Illustration of how machine learning application is used for Seismic global QC and detailled clusterization of data for tailored processing. Data reduction with deep neural network shows great efficiency to limit memory issue.
Reprocessing of available seismic dataset using high-end processing technology was seen to be the most economical solution for search of deeper, pre-carbonate targets in a marginal field. The vintage data suffered from amplitude loss due to absorption by gas bodies and carbonate layers causing poor reflector continuity especially at target zone. Removal of noise and multiples beneath the carbonate layer was also essential to image the deeper fault systems properly. Even with narrow azimuth and limited offset data, which has illumination issues, current advanced imaging technologies were able to unmask the reservoir.
We illustrate a new case study based on our land FWI workflow. We also demonstrate the impact of optimal transport (OT) FWI on the resulting velocity model, which aims to mitigate cycle skipping. We demonstrate how it corrects several localized failures with conventional FWI and constructs the geologically consistent velocity model.
Placing marine sources on the top of the seismic streamer spread improves resolution and reservoir inversion in shallow and intermediate depth targets. This is due to the abundance of near offset data and high level of illumination compared to conventional marine seismic. A drawback of this top source solution is the lack of long offsets, which are important for deeper imaging and AVO. In this paper, we present a combined solution with both sources on top and in the front of the spread. We deploy the front sources from the vessel that also is towing the streamers, while a separate source vessel is towing the top sources. This gives an operationally efficient top-to-bottom solution to the seismic imaging and inversion problem solving both high and low targets. We will show examples of real data with up to six top sources and describe the solutions to the deblending challenges.
Shallow stratigraphy in Southern Oman is characterized by the presence of a shallow anhydrite layer causing a strong velocity inversion which makes seismic imaging particularly tedious. This known shallow sharp velocity inversion cannot be easily captured with reflected waves based techniques or even acoustic full waveform inversion. We propose to recover it applying multi-wave inversion, an approach combining information from first breaks of P waves and from dispersion curves picked on ground-roll. In addition, an unsupervised machine learning technology is used to improve the quality of surface wave dispersion curve picks, crucial for the reliability of the multi-wave inversion results. With this innovative approach, the joint inversion of first breaks and surface waves leads to an enhanced and high resolution P-wave velocity model of the near surface along with an improvement in the depth imaging.
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.