Conventional imaging does not deal adequately with absorption, especially in the case of strong anomalies. Over recent years, many authors have proposed to compensate the absorption loss effects inside of the migration through the use of an attenuation model. Q tomography has been developed for estimating this attenuation model but is generally limited to estimating attenuation in predefined anomalies. In this paper, we explain how we developed a high-resolution volumetric Q tomography to attain an accurate volumetric estimation of the attenuation model. A key component of our workflow is the estimation of effective attenuation in the pre-stack data domain through accurate picking of the frequency peak. Finally we present a case study where our approach has been used to reveal shallow gas pockets and compensate for absorption in the migration.
The computation of velocity by tomography using Residual Move-Out (RMO) curves is widely used in the industry. The semblance-based, automatic dense picking of RMO has proved to be a very efficient technique for conventional data. However, in the case of variable-depth streamer acquisition, this technique is poorly adapted due to the presence of ghosts in the data. We present here a robust, automatic, high-order dense picking technique based on a cross-semblance criteria using the migrated and mirror-migrated common image gathers that eliminates the need for pre-stack deghosting prior to velocity update. This paper focuses on velocity model building using our method. It is applied to a complex 2D variable-depth streamer acquisition from Brunei, which is characterized by noisy data where faults and gas pockets coexist. We show that our method allows us to obtain an accurate velocity function conformable to structure that improves the focusing.
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