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Time series are analysed to detect changes and predict future behaviour. Monitoring of the surface or subsurface in Geoscience provides such time series. As new data becomes available, changes of regime are detected. They should be identified as early as possible with as few false positive as possible. In this paper, we define the ideas of regime and change of regime in a time series. We then give an overview of the Bayesian method used to detect these changes. The principles are illustrated with an application for the detection of a mine tailings dam failure using InSAR satellite data.

2D deconvolution is an attractive approach for short period multiple prediction as it does not require direct recording of the multiple generator and can model multiples relating to more than one multiple generator at a time. One drawback, however, relates to an inherent over prediction of mixed side multiples leading to an inconsistency in the amplitude of multiple predictions from one multiple order to the next. We present a modified form of predictive deconvolution that iteratively refines the prediction operator and prevents the over prediction of higher order multiples. The resulting multiple prediction is consistent with the amplitude of multiples in the recorded data, reducing the necessity for adaptive subtraction. The algorithm may be applied in 2D or 3D, or alternatively with a receiver only side 3D implementation suited to towed streamer geometries. The effectiveness of the algorithm is demonstrated on synthetic data as well as on two towed streamer 3D seismic data examples acquired in the North Sea.

Solving fault shadow imaging problems has been a long-existing topic when heavy faults are present and the seismic imaging underneath is distorted. With full-waveform inversion (FWI), they are usually less of a concern when their depths are within diving wave penetration. However, beyond diving wave penetration, we have to rely on reflection energy, either by various tomographic methods or reflection FWI, both of which require special considerations in order to effectively resolve small-scale velocity anomalies associated with faults. In this paper, we present applications of fault-constrained tomography (FCT) and Time-lag FWI (TLFWI) with a weighted tomographic term for deep fault imaging using a streamer dataset with limited offsets from the South China Sea. Our learnings show FCT heavily hinges on high-quality common image gather (CIG) and fault picking. With a weighting factor to promote the tomographic term from reflection energies, the TLFWI workflow successfully corrected fault shadows and demonstrated advantages over FCT for resolving velocity anomalies in areas of complex faults.

We present the main results of a tailored velocity model building workflow on a recent broadband survey from North Kuwait. Depth imaging in Kuwait presents several challenges, including the need to capture the strong velocity variations of a complex near surface that generates long and short wavelength distortions, the need for a detailed velocity model to accurately restore the structures of low-relief and faulted Cretaceous and Jurassic traps, and the difficult imaging of the Paleozoic structure hidden below a curtain of multiples. First, we give insight on a Multi-Wave Inversion and Full-Waveform Inversion workflow, which exploited the finely sampled surface waves and the diving waves to derive a high-resolution near-surface P-wave velocity model. Then we show how high-definition Multi-Layer Tomography is able to capture the complex velocity variations within the Cretaceous, which helps resolve imaging distortions. Finally, we focus on the imaging of the Paleozoic structures, which are key to understanding the regional geological history.

Applications of full-waveform inversion (FWI) to land data have proven much more challenging than to marine data. The difficulties are linked to a lower signal-to-noise ratio but also to a greater influ-ence of elastic wave phenomena in these data sets, especially those characterized by strong elastic property contrasts. The Middle East, where FWI-dedicated acquisitions and pre-processing work-flows have been developed, has emerged as a promising proving ground for land acoustic FWI. But it also proved to be challenging due to strong elastic effects from alternating fast and slow velocity layers in the shallow section. Elastic land FWI then appears as a natural solution to be investigated, especially considering that it becomes more practical thanks to the progresses of the computing ca-pacities. We study the potential of elastic land FWI to overcome the limitations of acoustic land FWI, through a set of synthetic and real data applications to typical challenging areas from the Mid-dle East. We show the improved data fitting leading to an increased resolution and stability that can be obtained with elastic land FWI compared to acoustic land FWI when inverting diving waves. We also present some preliminary inversion results of ultra-low frequency surface-waves obtained by interferometry.

Fizz gas detection is a critical step in field development as it is often difficult to differentiate it from commercial gas in reservoirs on acquired seismic data. In a shallow reservoir context, it is theoretically possible to observe a fizz gas effect through a joint analysis of P-wave velocity (Vp) and density (Rhob) properties. Although inverting density is routinely done in pre-stack inversion, it is generally too strongly coupled to Vp to achieve an efficient fizz gas characterization through a Vp versus Rhob crossplot. This paper presents a pre-stack inversion case study conducted in offshore North Sabah, Malaysia, to characterize Late and Middle Miocene clastic, gas-bearing deposits. Very high-quality pre-stack seismic data allowed for the possibility to partially decouple Vp and density through an adequate inversion parameterization. Supported by fluid change predictions coming from a petro-elastic model calibrated to the field conditions, a fizz gas characterization routine could be established through a joint analysis of the inverted Vp and Rhob properties, giving access to a potential fizz gas detection over the targeted reservoir.

The Haugaland High, in the Norwegian North Sea, consists of a layered overburden of sub-horizontal sediments of almost 2km thick which sits on the Cretaceous Chalk. The background velocity regime of these top layers has a low vertical gradient down to the Chalk interface. This velocity behavior is particularly poorly suited for diving wave FWI and the strong multiple content present in the data does not allow an efficient tomographic update. Using all reflection and diving waves recorded in the Time-Lag FWI has allowed to provide a high-resolution velocity field, well explaining the complex velocity variation present in the overburden and easing the reservoir imaging. With the use of narrow azimuth towed streamer covering 2000km2, the velocity was updated up to 40Hz both helping structural imaging and bringing additional information to better understand the rock property of the basement over the entire region.

This study aimed to provide greater insight into the question of whether a near-field Paleozoic interval could retain sufficient reservoir quality to be attractive for future exploration. To meet this challenge, an integrated multidisciplinary approach was adopted that brought together detailed geological analysis with seismic inversion. Both of which were complimented by synchronized concurrent seismic reprocessing. The project workflow began with a stratigraphic review before investigating the detailed sedimentology and diagenesis for reservoir characterisation, with the latter incorporating burial history modelling to constrain the role of compaction. The results of seismic attribute analysis and seismic inversion performed during data reprocessing were then integrated with the geological story to generate a truly holistic reservoir quality evaluation.

To improve the imaging of the Barents Sea’s Nordkapp basin, either in terms of resolution or geological structure, a new seismic acquisition design was proposed, using a widespread hexa-source sitting on top of 18 non-flat streamers. Imaging this new recorded data required specific and complex processing steps using advanced algorithms to maintain high spatial resolution. This were not easy to handle onboard or through a fast-processing flow, so we implemented an Ultra-Fast Track processing by leveraging the capability of Deep Neural Networks to perform the pre-processing stages, ensuring quality in a limited timeframe. Pseudo-synthetic training sets were built from the first batch of received data and data augmentation was applied, with different setting for each processing steps. This result showed an improved resolution with respect to the legacy volume and was used to start interpreting potential hydrocarbon prospects and initiate the velocity model building. The Ultra-Fast Track also served as a helpful tool to assess the remaining challenges to be faced during the full processing.