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Hydrocarbon Prospect Derived from Attributes Analysis on Low-Frequency Passive Seismic Survey: A Case Study from Kalimantan, Indonesia
Sugiartono Prabowo B.a, Verdhora Ry R.a, Dian Nugraha A.a, Siska K.a
a Volcanology and Geothermal Laboratory, Geophysical Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Hydrocarbon Microtremor Analysis is a low-frequency passive seismic method which derives a quick look estimates new hydrocarbon reservoir prospect area. This method based on the empirical study which investigated an increasing of spectra anomaly between 2 – 4 Hz above the reservoir. We determined five attributes on low-frequency band of microtremors including Power Spectral Density integral of vertical component (PSD-IZ), Power Spectral Density (PSD) on 3 Hz frequency, frequency shifting, the spectral ratio of vertical and horizontal components (V/H) maximum and integral of spectral ratio of vertical and horizontal components (V/H). We deployed 105 points of measurement spreading in our suspect area. We used time series data that recorded from particle velocity of three components with 80 minutes duration and 100 Hz of the sampling frequency. The noise identification analysis in each station data set has been made from the measurement location, considering the suspect area had different local cultural noise. We proceed attributes for each data acquired from all station then used the interpolated map using a standard kriging algorithm spatially. As a result, each attribute analysis and spatial attribute map are combined to identify and estimate a good prospect of the hydrocarbon reservoir.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Attribute analysis,Hydrocarbon prospects,Hydrocarbon reservoir,Measurement locations,Noise identification,Particle velocities,Power spectral densities (PSD),Sampling frequencies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/62/1/012020[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]