Imtiaz Ahmed

Advanced Subsalt Imaging and 3D Surface Multiple Attenuation in Atlantis: A Case Study

Atlantis sub-salt seismic image quality ranges from poor to unreliable over 60% of the anticipated areal extent of the field. Initial field development will focus on the betterimaged southern segments. Development of sub-salt reserves will be contingent on additional appraisal drilling and early field performance. Seismic data quality is limited due to the following: surface related multiple contamination; uncertainty in the sediment and salt velocity model; complex illumination due to the overlying salt geometry and salt fingers and a steeply dipping water bottom associated with the Sigsbee Escarpment. Recent seismic imaging efforts at Atlantis include the application of wavefield migration techniques, enhanced velocity model building, raytrace illumination studies and the application of 2D and 3D Surface Related Multiple Elimination (SRME).

Application of one‐way wave‐equation migration in tilted coordinates to salt‐model building at Atlantis

We apply shot-record wave-equation migration in tilted coordinates to 3D OBS data from the Atlantis field in the Gulf of Mexico for the purpose of salt-model building. This technique produces images on which steeply-dipping salt flanks can be picked with confidence, which is not possible on images computed with conventional one-way waveequation migration because of its inherent steep-dip limitations.

2D Wavelet Transform Domain Adaptive Subtraction For Enhancing 3D SRME

Surface related multiple elimination (SRME) (Berkhout 1982, Verschuur et al., 1992) is a very popular and effective algorithm for removing surface related multiples. The SRME method includes two steps: multiple modeling or multiple prediction followed by adaptive subtraction. The success of the SRME method depends on how well the predicted multiples match to the actual multiples in the data and on the success of the adaptive subtraction algorithm. This paper deals with the adaptive subtraction algorithm. There are two most common strategies for adaptive subtraction. The first strategy is posed as the least-squares minimization problem that minimizes the energy difference between the original data and the predicted multiples in the x-t domain. The second strategy is based on pattern-based adaptive subtraction (Spitz, 1999, 2000, Soubaras, 1994), which is based on the assumption that the primaries and multiples are predictable in the f-x domain. A detailed comparison study of different adaptive subtraction algorithms is discussed in the paper from Abma et al. (2005). One of the main conclusion from the paper was that the least-squares minimization technique is probably the best available adaptive subtraction algorithm at present, however when multiples strongly interfere with the primaries the technique is not as effective. This conclusion is the main motivation for this paper. The transformation of the data to the 2D stationary wavelet transform domain (SWT) (Nason et al., 1995) provides a potential dip separation to the data and thus gives an opportunity to separate interfering events. In this paper the implementation of the least-squares minimization technique in the 2D SWT domain is discussed.