Double-wavelet double-difference time-lapse waveform inversion

2020 ◽  
Author(s):  
X. Fu ◽  
S. Romahn ◽  
K. A. Innanen
Keyword(s):  
Waveform Inversion ◽  
Time Lapse ◽  
Double Difference ◽  
Difference Time

Time-lapse velocity imaging via deep learning

2019 ◽  
Author(s):  
Congcong Yuan ◽  
Xiong Zhang ◽  
Xiaofeng Jia ◽  
Jie Zhang
Keyword(s):  
Iterative Methods ◽  
Network Architecture ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Double Difference ◽  
Target Velocity ◽  
Waveform Data ◽  
Processing Cost ◽  
Velocity Changes

Summary It is of great significance and a great challenge to quickly and effectively monitor subsurface time-lapse velocities in the earth. Over the past few decades, regularized iterative methods, such as traveltime and waveform inversions, have been presented to monitor velocity changes. Due to high processing cost, these iterative methods have been hardly employed in practice to investigate the subsurface velocity changes in real time. In this study, we propose a new time-lapse imaging technique that effectively eliminates these limitations and directly produces accurate velocity changes from the time-lapse data. The approach uses a fully convolutional neural network (FCN) to perform the inverse problem. The network architecture consists of a contracting path to quickly extract the features of waveform data and a symmetric expanding path to yield an accurate velocity model. With the known baseline velocity and data, we cast a mapping between time-lapse data and target velocity changes via the proposed FCN algorithm. Along with the observed time-lapse data, this mapping will generate a predictive estimation of the target velocity changes. We demonstrate the efficiency and accuracy of our approach in three 2D synthetic tests. The proposed technique is able to invert the velocity changes successfully with much higher efficiency than the regular double-difference full waveform inversion.

Source-independent time-lapse full-waveform inversion for VTI media

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Yanhua Liu ◽  
Ilya Tsvankin
Keyword(s):  
Waveform Inversion ◽  
Transversely Isotropic ◽  
Time Lapse ◽  
Model Complexity ◽  
Double Difference ◽  
Full Waveform Inversion ◽  
Reservoir Properties ◽  
Hydrocarbon Production ◽  
Full Waveform ◽  
Adjoint State

Time-lapse full-waveform inversion can provide high-resolution information about changes in the reservoir properties during hydrocarbon production and CO2 injection. However, the accuracy of the estimated source wavelet, which is critically important for time-lapse FWI, is often insufficient for field-data applications. The so-called “source-independent” FWI is designed to reduce the influence of the source wavelet on the inversion results. We incorporate the convolution-based source-independent technique into a time-lapse FWI algorithm for VTI (transversely isotropic with a vertical symmetry axis) media. The gradient of the modified FWI objective function is obtained from the adjoint-state method. The algorithm is tested on a model with a graben structure and the modified VTI Marmousi model using three time-lapse strategies (the parallel-difference, sequential-difference, and double-difference methods). The results confirm the ability of the developed methodology to reconstruct the localized time-lapse parameter variations even for a strongly distorted source wavelet. The algorithm remains robust in the presence of moderate noise in the input data but the accuracy of the estimated time-lapse changes depends on the model complexity.

scholarly journals Time-lapse full-waveform inversion with ocean-bottom-cable data: Application on Valhall field

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. R225-R235 ◽  
Author(s):  
Di Yang ◽  
Faqi Liu ◽  
Scott Morton ◽  
Alison Malcolm ◽  
Michael Fehler
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Double Difference ◽  
Baseline Survey ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Full Waveform ◽  
Future Production

Knowledge of changes in reservoir properties resulting from extracting hydrocarbons or injecting fluid is critical to future production planning. Full-waveform inversion (FWI) of time-lapse seismic data provides a quantitative approach to characterize the changes by taking the difference of the inverted baseline and monitor models. The baseline and monitor data sets can be inverted either independently or jointly. Time-lapse seismic data collected by ocean-bottom cables (OBCs) in the Valhall field in the North Sea are suitable for such time-lapse FWI practice because the acquisitions are of a long offset, and the surveys are well-repeated. We have applied independent and joint FWI schemes to two time-lapse Valhall OBC data sets, which were acquired 28 months apart. The joint FWI scheme is double-difference waveform inversion (DDWI), which inverts differenced data (the monitor survey subtracted by the baseline survey) for model changes. We have found that DDWI gave a cleaner and more easily interpreted image of the reservoir changes compared with that obtained with the independent FWI schemes. A synthetic example is used to demonstrate the advantage of DDWI in mitigating spurious estimates of property changes and to provide cross validations for the Valhall data results.

