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Seismic data must be optimally processed for accurate reservoir characterization, and therefore each step of the processing sequence should be carefully designed and assessed for AVA compliance and primary preservation. In this paper, we focus on the demultiple, a critical step as it can be difficult to identify and separate primaries and multiples, especially in shallow water settings and at the low frequencies typical of broadband data. We first propose a new AVA quality control via a pre-defined pre-stack classification method. In a second step, we show three examples of directly incorporating the AVA preservation into the cost functions of existing algorithms, such as the Radon transform and a new adaptive subtraction scheme.

In principle, high quality pre-stack converted wave (PS) data can provide valuable complementary information to PP data to highlight seismic amplitude anomalies in areas with complex imaging problems such as steeply dipping structures and obscured areas. However, in practice, we often find that PS images are noisier than the corresponding PP images. In addition, the amplitude versus offset (AVO) behaviour of PS-gathers generally proves difficult and joint PP-PS AVO is a struggle. In this paper, we propose to employ specular imaging for converted waves. Benefits of specular imaging with dip-angle migration have been widely shown both for 3D imaging and 4D monitoring of P-wave data. Here we apply the specular imaging method to an OBC dataset from the UK North Sea to enhance the PS images. We show that by selection of specular energy in the dip-angle domain PS images are significantly less noisy and migration artefacts reduced. The AVO compliance of specular migrated gathers is significantly enhanced.

New high-quality seismic datasets and regional prospectivity study brings greater understanding of the distribution and extent of resource plays throughout the Banda Arc.

Data science-based methods, such as supervised neural networks, provide powerful techniques to predict reservoir properties from seismic and well data without the aid of a theoretical model. In these supervised learning approaches, the seismic to rock property relationship is learned from the data. One of the major factors limiting the success of these methods is whether there exists enough labelled data, sampled over the expected geology, to train the neural network adequately. To overcome these issues, this paper explores hybrid theory-guided data science (TGDS)-based methods.

The current climate in the oil exploration industry has engendered a strong push towards efficiency in acquisition. One technique that offers this is synchronized simultaneous sources, or SyncSource, where sources are fired before completion of recording the data from the previous shot. This may result in significant overlap of seismic data between successive shotpoints, so that the data must be de-blended to recover the individual contribution from each source. However, this enables the acquisition of data with higher trace density, smaller bins or longer records than would otherwise be possible. We describe the use of blended acquisition to acquire ultra-long offset data offshore Gabon

Making informed field development decisions requires taking uncertainty into account. Geostatistical inversion is a key technology for quantifying uncertainties using available seismic and well data. However, the common practice, consisting of choosing the "best possible" parameters, results in unrealistically small uncertainty estimates. In this paper, we propose a multi-scenario approach to geostatistical inversion. By considering various alternative scenarios, a more realistic picture of the overall uncertainty can be built. This is illustrated on a case study, where the traditional single-scenario practice and the proposed multi-scenario approach are compared.

The size and complexity of the mega gas clouds in offshore Brunei pose severe imaging problems to the structures underneath. We present a comprehensive technical package to tackle the complex wave propagation and anelastic energy losses associated with these gas clouds. We started by running FWI to resolve the velocity of the shallow gas clouds, followed by reflection tomography. We then conducted FWI-guided Q tomography to obtain high-resolution absorption model. For the deep gas clouds, since their depth and the incurred low signal-to-noise ratio in the CMP gathers are beyond the limit of these geophysical methods, we moved on with geologically-guided scenario testing in intense collaboration with geologists. Finally, we carried out visco-acoustic TTI reverse time migration (Q TTI RTM) to better deal with the issues of multi-pathing and strong attenuation. This complete package brings significant uplift to the image as compared to the vintage QPSDM result, and therefore can serve as an effective option before turning to C-wave imaging.

Recent discoveries in the Pil and Bue prospects, targeting Melke and Rogn Formation sandstones on the footwall of the Vingleia Fault complex, have rejuvenated interest in exploration around the Frøya High region. The wildcat well, 6406/12-3 S (Pil 1), with estimated resources of 8.8-21.1 MMSCM, has focused current attention on combined structural-stratigraphic traps associated with proximal rift sedimentation, restricted to the eastern margins of the Southwest Basin, Halten Terrace. This study provides an integrated reservoir quality assessment of the Late Jurassic Viking Group in the Frøya High region, focusing on sand distribution and reservoir quality.

This paper focusses on improving the seismic image on a dataset from offshore Trinidad; Tobago by using full-waveform inversion (FWI) to refine the shallow velocities and also describes geological information that can be inferred directly from the resulting velocity model. In the first half of the paper the geology of the region is introduced, then the imaging issues, before describing the FWI methodology and results. The second half of the paper discusses interpretational aspects of the FWI velocity model and highlights its use in a blind pore pressure prediction (PPP) test of well data.