TY - JOUR
T1 - SANDSTONE RESERVOIR DELINEATION USING MACHINE LEARNING-BASED SPECTRAL ATTRIBUTE ANALYSIS IN "G" FIELD JAMBI SUB-BASIN, SOUTH SUMATRA BASIN
AU - Putri, Gabriella Eka
AU - Haris, Abdul
AU - Septyandy, Muhammad Rizqy
N1 - Funding Information:
Figure 4 shows the north to northeast part, interpreted as the fluvial floodplain and close to the area interpreted as a channel (in the southeast part). This channel form is also found at the same area in the RMS Amplitude visualization (Figure 2). Meanwhile, the magenta to blue color at the northeast part interpreted as fluvial floodplain explains the show of high frequency associated with the thin sand layer. This is confirmed from the gamma-ray log near the area showing thin sand in between mudstone with serrated log form correlated with fluvial floodplain supporting this interpretation. Similar to Figure 3, the west direction's middle part is interpreted as shoreface delta form, indicating the form of the delta. This interpretation is supported by the deposition direction of NE – SW, and the deposition development begins from the fluvial floodplain to the distributary channel in the delta area (towards the marine).
Publisher Copyright:
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PY - 2022/6
Y1 - 2022/6
N2 - A lot of reservoirs have thick and thin sand bodies at the same intervals, while the amplitude values of seismic data frequently highlight sand bodies near the ¼ wavelength for the tuning phenomena. These machine learning methods aim to link seismic attributes for the qualitative prediction of facies classification and compare the results obtained with the common seismic attributes visualizations controlled by the gamma-ray log data as the lithofacies guidance. This study performed an extraction of Seismic Spectral Attributes (SSAs) in the area of interest for the spectral decomposition RGB Blending visualization. Furthermore, numerical values were applied for several seismic attributes in the clustering step, while a principal component analysis (PCA) was proposed towards lowering the computational time and storage space on these values.
AB - A lot of reservoirs have thick and thin sand bodies at the same intervals, while the amplitude values of seismic data frequently highlight sand bodies near the ¼ wavelength for the tuning phenomena. These machine learning methods aim to link seismic attributes for the qualitative prediction of facies classification and compare the results obtained with the common seismic attributes visualizations controlled by the gamma-ray log data as the lithofacies guidance. This study performed an extraction of Seismic Spectral Attributes (SSAs) in the area of interest for the spectral decomposition RGB Blending visualization. Furthermore, numerical values were applied for several seismic attributes in the clustering step, while a principal component analysis (PCA) was proposed towards lowering the computational time and storage space on these values.
KW - And unsupervised learning
KW - Principal component analysis
KW - Seismic spectral attributes
UR - http://www.scopus.com/inward/record.url?scp=85131446457&partnerID=8YFLogxK
U2 - 10.21660/2022.94.3246
DO - 10.21660/2022.94.3246
M3 - Article
AN - SCOPUS:85131446457
SN - 2186-2982
VL - 22
SP - 83
EP - 92
JO - International Journal of GEOMATE
JF - International Journal of GEOMATE
IS - 94
ER -