Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach

Many hydrocarbon explorations in mature fields have been severely affected by complex and overburdening issues, such as shallow gas accumulation, gas pockets, and gas seepage. In this work, a new forward modelling technique is proposed in evaluating the potential survey design for fields affected by...

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Main Authors: Abdul Latiff, Abdul Halim, Ghosh, Deva Prasad, Abdul Latiff, Nurul Mu'azzah
Format: Article
Language:English
Published: Atlantis Press 2017
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Online Access:http://eprints.utm.my/id/eprint/77510/1/NurulMuazzah2017_OptimizingAcquisitionGeometryinShallowGasCloud.pdf
http://eprints.utm.my/id/eprint/77510/
https://www.atlantis-press.com/journals/ijcis/25883373
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spelling my.utm.775102018-09-24T01:22:10Z http://eprints.utm.my/id/eprint/77510/ Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach Abdul Latiff, Abdul Halim Ghosh, Deva Prasad Abdul Latiff, Nurul Mu'azzah TK Electrical engineering. Electronics Nuclear engineering Many hydrocarbon explorations in mature fields have been severely affected by complex and overburdening issues, such as shallow gas accumulation, gas pockets, and gas seepage. In this work, a new forward modelling technique is proposed in evaluating the potential survey design for fields affected by shallow gas cloud. In recent years, the implementation of innovative acquisition layouts has been producing significantly better seismic images, especially in the low illumination subsurface area. However, the uncertainty of the effectiveness in new acquisition design subsurface coverage always become a major stumbling block. To overcome this constraint, an optimization approach is suggested through the smart source and receiver location arrangement on the surface, with significant alignment to the conventional source and receiver arrangement approach. The particle swarm optimization (PSO) method is used to find the source-receiver configuration with maximum subsurface illumination coverage for the gas affected field situated in Malaysia Basin. Implementation of the PSO algorithm requires both a velocity model building process and wave field extrapolation from a target reflector to the surface level. The wave field data then was used to simulate receiver optimization outputs which eventually determined the subsurface illumination coverage. The results from the new optimization method for both synthetic model and Malaysia Basin data, offer a greater understanding of the consequences of obstacles caused by shallow anomalies with respect to seismic acquisition, data processing, and interpretation. Atlantis Press 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/77510/1/NurulMuazzah2017_OptimizingAcquisitionGeometryinShallowGasCloud.pdf Abdul Latiff, Abdul Halim and Ghosh, Deva Prasad and Abdul Latiff, Nurul Mu'azzah (2017) Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach. International Journal of Computational Intelligence Systems, 10 (1). pp. 1198-1210. ISSN 1875-6891 https://www.atlantis-press.com/journals/ijcis/25883373
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Abdul Latiff, Abdul Halim
Ghosh, Deva Prasad
Abdul Latiff, Nurul Mu'azzah
Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
description Many hydrocarbon explorations in mature fields have been severely affected by complex and overburdening issues, such as shallow gas accumulation, gas pockets, and gas seepage. In this work, a new forward modelling technique is proposed in evaluating the potential survey design for fields affected by shallow gas cloud. In recent years, the implementation of innovative acquisition layouts has been producing significantly better seismic images, especially in the low illumination subsurface area. However, the uncertainty of the effectiveness in new acquisition design subsurface coverage always become a major stumbling block. To overcome this constraint, an optimization approach is suggested through the smart source and receiver location arrangement on the surface, with significant alignment to the conventional source and receiver arrangement approach. The particle swarm optimization (PSO) method is used to find the source-receiver configuration with maximum subsurface illumination coverage for the gas affected field situated in Malaysia Basin. Implementation of the PSO algorithm requires both a velocity model building process and wave field extrapolation from a target reflector to the surface level. The wave field data then was used to simulate receiver optimization outputs which eventually determined the subsurface illumination coverage. The results from the new optimization method for both synthetic model and Malaysia Basin data, offer a greater understanding of the consequences of obstacles caused by shallow anomalies with respect to seismic acquisition, data processing, and interpretation.
format Article
author Abdul Latiff, Abdul Halim
Ghosh, Deva Prasad
Abdul Latiff, Nurul Mu'azzah
author_facet Abdul Latiff, Abdul Halim
Ghosh, Deva Prasad
Abdul Latiff, Nurul Mu'azzah
author_sort Abdul Latiff, Abdul Halim
title Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
title_short Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
title_full Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
title_fullStr Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
title_full_unstemmed Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
title_sort optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
publisher Atlantis Press
publishDate 2017
url http://eprints.utm.my/id/eprint/77510/1/NurulMuazzah2017_OptimizingAcquisitionGeometryinShallowGasCloud.pdf
http://eprints.utm.my/id/eprint/77510/
https://www.atlantis-press.com/journals/ijcis/25883373
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score 13.188473