A constrained spectral inversion method based on compressive sensing in order to distinguish high-quality shale

Hua Zhang; Zhenhua He; Yalin Li; Rui Li; Guangming He

doi:10.1071/EG17055

ISSN: 0812-3985
E-ISSN: 1834-7533

A constrained spectral inversion method based on compressive sensing in order to distinguish high-quality shale
Authors Hua Zhang^a,b,c,d,e, Zhenhua He^a,b, Yalin Li^c,d, Rui Li^a,b and Guangming He^c,d
View Affiliations Hide Affiliations

^a State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China. ^b College of Geophysics, Chengdu University of Technology, Chengdu, Sichuan 610059, China. ^c Geophysical Exploration Company, Chuanqing Drilling Engineering Co. Ltd, China National Petroleum Corporation, Chengdu, Sichuan 610213, China. ^d Mountain Geophysical Technology Test Center, China National Petroleum Corporation, Chengdu, Sichuan 610213, China. ^e Corresponding author. Email: [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: Exploration Geophysics, Volume 49, Issue 5, Oct 2018, p. 782 - 791
DOI: https://doi.org/10.1071/EG17055
- Received: 28 Feb 2017
- Accepted: 06 Oct 2017
- Published online: 01 Oct 2018

Abstract

[

Multi-wave exploration has become one of the main means of unconventional shale gas exploration in the Sichuan Basin, China. How to effectively improve the resolution of the shale gas reservoir layer and distinguish high-quality shale has become one of the difficulties for shale gas exploration. The spectral inversion technology can effectively break the resolution limit of the conventional technology and greatly improve the resolution of the thin shale layer; however, it also needs enough prior information to overcome the problem of multiple solutions in the inversion. The compressed sensing (CS) theory has the ability to reconstruct complete data using incomplete data can use less data information to improve the accuracy of spectral inversion. A novel constrained spectral inversion method based on CS is presented to handle these situations. The CS technique is applied to the objective function of spectral inversion, which can improve the accuracy of the spectral inversion algorithm and create profiles with a higher resolution and greater continuity. Applications through theoretical and real data can illustrate very high performance of the presented algorithm.

Distinguishing high-quality shale effectively is a difficult exploration problem. We propose a constrained spectral inversion method based on compressive sensing to handle this challenge. This method can help us improve the resolution and continuity of the profiles and enhance our ability to discover high-quality shale.

]

Article metrics loading...

/content/journals/10.1071/EG17055

2018-10-01

2026-01-18

From This Site

/content/journals/10.1071/EG17055

dcterms_title,dcterms_subject,pub_keyword

-contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

Castagna J. P. Sun S. J. 2006 Comparison of spectral decomposition methods:First Break247579
[Google Scholar]
Chai X. T. Li Z. C. Han W. G. Liu L. H. 2012 Spectral inversion method analysis based on the LSQR algorithm:Geophysical Prospecting for Petroleum511118 10.3969/j.issn.1000‑1441.2012.01.002
https://doi.org/10.3969/j.issn.1000-1441.2012.01.002 [Google Scholar]
Chen, K., Li, Z. C., and Han, W. G., 2010, Spectral inversion method analysis based on simulated annealing: College of Geo-resources and Information, China University of Petroleum (East China).
Chi H. Z. Liu C. Shan X. L. Lu Q. 2015 Application of spectral inversion for tight thin-bed sand body prediction:Geophysical Prospecting for Petroleum5433734410.3969/j.issn.1000‑1441.2015.03.013
https://doi.org/10.3969/j.issn.1000-1441.2015.03.013 [Google Scholar]
Donoho D. L. 2006 Compressive sensing:IEEE Transactions on Information Theory521289130610.1109/TIT.2006.871582
https://doi.org/10.1109/TIT.2006.871582 [Google Scholar]
Maravic I. Vetterli M. 2005 Sampling and reconstruction of signals with finite rate of innovation in the presence of noise:IEEE Transactions on Signal Processing532788280510.1109/TSP.2005.850321
https://doi.org/10.1109/TSP.2005.850321 [Google Scholar]
Oyem, A., and Castagna, J., 2013, Layer thickness estimation from the frequency spectrum of seismic reflection data: SEG Technical Program, Expanded Abstracts, 1451–1455.10.1190/segam2013‑0691.1
https://doi.org/10.1190/segam2013-0691.1
Puryear C. I. Castagna J. P. 2008 Layer-thickness determination and stratigraphic interpretation using spectral inversion: theory and application:Geophysics73R37R4810.1190/1.2838274
https://doi.org/10.1190/1.2838274 [Google Scholar]
Vetterli M. Marziliano P. Blu T. 2002 Sampling signals with finite rate of innovation:IEEE Transactions on Signal Processing501417142810.1109/TSP.2002.1003065
https://doi.org/10.1109/TSP.2002.1003065 [Google Scholar]
Zhang S. Q. Han L. G. Li C. Yan T Wang Y. X. Ma X. G. 2015 Computation method for reservoir fluid mobility based on high-resolution inversion spectral decomposition:Geophysical Prospecting for Petroleum5414214910.3969/j.issn.1000‑1441.2015.02.004
https://doi.org/10.3969/j.issn.1000-1441.2015.02.004 [Google Scholar]

/content/journals/10.1071/EG17055

A constrained spectral inversion method based on compressive sensing in order to distinguish high-quality shale

Exploration Geophysics 49, 782 (2018); https://doi.org/10.1071/EG17055

/content/journals/10.1071/EG17055

Data & Media loading...

Article Type: Research Article

Keyword(s): compressed sensing; inversion; resolution; shale gas

Most Cited This Month Most Cited RSS feed

- A comparison of smooth and blocky inversion methods in 2D electrical imaging surveys
  
  Authors M.H. Loke, Ian Acworth and Torleif Dahlin
- Reverse-Time Migration using the Poynting Vector
  
  Authors Kwangjin Yoon and Kurt J. Marfurt
- SkyTEM–a New High-resolution Helicopter Transient Electromagnetic System
  
  Authors K. I. Sorensen and Esben Auken
- An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data
  
  Authors Esben Auken, Anders Vest Christiansen, Casper Kirkegaard, Gianluca Fiandaca, Cyril Schamper, Ahmad Ali Behroozmand, Andrew Binley, Emil Nielsen, Flemming Effersø, Niels Bøie Christensen, Kurt Sørensen, Nikolaj Foged and Giulio Vignoli
- The Fast Marching Method: An Effective Tool for Tomographic Imaging and Tracking Multiple Phases in Complex Layered Media
  
  Authors Nicholas Rawlinson and Malcolm Sambridge
- 3-D Analytic Signal in the Interpretation of Total Magnetic Field Data at Low Magnetic Latitudes
  
  Authors Ian N. MacLeod, Keith Jones and Ting Fan Dai
- CMP cross-correlation analysis of multi-channel surface-wave data
  
  Authors Koichi Hayashi and Haruhiko Suzuki
- A new direct non-invasive groundwater detection technology for Australia
  
  Authors M. Schirov, A. Legchenko and G. Creer
- 3D inversion of airborne electromagnetic data using a moving footprint
  
  Authors Leif H. Cox, Glenn A. Wilson and Michael S. Zhdanov
- Notes On Rock Magnetization Characteristics In Applied Geophysical Studies
  
  Authors D. A. Clark and J. B. Emerson
More Less

A constrained spectral inversion method based on compressive sensing in order to distinguish high-quality shale

Abstract

Most Read This Month

Most Cited This Month Most Cited RSS feed