Seismic Facies Classification And Identification Using Neural Net And Principal Component Methods In The Bemba Field In Niger Delta, Nigeria

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.

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APA

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

MLA 8th

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 27 May. 2024.

MLA7

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. 27 May. 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 >.

Chicago

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 May 27, 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