability , mathematics , world wide web , economics , macroeconomics
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.