Migration produces a representation of the earth’s reflectivity. With PGS LSM the imaging process is considered a least-squares problem, where the goal is to update the reflectivity, improving illumination and wavenumber content.
Image resolution is controlled by a number of factors including: acquisition parameters (source and acquisition geometry); earth properties (velocity and attenuation), and the migration algorithm. The influence of these factors can be reduced during acquisition and processing using: multisensor data; rich-azimuth acquisition geometries; accurate velocity models and compensation for attenuation.
Where the geological overburden is complex and the acquisition geometry leads to poor source and receiver surface coverage, both the illumination and wavenumber content of the migrated images can be suboptimal. This leads to uncertainty in interpretation and a lack of confidence in reservoir characterization. PGS LSM can solve this.
Any PGS migration algorithm can be used for the initial migration, and the application can be performed in either the image or data domain. Optimal processing of the initial migration along with regularization for the inversion helps reduce the resource requirements.
Whilst a standard migration produces an image of the earth, PGS LSM resolves the inadequacies in the migration image, producing a final data set, which is a reflectivity estimation from the LSM process. Dealing with the challenges faced by standard migration reduces the uncertainty for reservoir characterization.
The best implementation of LSM will be selected by our imaging experts on a case-by-case basis.