Integrated kinematic time-lapse inversion workflow leveraging full-waveform inversion and machine learning

We demonstrate that a workflow combining emergent time-lapse full-waveform inversion (FWI) and machine learning technologies can address the demand for faster time-lapse processing and analysis. During the first stage of our proposed workflow, we invert long-wavelength velocity changes using a tomographically enhanced version of multiparameter simultaneous reflection FWI with model-difference regularization. Short-wavelength changes are inverted during the second stage of the workflow by a specialized high-resolution image-difference tomography algorithm using a neural network. We discuss application areas for each component of the workflow and show the results of a West Africa case study.

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

International Journal of Greenhouse Gas Control ◽

10.1016/j.ijggc.2021.103417 ◽

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

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4D Time-Lapse Full Waveform Inversion Case Study for SAGD Steam Chamber Imaging

10.3997/2214-4609.202113199 ◽

2021 ◽

Author(s):

H. Feng ◽

T. Kay ◽

A. Knudsen ◽

W. Wang ◽

A. Ayre

Keyword(s):

Waveform Inversion ◽

Time Lapse ◽

Full Waveform Inversion ◽

Steam Chamber ◽

Full Waveform

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Time-lapse inverse theory with applications

Geophysics ◽

10.1190/geo2016-0131.1 ◽

2016 ◽

Vol 81 (6) ◽

pp. R485-R501 ◽

Cited By ~ 19

Author(s):

Musa Maharramov ◽

Biondo L. Biondi ◽

Mark A. Meadows

Keyword(s):

Waveform Inversion ◽

Significant Risk ◽

Time Lapse ◽

Long Wavelength ◽

Full Waveform ◽

Total Variation Model ◽

Velocity Anomalies ◽

Velocity Changes ◽

Primary Focus ◽

Production Effects

Compaction in the reservoir overburden can impact production facilities and lead to a significant risk of well-bore failures. Prevalent practices of time-lapse seismic processing of 4D data above compacting reservoirs rely on picking time displacements and converting them into estimated velocity changes and subsurface deformation. This approach relies on prior data equalization and requires a significant amount of manual interpretation and quality control. We have developed methods for automatic detection of production-induced subsurface velocity changes from seismic data. We have evaluated a time-lapse inversion technique based on a simultaneous regularized full-waveform inversion (FWI) of multiple surveys. In our approach, baseline and monitor surveys are inverted simultaneously with a model-difference regularization penalizing nonphysical differences in the inverted models that are due to survey or computational repeatability issues. The primary focus of our work was the inversion of long-wavelength “blocky” changes in the subsurface model, and this was achieved using a phase-only FWI with a total-variation model-difference regularization. However, we have developed a multiscale extension of our method for recovering long- and short-wavelength production effects. We have developed a theoretical foundation of our method and analyzed its sensitivity to a realistic 1%–2% velocity deformation. The method was applied in a study of overburden dilation above the Gulf of Mexico Genesis field and recovered blocky negative-velocity anomalies above compacting reservoirs.

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Time-lapse full-waveform inversion as a reservoir-monitoring tool — A North Sea case study

The Leading Edge ◽

10.1190/tle35100850.1 ◽

2016 ◽

Vol 35 (10) ◽

pp. 850-858 ◽

Cited By ~ 11

Author(s):

Erik Hicks ◽

Henning Hoeber ◽

Marianne Houbiers ◽

Séverine Pannetier Lescoffit ◽

Andrew Ratcliffe ◽

...

Keyword(s):

North Sea ◽

Waveform Inversion ◽

Time Lapse ◽

Full Waveform Inversion ◽

Monitoring Tool ◽

Full Waveform ◽

Reservoir Monitoring

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Full waveform inversion of repeating seismic events to estimate time-lapse velocity changes

Geophysical Journal International ◽

10.1093/gji/ggx057 ◽

2017 ◽

Cited By ~ 6

Author(s):

R. Kamei ◽

D. Lumley

Keyword(s):

Waveform Inversion ◽

Time Lapse ◽

Seismic Events ◽

Full Waveform Inversion ◽

Full Waveform ◽

Velocity Changes

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Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation

Geophysical Journal International ◽

10.1093/gji/ggaa337 ◽

2020 ◽

Vol 223 (2) ◽

pp. 811-824

Author(s):

Chao Huang ◽

Tieyuan Zhu

Keyword(s):

Kalman Filter ◽

Real Time ◽

Waveform Inversion ◽

Time Lapse ◽

Uncertainty Estimation ◽

Covariance Matrices ◽

Full Waveform Inversion ◽

Full Waveform ◽

Velocity Changes ◽

Over Time

SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.

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