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The oil-bearing fractured granite basement rocks form a very important and complicated hydrocarbon reservoir in Cuu Long basin offshore Vietnam. However, the poor fractured basement imaging in the conventional tow streamer data makes it hard for detailed interpretation and future well placement. To improve the seismic imaging, the first 3D/4C OBC acquisition over the field was carried out to provide better illumination, better elimination of multiples and broader spectrum with better signal to noise ratio. However, the presence of strong azimuthal anisotropy poses a serious challenge in imaging this OBC data with the full azimuth (FAZ) nature. The steeply dipping fracture imaging can be smeared if the subsurface orthorhombic (ORT) velocity model is not properly derived. In this paper, we present a new orthorhombic velocity model building workflow to estimate the azimuthal anisotropic velocity by incorporating shear wave splitting analysis, well formation microimager (FMI) information and 3D RTM subsurface angle gather based velocity sweeping inside basement. Two geological layers with strong azimuthal anisotropy are identified and incorporated into the final ORT model which results in much shaper imaging not only in shallow classic sediment layers but also in the fractured basement.

After many years of processing seismic streamer data acquired over the Shenzi field in deep-water Gulf of Mexico (GOM) with the best available technology, the workflows have remained highly interpretive, far from robust, often time-consuming, and ultimately proved inadequate for resolving the complexity of the salt and subsalt velocity models. Recent advances in full-waveform inversion (FWI) algorithms combined with a newly acquired, ocean bottom node (OBN) data set have ushered in a fundamental shift in velocity model building and imaging at Shenzi as we move away from mostly interpretation-driven to more data-driven processes. While the velocity model obtained from Time-lag FWI (TLFWI) using the legacy rich-azimuth (RAZ) streamer data provided significant imaging uplift over the best previous processing, an additional step-change improvement in image quality was achieved by running TLFWI with the OBN data. The OBN TLFWI model combined with the long offsets of the OBN data allowed steeply dipping events along the salt feeder to be seen for the first time on the reverse time migration (RTM) image. However, despite these large improvements, the image still suffers in areas of low illumination. Least-squares RTM (LSRTM) showed some good improvement over the RTM but proved less effective where the starting image was too poor. By running TLFWI to a maximum frequency comparable to the RTM, we were able to generate an FWI Image, an estimation of the reflectivity obtained directly from the FWI velocity model, with good S/N, more balanced amplitudes, and improved steep-dip and fault imaging, making it a direct rival to the RTM and LSRTM images.

Driven by the notion that blending noise may materially increase the background noise level and obscure the interpretation of weak time-lapse (4D) signals related to subtle reservoir changes, the industry has not yet seen any simultaneous-source (sim-source) surveys acquired for reservoir monitoring. Thus, whether sim-source acquisition is feasible for 4D remains a long-standing question. In 2019, BP took the step forward to acquire the first sim-source ocean bottom node (OBN) monitor survey at the Atlantis field in the Gulf of Mexico (GoM). With 4D-friendly deblending and matching of sources from different vessels, we were able to mitigate the challenges associated with the 2019 independent simultaneous source (ISS) OBN survey and obtain a similar level of 4D signal-to-noise ratio (S/N) and valuable 4D signals comparable to what we could achieve with conventional OBN data. Further, meaningful subsalt 4D signals were revealed for the first time in the areas with fairly poor illumination even with the 2019 ISS OBN survey, partly due to the larger reservoir changes from a longer production history and a more accurate velocity built from full-waveform inversion (FWI).

Shear wave velocity model building (S-wave VMB) is a critical and difficult processing step for converted wave imaging. Conventional S-wave VMB depends on PP-PS joint interpretation-based image registration and PP-PS joint tomography-based residual moveout flattening which have certain advantages and drawbacks. We introduce PS reflection FWI (PS-RFWI) for S-wave VMB in the presence of a complex, heterogeneous subsurface, which can address some of the concerns with conventional approaches. This method assumes that we have previously obtained sufficiently accurate pressure wave (P-wave) velocities and reflectivity. PS-RFWI solely updates S-wave velocity by minimizing the kinematic difference between the modeled and recorded PS reflections, while leaving the P-wave parameters unchanged. In conjunction with conventional methods, PS-RFWI can provide a superior PS image, as we will demonstrate with a field data example from offshore Malaysia.

Although the resolution of a seismic image is ultimately bound by the spatial and temporal sampling of the acquired seismic data, the seismic images obtained through conventional imaging methods normally fall far short of this limit. In addition to attenuation in the Earth, factors such as velocity errors, illumination holes, residual noise and multiples, source and receiver ghost notches, and migration swings can prevent conventional imaging methods from obtaining a high-resolution image of good signal-to-noise ratio (S/N) and well-focused details as promised by the maximum migration frequency. Recently, FWI Imaging, which uses the full-wavefield data to iteratively invert for the reflectivity together with velocity through full-waveform inversion (FWI), has shown to be a superior method for providing seismic images of greatly improved illumination, S/N, focusing, and thus better resolution, over conventional imaging methods. Here, we push FWI Imaging to a frequency close to the temporal resolution limit of seismic data (100 Hz) and demonstrate that FWI Imaging at a very high frequency can provide seismic images of unprecedented resolution from the recorded data, which has been impossible to achieve by other seismic imaging approaches.

The Cornerstone OBN survey has unquestionably brought a step-change in image quality compared to towed-streamer acquisition, especially for deeper, sub-BCU reservoirs and around salt diapirs where reservoir units are often heavily faulted and steeply-dipping. It will allow for the effective development and monitoring of producing assets and facilitate near-field exploration.

A broadband Wide-Azimuth Towed-Streamer (WATS) survey was acquired in a shallow water region of offshore China for the purpose of resolving strike direction narrow-azimuth acquisition shortcomings. However, the current WATS acquisition is much sparser than WATS surveys in deep water environments. The challenges of sparse acquisition, shallow water, and deep targets, require optimal utilization of the side-gun data as the key to final imaging improvements. This paper presents a comprehensive flow for resolving the imaging challenges of de-ghosting and de-multiple for sparse and shallow wide-azimuth data. Side-gun data clearly enhances the final target reservoir imaging and better ties the well. Contribution analysis further provides valuable guidance for future acquisition designs in similar geographical and geological conditions. It is recommended that the 2nd pass source-boat should be nearer to the streamer vessel in order to obtain better azimuth distribution.

The importance of detailed reservoir characterization and modelling for accurate reservoir performance prediction has always been recognized, but generally it is necessary to upscale reservoir properties derived from seismic data to create models that can be run in a reasonable timeframe and are a useful tool for prediction. CGG has combined its strengths in high-resolution seismic imaging and its high-performance computing (HPC) capability into a new modelling workflow which allows preservation of the high-resolution seismic information directly into the dynamic reservoir simulation model. These models can be used for field development planning and production optimization.

Hydrocarbon energy resources have been extracted from the Earth for many decades using in-depth technical knowledge of its subsurface. With the need to achieve net zero goals, many traditional hydrocarbon companies are reducing the carbon intensity of their operations and investing in renewable energy and carbon offsetting, repurposing existing hydrocarbon infrastructure and technology to support this transition. Subsurface technical specialists across the energy sector have decades of knowledge and skills that can help to accelerate decarbonisation. In this article, we summarise some of our recent multi-disciplinary projects that demonstrate the valuable role geoscience can play in the energy transition, particularly when supported by data science, high performance computing (HPC) and other technologies.