A constrained spectral inversion method based on compressive sensing in order to distinguish high-quality shale
Hua Zhang 1 2 3 4 5 Zhenhua He 1 2 Yalin Li 3 4 Rui Li 1 2 Guangming He 3 41 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China.
2 College of Geophysics, Chengdu University of Technology, Chengdu, Sichuan 610059, China.
3 Geophysical Exploration Company, Chuanqing Drilling Engineering Co. Ltd, China National Petroleum Corporation, Chengdu, Sichuan 610213, China.
4 Mountain Geophysical Technology Test Center, China National Petroleum Corporation, Chengdu, Sichuan 610213, China.
5 Corresponding author. Email: zhanghua_sc@cnpc.com.cn
Exploration Geophysics 49(5) 782-791 https://doi.org/10.1071/EG17055
Submitted: 28 February 2017 Accepted: 6 October 2017 Published: 17 November 2017
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.
Key words: compressed sensing, inversion, resolution, shale gas.
References
Castagna, J. P., and Sun, S. J., 2006, Comparison of spectral decomposition methods: First Break, 24, 75–79Chai, X. T., Li, Z. C., Han, W. G., and Liu, L. H., 2012, Spectral inversion method analysis based on the LSQR algorithm: Geophysical Prospecting for Petroleum, 51, 11–18
| Spectral inversion method analysis based on the LSQR algorithm:Crossref | GoogleScholarGoogle 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., and Lu, Q., 2015, Application of spectral inversion for tight thin-bed sand body prediction: Geophysical Prospecting for Petroleum, 54, 337–344
| Application of spectral inversion for tight thin-bed sand body prediction:Crossref | GoogleScholarGoogle Scholar |
Donoho, D. L., 2006, Compressive sensing: IEEE Transactions on Information Theory, 52, 1289–1306
| Compressive sensing:Crossref | GoogleScholarGoogle Scholar |
Maravic, I., and Vetterli, M., 2005, Sampling and reconstruction of signals with finite rate of innovation in the presence of noise: IEEE Transactions on Signal Processing, 53, 2788–2805
| Sampling and reconstruction of signals with finite rate of innovation in the presence of noise:Crossref | GoogleScholarGoogle 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.
Puryear, C. I., and Castagna, J. P., 2008, Layer-thickness determination and stratigraphic interpretation using spectral inversion: theory and application: Geophysics, 73, R37–R48
| Layer-thickness determination and stratigraphic interpretation using spectral inversion: theory and application:Crossref | GoogleScholarGoogle Scholar |
Vetterli, M., Marziliano, P., and Blu, T., 2002, Sampling signals with finite rate of innovation: IEEE Transactions on Signal Processing, 50, 1417–1428
| Sampling signals with finite rate of innovation:Crossref | GoogleScholarGoogle Scholar |
Zhang, S. Q., Han, L. G., Li, C., Yan, T, Wang, Y. X., and Ma, X. G., 2015, Computation method for reservoir fluid mobility based on high-resolution inversion spectral decomposition: Geophysical Prospecting for Petroleum, 54, 142–149
| Computation method for reservoir fluid mobility based on high-resolution inversion spectral decomposition:Crossref | GoogleScholarGoogle Scholar |