TY - GEN
T1 - Reservoir zone prediction using logging data - Multi well based on levenberg-marquardt method
AU - Utami, E.
AU - Wibawa, A. D.
AU - Biyanto, T. R.
AU - Purnomo, M. H.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/18
Y1 - 2017/10/18
N2 - Well logging is a well-known and effective method for oil and natural gas exploration in new fields in order to enhance oil and gas production. Well Logging is defined as an acquisition method to qualitatively and quantitatively evaluate the existence of hydrocarbon layer in the well. In this research, we studied the relations between well logging data and reservoir zone in Salawati basin, Irian Jaya area. Four well logs with four attributes such as Log Gamma Ray (GR), Log Resistivity (ILD), Log Density (RHOB), and Log Neutron (NPHI) were explored. The reservoir zone data has been previously determined by using log curve whether it is a reservoir zone or not. This data then is being used as a target for learning. Since the logging data is a complex and nonlinear, Levenberg-Marquardt (LM) was then implemented as an artificial intelligent algorithm in performing this study. The objective of this work is to build decision support system that will automatically find reservoir zone based on well logging data. The results of this work showed that Mean Absolute Percentage Error (MAPE) of training for reservoir zone prediction by exploiting Levenberg - Marquardt is 0.3803 % with 500 iteration. Validity test results based on ROC curve with cross validation folds 10 is 84.9984% and area of under ROC is 0.992. This result showed that this method has a high potential to be used in real exploration activities so that the predicting reservoir zone then can be done precisely.
AB - Well logging is a well-known and effective method for oil and natural gas exploration in new fields in order to enhance oil and gas production. Well Logging is defined as an acquisition method to qualitatively and quantitatively evaluate the existence of hydrocarbon layer in the well. In this research, we studied the relations between well logging data and reservoir zone in Salawati basin, Irian Jaya area. Four well logs with four attributes such as Log Gamma Ray (GR), Log Resistivity (ILD), Log Density (RHOB), and Log Neutron (NPHI) were explored. The reservoir zone data has been previously determined by using log curve whether it is a reservoir zone or not. This data then is being used as a target for learning. Since the logging data is a complex and nonlinear, Levenberg-Marquardt (LM) was then implemented as an artificial intelligent algorithm in performing this study. The objective of this work is to build decision support system that will automatically find reservoir zone based on well logging data. The results of this work showed that Mean Absolute Percentage Error (MAPE) of training for reservoir zone prediction by exploiting Levenberg - Marquardt is 0.3803 % with 500 iteration. Validity test results based on ROC curve with cross validation folds 10 is 84.9984% and area of under ROC is 0.992. This result showed that this method has a high potential to be used in real exploration activities so that the predicting reservoir zone then can be done precisely.
KW - Levenberg-Marquardt algorithm
KW - Neural Network in oil and gas
KW - Reservoir zone prediction
KW - Well logging data
UR - http://www.scopus.com/inward/record.url?scp=85040022371&partnerID=8YFLogxK
U2 - 10.1109/ISCAIE.2017.8074962
DO - 10.1109/ISCAIE.2017.8074962
M3 - Conference contribution
AN - SCOPUS:85040022371
T3 - ISCAIE 2017 - 2017 IEEE Symposium on Computer Applications and Industrial Electronics
SP - 122
EP - 126
BT - ISCAIE 2017 - 2017 IEEE Symposium on Computer Applications and Industrial Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2017
Y2 - 24 April 2017 through 25 April 2017
ER -