TY - JOUR
T1 - The application of geostatistical seismic inversion for delineating thin reservoirs
T2 - A case study of the Jambi Sub-Basin
AU - Haris, Abdul
AU - Prasetio, Aditya Dwi
AU - Riyanto, Agus
AU - Mardiyati, Sri
N1 - Publisher Copyright:
© IJTech 2018.
PY - 2018
Y1 - 2018
N2 - Geostatistical seismic inversion has been successfully carried out to characterize a thin reservoir of the Air Benakat Formation in Indonesia's Jambi sub-basin. The objective of this paper is to characterize detailed P-impedance of the thin reservoir in the Jambi sub-basin using geostatistical seismic inversion rather than deterministic seismic inversion. Geostatistical seismic inversion is believed to be able to enhance vertical resolution and accurately map sub-seismic features. This algorithm uses a geostatistical model, which is constrained by probability density function and a variogram as the input models. The method was applied to eight wells and three-dimensional seismic data that consist of 198 inline and 261 crossline. Prior to performing geostatistical seismic inversion, sensitivity analysis was carried out by cross-plotting petrophysical data to identify the petrophysical properties of the reservoir target. The geostatistical seismic inversion considered 50 realization models that were used as inputs in estimating the probability of the existing subsurface layer and the calculated P-impedance models to obtain the most probable P-impedance model that is useful for characterizing the detailed thin reservoir of the Air Benakat Formation in the Jambi sub-basin. The geostatistical seismic inversion results show a higher resolution of P-impedance compared to the deterministic seismic inversion and are able to resolve thin reservoirs below tuning thickness. In addition, this method is able to optimize better correlation between seismic and petrophysical properties of the thin reservoir with an average thickness below five metres, which is well modelled with reference to both seismic and well data.
AB - Geostatistical seismic inversion has been successfully carried out to characterize a thin reservoir of the Air Benakat Formation in Indonesia's Jambi sub-basin. The objective of this paper is to characterize detailed P-impedance of the thin reservoir in the Jambi sub-basin using geostatistical seismic inversion rather than deterministic seismic inversion. Geostatistical seismic inversion is believed to be able to enhance vertical resolution and accurately map sub-seismic features. This algorithm uses a geostatistical model, which is constrained by probability density function and a variogram as the input models. The method was applied to eight wells and three-dimensional seismic data that consist of 198 inline and 261 crossline. Prior to performing geostatistical seismic inversion, sensitivity analysis was carried out by cross-plotting petrophysical data to identify the petrophysical properties of the reservoir target. The geostatistical seismic inversion considered 50 realization models that were used as inputs in estimating the probability of the existing subsurface layer and the calculated P-impedance models to obtain the most probable P-impedance model that is useful for characterizing the detailed thin reservoir of the Air Benakat Formation in the Jambi sub-basin. The geostatistical seismic inversion results show a higher resolution of P-impedance compared to the deterministic seismic inversion and are able to resolve thin reservoirs below tuning thickness. In addition, this method is able to optimize better correlation between seismic and petrophysical properties of the thin reservoir with an average thickness below five metres, which is well modelled with reference to both seismic and well data.
KW - Air Benakat Formation
KW - Geostatistical seismic inversion
KW - Jambi sub-basin
KW - Thin reservoir
UR - http://www.scopus.com/inward/record.url?scp=85055559681&partnerID=8YFLogxK
U2 - 10.14716/ijtech.v9i5.2088
DO - 10.14716/ijtech.v9i5.2088
M3 - Article
AN - SCOPUS:85055559681
SN - 2086-9614
VL - 9
SP - 955
EP - 963
JO - International Journal of Technology
JF - International Journal of Technology
IS - 5
ER -