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This paper describes the construction of a high-resolution anisotropic FWI model that is used to help image a karstified carbonate layer and also to help define the underlying clastic reservoirs. The karst zones show extreme velocity rugosity, especially as compared to the surrounding sediments. Conventional reflection tomography cannot resolve the anomalous velocities, and the resulting seismic images below these karstified carbonates are distorted and poor for interpretation. This paper presents the strategies for determining the appropriate input data for FWI and how to build a multi-layer horizon-constrained anisotropic model and the uplift that results from using Q-factors .

Traditionally, the regularization step is performed independently for each time-lapse vintage. This disregards any geometrical limitations imposed by different surveys. Here we recast the regularization process as a minimization problem with model-space constraints. These constraints couple geometrical relations between surveys to improve repeatability. We also demonstrate how to solve the minimization using a practical and pragmatic approach.

In this paper a comparison of deterministic and geostatistical inversion approaches to reservoir characterization of a complex gas saturated clastic reservoir with thin coal beds will be discussed. This type of reservoir is characterized by high acoustic contrasts which cause challenges for inversion techniques. To overcome these challenges in deterministic inversion a methodology was applied to update the low frequency model in an iterative manner. This improves the accuracy of elastic properties prediction and, therefore resulting in a more reliable reservoir model, which was verified with data from 28 newly drilled wells.

Water column statics caused by tidal variation and water velocity change during seismic surveys is one major source of noise in marine 4D projects. Correction of this statics effect is a key step in any marine 4D processing. Applying water column statics correction requires a good knowledge of the distance or surface take-off angle when waves travel through the water column, which conventional methods such as ray tracing are not able to obtain accurately when the subsurface velocity is complex. We propose a new method to apply water column statics correction through progressive sparse TauP inversion. This method does not need prior inputs of subsurface velocity and reflector dips, as required for ray-tracing methods, and benefits from the progressive sparse TauP inversion engine that can properly handle spatially aliased marine seismic data and mitigate energy leakage in the TauP domain. We demonstrate the effectiveness of this new method using synthetic ocean bottom seismometer (OBS) data derived from a SEAM velocity model and using real OBS data from 4D surveys over the Atlantis field in the Green Canyon area of the Gulf of Mexico (GOM).

Surface wave inversion (SWI) for S-wave velocity plays an important role in near surface characterization and PS-wave velocity model building for depth migration. A hybrid approach is proposed to reduce non-linearity for multi-modal inversion without a-priori identification of higher order modes. This method is applied to north sea OBN data and produce a shallow Vs model that penetrates 120m depth below seabed. Vp/Vs ratios are then calculated as a powerful tool for near-surface characterization. SWI provides a high-resolution shallow Vs model which complements the weakness of PS-wave tomography in illuminating the near surface.

The compensation of absorption loss inside the imaging process using attenuation models estimated by Q-tomography is now widely accepted and used in the industry. This technology becomes even more important in the case of a complex dataset. For the Martin Linge field, characterized by a strong presence of faults and gas clouds, the multi-azimuth broadband acquisition involving a variable-depth streamer has helped to improve the quality of the data. High-end processing and imaging so far provided an image with enhanced resolution compared to legacy data mainly with regard to faulting in the deeper area. Nevertheless, imaging remained poor in the deeper part of the section because of a seismic obscured area (SOA) caused by gas clouds. In this paper we now illustrate how we managed to enhance the resolution under this SOA zone using volumetric Q-tomography and Q-prestack depth migration (Q-PSDM). In other words, we show that multi-azimuth broadband acquisition combined with Q-tomography/Q-PSDM techniques can provide an improved final image in the case of a complex data with a SOA.

Self-published 12-page White Paper on the subject of National Data Repositories. Distributed at NDR conference in June 2017.

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