TY  - JOUR
ID  - scholars9061
KW  - Data handling; Gases; Hydrocarbon refining; Seepage; Seismic prospecting; Seismology
KW  -  Acquisition geometry; Hydrocarbon exploration; Malaysia; Particle swarm optimization approaches; Particle swarm optimization method (PSO); Receiver optimizations; Shallow gas; Velocity model building
KW  -  Particle swarm optimization (PSO)
N2  - 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. © 2017, the Authors. Published by Atlantis Press.
IS  - 1
VL  - 10
JF  - International Journal of Computational Intelligence Systems
A1  - Latiff, A.H.A.
A1  - Ghosh, D.P.
A1  - Latiff, N.M.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044790241&doi=10.2991%2fijcis.10.1.79&partnerID=40&md5=68a20ad6d2ea05ec548e7aaff3a206f7
Y1  - 2017///
SP  - 1198
TI  - Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach
N1  - cited By 4
AV  - none
EP  - 1210
PB  - Atlantis Press
SN  - 18756891
ER  -