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In addition to conventional P-wave refraction tomography, we propose a workflow based on the complementary use of Rayleigh waves. We develop a surface wave processing sequence to determine the S-wave near surface velocity field, which can be used as a constraint for P-wave tomography and statics determination. Rayleigh waves are processed in three steps. The first one consists of an accurate frequency-dependent travel-time measurement for each selected source-receiver pair, in which the phase difference between two adjacent traces is used to derive the phase velocity. Then, a frequency-dependent surface-wave velocity tomography is performed, in order to merge information coming from multi-azimuth and multi-offset picking. Finally, after surface wave tomography, the frequency-dependent phase velocity volume is inverted to deliver an S-wave near-surface velocity field. This model is then used as a constraint to the first arrivals P-wave tomography. To illustrate our method a conventional 3D Narrow-Azimuth land dataset is used, acquired around a river surrounded by hills. Strong lateral velocity variations are expected in the shallow part, with slow velocities around the unconsolidated sediments of the river bed, and faster velocities in the consolidated sediments of the surrounding hills. A combined first arrivals tomography using the depth S-wave velocity, the refracted P-wave velocity and their first arrivals is used to obtain a more accurate refracted P-waves model in the shallow part. Hence, the application of primary statics derived from this constrained refracted P-wave velocity model results in a much better image in the shallowest part with better focusing and continuity of thin layers.

TopSeis unique setting creates both new benefits and challenges for processing and imaging which, once solved, offer an ideal high resolution picture of the underground. This paper define faced challenges and how processing needs to be optimized in order to obtain brand new geological result.

We discuss the survey planning and provide results from a marine acquisition conducted northwest of the Shetland Islands. The survey was designed to image multiple targets from shallow Tertiary and Cretaceous plays, through to complex fractured Devono-Carboniferous reservoirs. Marine acquisitions are typically conducted along the direction of structural dip. In this case, this was not possible due to the proximity of the survey to the Shetland Islands. In an attempt to address this problem, triple sources were deployed to decrease the spacing between inlines and improve sampling along the structural dip direction. In addition, a second vessel was deployed broadside in a rich-azimuth configuration to increase offset coverage in the y-direction and to help undershoot sills and dykes present in the area. Activating so many sources sequentially would significantly decrease fold and increase noise levels of the data. After careful scenario testing and de-risking, which involved blending and deblending conventional data in various ways, the decision was made to acquire the data with simultaneous shooting. Data after deblending and depth imaging highlight the benefits provided by this rich-azimuth approach.

Gas absorption causes anelastic effects (described by the quality factor Q) and is a well-known source of amplitude loss and phase distortion in seismic data. Advances in our full waveform inversion technology mean it is now possible to compensate for phase distortion and amplitude loss by a joint update between velocity and the quality factor. In this paper we present the results of Q-FWI derived on dense, wide azimuth ocean bottom p-wave seismic for a North Sea dataset with gas present in the overburden. We see that when combined with Q-Reverse Time Migration a significant uplift in reservoir imaging is obtained.

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