Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Li, Yuanyuan; Guo, Qiang; Alkhalifah, Tariq Ali; Kazei, Vladimir

Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Files

segam2020-3420515.1.pdf (561.37 KB)

Type

Conference Paper

Authors

Li, Yuanyuan

Guo, Qiang
Alkhalifah, Tariq Ali

Kazei, Vladimir

KAUST Department

Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group

Online Publication Date

2020-09-30

Print Publication Date

2020-09-30

Date

2020-09-30

Abstract

Seismic monitoring of the changes in the subsurface induced by various types of injections into reservoirs is important, yet challenging. Time-lapse waveform inversion can retrieve quantitative estimates of subsurface property changes. Considering that property changes usually occur in a limited region rather than the whole subsurface, we present a target-oriented time-lapse waveform inversion method, which allows for dynamic monitoring of the target of interest. We employ a redatuming technique to produce virtual data at a desired datum level for the target-oriented inversion. Given the redatumed time-lapse data, the property changes can be quantitatively estimated from the data difference for the virtual survey using a double-difference waveform inversion (DDWI). In the numerical examples, the dependence of the inversion performance on the quality of overburden model and its robustness to nonrepeatable acquisition survey and random noise is investigated. The numerical results demonstrate that the inversion method is capable of recovering the time-lapse changes reasonably well under some challenging circumstances. We will show field data examples at the conference.

Citation

Li, Y., Guo, Q., Alkhalifah, T., & Kazei, V. (2020). Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3420515.1

Acknowledgements

We would like to thank the Shaheen supercomputing Laboratory in KAUST for their computational support. We thank KAUST for its support and SWAG for collaborative environment.