Abstract
Original language | English |
---|---|
Pages | 241-246 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2012 |
Event | Innovation Management and Technology Research (ICIMTR), 2012 International Conference on - Duration: 21 May 2012 → 22 May 2012 |
Conference
Conference | Innovation Management and Technology Research (ICIMTR), 2012 International Conference on |
---|---|
Period | 21/05/12 → 22/05/12 |
Fingerprint
Cite this
Prediction of oil reservoir porosity based on BP-ANN. / Hamidi, Hossein; Rafati, Roozbeh.
2012. 241-246 Paper presented at Innovation Management and Technology Research (ICIMTR), 2012 International Conference on, .Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Prediction of oil reservoir porosity based on BP-ANN
AU - Hamidi, Hossein
AU - Rafati, Roozbeh
PY - 2012
Y1 - 2012
N2 - Porosity of oil reservoir rock is usually determined by Core Analysis. But this method is expensive and time consuming. Also because of lithology changes, heterogeneity of reservoir rock, and nonexistence of sufficient well cores, determination of the parameters by the usual methods is not accurate. So the best way to decrease cost, increase accuracy, and decrease time is applying advanced software such as Geolog and Back-Propagation of Error Artificial Neural Network (BP-ANN). In this paper, a BP-ANN was designed to predict the porosity of formations using the well logs data in Parsi field, located in southwest of Iran. The data of two wells (No. 33 and No. 19) that have core data were used for training, testing, validation, and generalization processes. Then the BP-ANN results were compared to evaluations obtained from Geolog Software (GS). With respect to the results, it was concluded that the BP-ANN is more accurate than GS in determining oil reservoir porosity. At the end, porosity was simulated in three other wells (No. 48, 49, and 64) that lack core data.
AB - Porosity of oil reservoir rock is usually determined by Core Analysis. But this method is expensive and time consuming. Also because of lithology changes, heterogeneity of reservoir rock, and nonexistence of sufficient well cores, determination of the parameters by the usual methods is not accurate. So the best way to decrease cost, increase accuracy, and decrease time is applying advanced software such as Geolog and Back-Propagation of Error Artificial Neural Network (BP-ANN). In this paper, a BP-ANN was designed to predict the porosity of formations using the well logs data in Parsi field, located in southwest of Iran. The data of two wells (No. 33 and No. 19) that have core data were used for training, testing, validation, and generalization processes. Then the BP-ANN results were compared to evaluations obtained from Geolog Software (GS). With respect to the results, it was concluded that the BP-ANN is more accurate than GS in determining oil reservoir porosity. At the end, porosity was simulated in three other wells (No. 48, 49, and 64) that lack core data.
U2 - 10.1109/ICIMTR.2012.6236396
DO - 10.1109/ICIMTR.2012.6236396
M3 - Paper
SP - 241
EP - 246
ER -