Target-oriented joint least-squares migration/inversion of time-lapse seismic data sets

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. R61-R73 ◽  
Author(s):  
Gboyega Ayeni ◽  
Biondo Biondi
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Time Lapse ◽  
Data Sets ◽  
Reservoir Properties ◽  
Phase Distortions ◽  
Time Lapse Imaging ◽  
Least Squares Migration ◽  
Seismic Images ◽  
Degradation Time

Two related formulations are proposed for target-oriented joint least-squares migration/inversion of time-lapse seismic data sets. Time-lapse seismic images can be degraded when reservoir overburden is complex or when acquisition geometries significantly differ, because the migration operator does not compensate for the resulting amplitude and phase distortions. Under these circumstances, time-lapse amplitudes are poor indicators of production-related changes in reservoir properties. To correct for such image degradation, time-lapse imaging is posed as joint inverse problems that utilize concatenations of target-oriented approximations to the linear least-squares imaging Hessian. In both formulations, spatial and temporal constraints ensure inversion stability and geologically consistent time-lapse images. Using two numerical time-lapse data sets, we confirmed that these formulations can attenuate illumination artifacts caused by complex overburden or geometry differences, and that they yield better-quality images than obtainable with migration.

Time-lapse imaging with joint least-squares migration

2019 ◽  
Author(s):  
Xuefeng Shang ◽  
Henning Kuehl ◽  
Michael Kiehn ◽  
Bruce Strawn ◽  
Jonathan Sheiman ◽  
...  
Keyword(s):  
Least Squares ◽  
Time Lapse ◽  
Time Lapse Imaging ◽  
Least Squares Migration

Common-focus point-based target-oriented imaging approach for continuous seismic reservoir monitoring

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. M41-M48 ◽  
Author(s):  
Hongwei Liu ◽  
Mustafa Naser Al-Ali
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Data Sets ◽  
Data Set ◽  
Velocity Models ◽  
Reservoir Monitoring ◽  
Focus Point

The ideal approach for continuous reservoir monitoring allows generation of fast and accurate images to cope with the massive data sets acquired for such a task. Conventionally, rigorous depth-oriented velocity-estimation methods are performed to produce sufficiently accurate velocity models. Unlike the traditional way, the target-oriented imaging technology based on the common-focus point (CFP) theory can be an alternative for continuous reservoir monitoring. The solution is based on a robust data-driven iterative operator updating strategy without deriving a detailed velocity model. The same focusing operator is applied on successive 3D seismic data sets for the first time to generate efficient and accurate 4D target-oriented seismic stacked images from time-lapse field seismic data sets acquired in a [Formula: see text] injection project in Saudi Arabia. Using the focusing operator, target-oriented prestack angle domain common-image gathers (ADCIGs) could be derived to perform amplitude-versus-angle analysis. To preserve the amplitude information in the ADCIGs, an amplitude-balancing factor is applied by embedding a synthetic data set using the real acquisition geometry to remove the geometry imprint artifact. Applying the CFP-based target-oriented imaging to time-lapse data sets revealed changes at the reservoir level in the poststack and prestack time-lapse signals, which is consistent with the [Formula: see text] injection history and rock physics.

Relative time seislet transform

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V223-V232 ◽  
Author(s):  
Zhicheng Geng ◽  
Xinming Wu ◽  
Sergey Fomel ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Computational Cost ◽  
Lifting Scheme ◽  
Real Data ◽  
Data Sets ◽  
Relative Time ◽  
New Approach ◽  
New Formulation ◽  
Wavelet Lifting ◽  
Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

Edge-aware filtering with Siamese neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 711-717
Author(s):  
Mehdi Aharchaou ◽  
Michael Matheney ◽  
Joe Molyneux ◽  
Erik Neumann
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Learning Ability ◽  
Seismic Exploration ◽  
Data Sets ◽  
3D Seismic ◽  
New Class ◽  
Seismic Image ◽  
Siamese Networks ◽  
Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

Time‐lapse imaging at Bullwinkle field, Green Canyon 65, offshore Gulf of Mexico

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1470-1484 ◽  
Author(s):  
Alastair M. Swanston ◽  
Peter B. Flemings ◽  
Joseph T. Comisky ◽  
Kevin D. Best
Keyword(s):  
Gulf Of Mexico ◽  
Time Lapse ◽  
Seismic Survey ◽  
Data Sets ◽  
Water Contact ◽  
Seismic Surveys ◽  
Fluid Properties ◽  
Induced Changes ◽  
Time Lapse Imaging ◽  
Difference Images

Two orthogonal preproduction seismic surveys and a regional seismic survey acquired after eight years of production from the Bullwinkle field (Green Canyon 65, Gulf of Mexico) reveal extraordinary seismic differences attributed to production‐induced changes in rock and fluid properties. Amplitude reduction (of up to 71%) occurs where production and log data show that water has replaced hydrocarbons as the oil–water contact moved upward. Separate normalizations of these surveys demonstrate that time‐lapse results are improved by using seismic surveys acquired in similar orientations; also, clearer difference images are obtained from comparing lower‐frequency data sets. Superior stratigraphic illumination in the dip‐oriented survey relative to the strike‐oriented surveys results in nongeological amplitude differences. This documents the danger of using dissimilar baseline and monitor surveys for time‐lapse studies.

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