Non-repeatability Effects on Time-Lapse 4D Seismic Full Waveform Inversion for Ocean-Bottom Node Data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Wei Zhou ◽  
David Lumley
Keyword(s):  
Waveform Inversion ◽  
Time Lapse ◽  
Seismic Survey ◽  
Double Difference ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Central Difference ◽  
Change Models ◽  
Full Waveform ◽  
4D Seismic

Full waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. However, non-repeatability (NR) issues can contaminate the data and cause artifacts in the estimation of 4D rock and fluid property changes. Therefore, evaluating and studying the NR effects on the 4D data and FWI results can help, for instance, discriminate inversion artifacts from true changes, guide seismic survey design and processing workflow. Using realistic reservoir models, data and field measurements of NR, we show the effects of NR source-receiver position and seawater velocity changes on the data and the 4D FWI results. We find that ignoring these NR effects in the inversion can cause strong artifacts in the estimated velocity change models, and thus should be addressed before or during inversion. The NR source-receiver positioning issue can be addressed by 4D FWI successfully, whereas the NR water velocity issue requires measurements or estimations of water velocities. Furthermore, we compare the accuracy and robustness of the parallel, double-difference and central-difference 4D FWI methods to realistic NR ocean-bottom node data in a quantitative way. Parallel 4D FWI fails to capture geomechanical changes and also overestimates the aquifer layer changes with NR data. Double-difference 4D FWI is capable of recovering the geomechanical changes, but is also sensitive to NR noises, generating more artifacts in the overburden. By averaging the forward and reverse bootstrap 4D estimates, central-difference 4D FWI is more robust to NR noises, and also produces the most accurate 4D estimates.

Double-difference elastic-waveform inversion with prior information for time-lapse monitoring

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. R259-R273 ◽  
Author(s):  
Zhigang Zhang ◽  
Lianjie Huang
Keyword(s):  
Oil Recovery ◽  
Prior Information ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Double Difference ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Reservoir Properties ◽  
Seismic Reflection Data ◽  
Reflection Data

Quantitative time-lapse seismic monitoring can provide crucial information for enhanced oil recovery, geologic carbon storage, and enhanced geothermal systems. Recently developed double-difference elastic-waveform inversion has the potential to quantitatively monitor reservoirs using seismic reflection data. Because the approximate location of a reservoir or a target monitoring region is usually known, we incorporated this knowledge as prior information into double-difference elastic-waveform inversion. Using numerical examples of synthetic time-lapse models, we found that our new method can quantitatively monitor the changes of elastic properties within reservoirs. Therefore, the double-difference elastic-waveform inversion with prior information on the location of a monitoring region is a promising tool for quantitatively monitoring reservoir properties’ changes.

Time-lapse full waveform inversion based on curvelet transform: Case study of CO2 storage monitoring

2021 ◽  
Vol 110 ◽  
pp. 103417
Author(s):  
Dong Li ◽  
Suping Peng ◽  
Xingguo Huang ◽  
Yinling Guo ◽  
Yongxu Lu ◽  
...  
Keyword(s):  
Co2 Storage ◽  
Waveform Inversion ◽  
Curvelet Transform ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform

Target-oriented inversion using the Patched Green′s function method

Geophysics ◽  
2021 ◽  
pp. 1-77
Author(s):  
Danyelle da Silva ◽  
Edwin Fagua Duarte ◽  
Wagner Almeida ◽  
Mauro Ferreira ◽  
Francisco Alirio Moura ◽  
...  
Keyword(s):  
Condensed Matter ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Dyson Equation ◽  
Computational Time ◽  
Green Functions ◽  
Target Area ◽  
Computational Domain ◽  
Full Waveform

We have designed a target-oriented methodology to perform Full Waveform Inversion using a frequency-domain wave propagator based on the so-called Patched Green’s Function (PGF) technique. Originally developed in condensed matter physics to describe electronic waves in materials, the PGF technique is easily adaptable to the case of wave propagation in a spatially variable media in general. By dividing the entire computational domain into two sections, namely the target area and the outside target area, we calculate the Green Functions related to each section separately. The calculations related to the section outside the target are performed only once at the beginning of inversion, whereas the calculations in the target area are performed repeatedly for each iteration of the inversion process. With the Green Functions of the separate areas, we calculate the Green Functions of the two systems patched together through the application of a Recursive Dyson equation. By performing 2D and time-lapse experiments on the Marmousi model and a Brazilian Pre-salt velocity model, we demonstrate that the target-oriented PGF reduces the computational time of the inversion without compromising accuracy. In fact, when compared with conventional FWI results, the PGF-based calculations are identical but done in a fraction of the time.

Multiscale time-domain time-lapse full-waveform inversion with a model-difference regularization

2017 ◽  
Author(s):  
Musa Maharramov ◽  
Ganglin Chen ◽  
Partha S. Routh ◽  
Anatoly I. Baumstein ◽  
Sunwoong Lee ◽  
...  
Keyword(s):  
Time Domain ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform
Sign in / Sign up

Export Citation Format

Share Document