ABSTRACT
Seismic facies classification was done on 3D Prestack Time Migrated seismic data (PSTM)
acquired in 1990 in the Bemba Field in the Costal Swamp Depobelt of the Niger Delta, with
the aid of STRATIMAGIC™ ( a 3D stratigraphic interpretation tool) and VOXELGEO™ ( a
Volume visualization tool) softwares. Three (3) horizons E2000, F2000, G1000 were
interpreted. To clearly delineate and control the quality of the auto-tracked picks, horizons
attributes (amplitude, dip and azimuth maps) were calculated and these helped in identifying
the zone of interest which is the E2000 horizon. A constant time interval corresponding to
10milliseconds (ms) interval above the E2000 horizon was used to characterize the zone of
interest over the entire 3D survey. After the intervals were identified, Neural Network was
then used to analyze the trace shape within the interval and a series of synthetic traces
(representing the shape variation within the interval) was generated and sorted in a model.
The analysis was done in an unsupervised mode that does not require any seismic preprocessing
or any well data. A mixed maps which is a combination of the Facies map and
Principal Component Analysis (PCA) were used to highlight the details of the geological
feature interpreted in the study. A seismic facies map showing the distribution of similar trace
shape and geological features was generated. A stratigraphic feature was identified above the
E2000 horizon and divided into two sections (E2000 main and E2000 Central). These
correspond to the -100 milliseconds (ms) above the E2000 horizon. The stratigraphic feature
is interpreted as a submarine fan (E2000 Main) with its associated channel complex and Lobe
(E2000 Central) respectively. The facies map combined with the formation sculpting using
VOXELGEO™, enabled the delineation of the extent of the Fan (E2000 Main) and Lobe
(E2000 Central) deposited as well as a channel system oriented in NW-SE direction within
the Fan. The geologic feature associated with the Fan and Lobe include Overbank, Point bar and Levees.
CHARLES, O (2021). Seismic Facies Classification And Identification Using Neural Net And Principal Component Methods In The Bemba Field In Niger Delta, Nigeria. Afribary. Retrieved from https://afribary.com/works/seismic-facies-classification-and-identification-using-neural-net-and-principal-component-methods-in-the-bemba-field-in-niger-delta-nigeria
CHARLES, OTOGHILE "Seismic Facies Classification And Identification Using Neural Net And Principal Component Methods In The Bemba Field In Niger Delta, Nigeria" Afribary. Afribary, 13 May. 2021, https://afribary.com/works/seismic-facies-classification-and-identification-using-neural-net-and-principal-component-methods-in-the-bemba-field-in-niger-delta-nigeria. Accessed 07 Nov. 2024.
CHARLES, OTOGHILE . "Seismic Facies Classification And Identification Using Neural Net And Principal Component Methods In The Bemba Field In Niger Delta, Nigeria". Afribary, Afribary, 13 May. 2021. Web. 07 Nov. 2024. < https://afribary.com/works/seismic-facies-classification-and-identification-using-neural-net-and-principal-component-methods-in-the-bemba-field-in-niger-delta-nigeria >.
CHARLES, OTOGHILE . "Seismic Facies Classification And Identification Using Neural Net And Principal Component Methods In The Bemba Field In Niger Delta, Nigeria" Afribary (2021). Accessed November 07, 2024. https://afribary.com/works/seismic-facies-classification-and-identification-using-neural-net-and-principal-component-methods-in-the-bemba-field-in-niger-delta-nigeria