Data-driven interferometry method to remove spatially aliased and nonlinear surface waves

Chiffot, Corentin; Prescott, A.; Grimshaw, M.; Oggioni, Francesca; Kowalczyk-Kedzierska, M.; Cooper, Sharon; Meur, D. Le; Johnston, Rodney

doi:10.1190/segam2017-17724305.1

Cited by 8 publications

References 13 publications

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High-resolution 3D seismic imaging and refined velocity model building improve the image of a deep geothermal reservoir in the Upper Rhine Graben

et al. 2020

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Over the past 35 years, geothermal projects have been developed in the Upper Rhine Graben (URG) to exploit deep geothermal energy. Underneath approximately 2 km of sedimentary deposits, the deep target consists of a granitic basement, which is highly fractured and hydrothermally altered. Therefore, it has high potential as a geothermal reservoir. Despite dense 2D seismic data coverage originally acquired for oil exploration (for a target two-way traveltime between 300 and 700 ms), the faults at the top of the granitic basement (between 1400 and 4000 ms) are poorly imaged, and their locations remain uncertain. To gain a better understanding of this large-scale faulting and to ensure the viability of future geothermal projects, a 3D seismic survey was acquired in the French part of the URG during the summer of 2018. This paper describes how an integrated project, combining seismic data processing, high-end imaging, and enhanced interpretation, was conducted to improve the understanding of this complex basin for geothermal purposes. By revealing the deep granite layer and its complex associated fault network, the insight from this project can help accurately locate future production wells.

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High-resolution 3D seismic imaging and refined velocity model building improve the image of a deep geothermal reservoir in the Upper Rhine Graben

et al. 2020

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Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

Donno¹,

Farooqui²,

Khalil³

et al. 2021

The Leading Edge

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The near surface in the Middle East, particularly in the Sultanate of Oman, is characterized by very shallow high-velocity carbonates and anhydrites interleaved by slow-velocity clastic layers, resulting in sharp velocity inversions in the first few hundred meters below the surface. In addition, the surface is characterized by features such as unconsolidated materials within dry riverbeds (known as “wadis”), small jebels, and sand dunes, which cause distortions in the underlying shallow and deeper seismic images. This work presents the building of a near-surface model by using multiwave inversion that jointly inverts information from P-wave first breaks and surface-wave dispersion curves. The use of surface waves in combination with first breaks captures the lateral and vertical velocity variations, especially in the shallowest parts of the near surface. This paper focuses on the analysis of two drawbacks of this technology: the picking of the input data information, which can be cumbersome and time consuming, and the limited penetration depth of surface waves at the typical frequencies of active data. To overcome these issues, an innovative workflow is proposed that combines the use of an unsupervised machine learning technique to guide the pick extraction phase and the reconstruction of ultra-low-frequency surface waves (0.5 to 1.5 Hz) through an interferometry process using information from natural and ambient noise. Deeper near-surface P- and S-wave velocity models can be obtained with multiwave inversion using these ultra-low frequencies. The integration of a near-surface model into the velocity model building workflow brings a major improvement in depth imaging from shallow to deep structures, as demonstrated on two data sets from the Sultanate of Oman.

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Application of Advanced Seismic Processing to Old Seismic Data for Enhanced Fault Plane Imaging: A Case Study from Offshore Abu Dhabi

Paul

Khakimov

Barajas

et al. 2019

Day 1 Mon, November 11, 2019

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Low fold poorly sampled vintage seismic data often suffers from poor fault imaging. This can have a critical impact on reserve estimation and well planning. Acquiring high density seismic data over producing fields requires overcoming logistic challenges along with additional costs and increased acquisition time. However, advances in seismic processing technology could improve the fault resolution of vintage seismic data in a cost effective manner. This has been proven in a case study from offshore Abu Dhabi. The presence of strong surface wave energy, resulting from the shallow water environment and near surface heterogeneity, masked events in the deeper part of the section. Poor and irregular spatial sampling caused aliasing of the surface wave. In the vintage processing, strong de-noising was applied to tackle the aliasing issue, which smeared the fault definitions. During the re-processing, a joint low-rank and sparse inversion was applied to regularize and densify the input data to obtain a de-aliased surface wave noise model. Subsequent adaptive subtraction of the noise from the input removed strong surface waves without damaging the body waves. The stack quality was improved by application of cascaded surface wave attenuation algorithms. Additional five dimensional Fourier reconstructions of the data improved the signal quality. A carefully designed fault-preserving residual noise attenuation workflow further reduced the residual noise content. Automatic picking of key stratigraphic horizons was carried out in order to evaluate the spatial resolution of the re-processing outcome. Sharper discontinuities along fault planes observed compared to the interpretation of the vintage seismic data. Increased confidence in fault interpretation is of value for structural restoration study and further reservoir understanding. In addition, several new, previously not-visible, small fault features were highlighted as evident from volumetric curvature and semblance analysis. They have been effectively utilized in a forthcoming drilling campaign to de-risk well operation. Multi-dimensional data densification to de-alias surface waves and five dimensional re-construction of the signal proved to be beneficial to enhance the fault features on the poorly sampled seismic data.

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Data-driven interferometry method to remove spatially aliased and nonlinear surface waves

Cited by 8 publications

References 13 publications

High-resolution 3D seismic imaging and refined velocity model building improve the image of a deep geothermal reservoir in the Upper Rhine Graben

High-resolution 3D seismic imaging and refined velocity model building improve the image of a deep geothermal reservoir in the Upper Rhine Graben

Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

Application of Advanced Seismic Processing to Old Seismic Data for Enhanced Fault Plane Imaging: A Case Study from Offshore Abu Dhabi

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