Abstract
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.
Highlights
The presence of shallow subsurface anomalies within high velocity contrast environments are the biggest obstacles in obtaining a good and clear seismic section
The usage of particle swarm optimization (PSO) for optimization process has a significant advantage over other optimization algorithm, Genetic Algorithm (GA), as it converges to global position at the faster and more accurate rate
The development of the PSO procedure is influenced by irregular receiver positions during the seismic acquisition process
Summary
The presence of shallow subsurface anomalies within high velocity contrast environments are the biggest obstacles in obtaining a good and clear seismic section. The low illumination related problems can be solved through two schools of thought; i) re-acquisition of seismic data in the field of interest, ii) developing a new and advanced seismic imaging algorithm Both streams are required to honor the seismic value chain[1] in implementing several noise and multiple removal steps and rigorous velocity analysis before a solution for the true subsurface imaging. The particle swarm optimization (PSO) method is a non-linear function concept where the algorithm tries to simulate real-life movement like particle swarming or bird flocking and solves problems by minimizing or maximizing parameters involved within a closed environment[11]. This is based on a defined cost function.
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