TY - GEN
T1 - Integrated seismic stochastic inversion and multi-attributes to delineate reservoir distribution
T2 - 2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
AU - Haris, Abd.
AU - Novriyani, M.
AU - Supriyanto, null
AU - Hidayat, R.
AU - Riyanto, Agus
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/7/10
Y1 - 2017/7/10
N2 - This study presents the integration of seismic stochastic inversion and multi-attributes for delineating the reservoir distribution in term of lithology and porosity in the formation within depth interval between the Top Sihapas and Top Pematang. The method that has been used is a stochastic inversion, which is integrated with multi-attribute seismic by applying neural network Probabilistic Neural Network (PNN). Stochastic methods are used to predict the probability mapping sandstone as the result of impedance varied with 50 realizations that will produce a good probability. Analysis of Stochastic Seismic Tnversion provides more interpretive because it directly gives the value of the property. Our experiment shows that AT of stochastic inversion provides more diverse uncertainty so that the probability value will be close to the actual values. The produced AT is then used for an input of a multi-attribute analysis, which is used to predict the gamma ray, density and porosity logs. To obtain the number of attributes that are used, stepwise regression algorithm is applied. The results are attributes which are used in the process of PNN. This PNN method is chosen because it has the best correlation of others neural network method. Finally, we interpret the product of the multi-attribute analysis are in the form of pseudo-gamma ray volume, density volume and volume of pseudo-porosity to delineate the reservoir distribution. Our interpretation shows that the structural trap is identified in the southeastern part of study area, which is along the anticline.
AB - This study presents the integration of seismic stochastic inversion and multi-attributes for delineating the reservoir distribution in term of lithology and porosity in the formation within depth interval between the Top Sihapas and Top Pematang. The method that has been used is a stochastic inversion, which is integrated with multi-attribute seismic by applying neural network Probabilistic Neural Network (PNN). Stochastic methods are used to predict the probability mapping sandstone as the result of impedance varied with 50 realizations that will produce a good probability. Analysis of Stochastic Seismic Tnversion provides more interpretive because it directly gives the value of the property. Our experiment shows that AT of stochastic inversion provides more diverse uncertainty so that the probability value will be close to the actual values. The produced AT is then used for an input of a multi-attribute analysis, which is used to predict the gamma ray, density and porosity logs. To obtain the number of attributes that are used, stepwise regression algorithm is applied. The results are attributes which are used in the process of PNN. This PNN method is chosen because it has the best correlation of others neural network method. Finally, we interpret the product of the multi-attribute analysis are in the form of pseudo-gamma ray volume, density volume and volume of pseudo-porosity to delineate the reservoir distribution. Our interpretation shows that the structural trap is identified in the southeastern part of study area, which is along the anticline.
UR - http://www.scopus.com/inward/record.url?scp=85026217827&partnerID=8YFLogxK
U2 - 10.1063/1.4991284
DO - 10.1063/1.4991284
M3 - Conference contribution
AN - SCOPUS:85026217827
T3 - AIP Conference Proceedings
BT - International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
A2 - Sugeng, Kiki Ariyanti
A2 - Triyono, Djoko
A2 - Mart, Terry
PB - American Institute of Physics Inc.
Y2 - 1 November 2016 through 2 November 2016
ER -