Application of All Reflectors Auto-Tracking Method to Characterize the Geometry and Distribution of Carbonate Shoals: An Example from the Lower Cambrian Longwangmiao Formation in the Moxi area, Sichuan Basin, Southwestern China
- Xiaoer Chen (Institute of Sedimentary Geology, Chengdu University of Technology, Geophysical Technology Research Centre, BGP, CNPC) | Kun Fan (Southwest Geophysical Research Institute, BGP, CNPC) | Chenghao Ren (Southwest Geophysical Company, BGP, CNPC) | Le Li (Geophysical Technology Research Centre, BGP, CNPC) | Zhenqian Yan (Sichuan Institute of Coral Field Geological Engineering Exploration and Designing) | Guoliang Zou (Exploration Department of Changqing Oilfield LTD, CNPC) | Zhonglin Cao (Geophysical Technology Research Centre, BGP, CNPC) | Yao Zhao (Geophysical Technology Research Centre, BGP, CNPC)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.1 Reservoir Characterisation, 5.1.5 Geologic Modeling, 5.8.2 Shale Gas, 5 Reservoir Desciption & Dynamics
- carbonate shoals, shingled progradational reflection, Longwangmiao Formation, all reflectors auto-tracking method
- 4 in the last 30 days
- 71 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
The Cambrian Longwangmiao Formation in the Sichuan Basin, southwest China, mainly comprising of dolomites, is one of the most ancient production layer in the world. Recently, Anyue gas field was discovered in the Leshan-longnvsi paleo-uplift in the central Sichuan Basin, and become the oldest gas field in the carbonate rocks in a single structural system in China. The reservoir is mainly distributed in the shoal grain dolomite, which is always controlled by the sedimentary environment. The conventional well correlation and sedimentary facies analysis might result in difficulty of carbonate shoals distribution and reservoir description in the gas field. Hence, how to characterize the geometry and distribution of carbonate shoals is critical for gas exploration and development. In our study, we completed an interpretation of 1172km2 3D seismic data in the field by means of all reflectors auto-tracking method. The method, combining density-based spatial clustering with waveform similarity clustering algorithm, can automatically track and interpret all reflectors within the 3D seismic cube. As a result, 18 local horizons, characterized by a shingled progradational configuration, were recognized within the Longwangmiao Formation. Synthetic seismograms suggest that these parallel oblique progradational sets were considered as carbonate shoals. The Longwangmiao Formation is consisted of stacked multistaged carbonate grainstones deposited on the shoals within the platform. These shoals, which grow towards northwest, are approximately distributed surrounding the Leshan-Longnvsi paleo-uplift. Stacked and widely distributed shoal grainstone reservoir is formed on the uplift. Our study suggests that the paleo-uplift mainly controls the shoal distribution in the study area, which provides important clues for gas exploration.
|File Size||1 MB||Number of Pages||12|
Borgos, H. G., T. Skov, T. Ramden, and L. Sonneland, 2003, Automated geometry extraction from 3D seismic data: SEG Technical Program Expanded Abstracts 2003: 1541–1544, doi: 10.1190/1.1817590.
Chen, M., L. Zhang, R. Feng, and F. Wang, 2017a, Double-scale flood fill approach for larger-scale 3D horizon auto-tracking: Oil Geophysical Prospecting, 52(5): 1033–1041, doi: 10.13810/j.cnki.issn.1000-7210.2017.05.017.
Chen, X., J. Wang, K. Fan, Y. Zhao, Z. Li, X. Zhao and X. Tang, 2016b, The Sand Reservoir Prediction of the 2nd member of the Xujiahe Formation in PL area, Central Sichuan Basin: Computing Techniques for Geophysical and geochemical Exploration, 38(6): 751–757, doi:10.3969/j.issn.1001-1749.2016.06.07.
David L. Davies; Donald W. Bouldin, 1979, A Cluster Separation Measure: IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224 – 227, doi: 10.1109/TPAMI.1979.4766909.
Figueiredo M. and A.K. Jain, 2002, Unsupervised learning of finite mixture models. IEEE Transactions On Pattern Analysis and Machine Intelligence, 2002,24(3):381–396, doi: 10.1109/34.990138.
Hoyes, J., and T. Cheret, 2011, "A review of "global" interpretation methods for automated 3D horizon picking.":The Leading Edge, 30(1), 38–47, doi: 10.1190/1.3535431.
Harrigan, E., J. R. Kroh, W. A. Sandham, T.S. Durrani, 1992, Seismic horizon picking using an artificial neural network: ICASSP-92., 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.3: 105–108, doi: 10.1109/ICASSP.1992.226265.
Kurban, H., C. Kockan, M. Jenne and M. Dalkilic, 2017, Improving expectation maximization algorithm over stellar data: 2017 IEEE International Conference on Big Data (Big Data), 79–83, doi: 10.1109/BigData.2017.8258215.
Pauget, F., S. Lacaze, and T. Valding, 2009, A global approach in seismic interpretation based on cost function minimization: SEG Technical Program Expanded Abstracts 2009, 2592–2596, doi: 10.1190/1.3255384.
Pestana, R., P. L. Stoffa, and W. G. Adriano, 2012. The relation between finite differences in time and the Chebyshev polynomial recursion: SEG Technical Program Expanded Abstracts 2012, 1–5, doi: 10.1190/segam2012-0833.1
Qian, F., C. Luo, Z. Su, X. Yao, G. Hu and X. Chen, 2014, A two-step mechanism for automated 3D horizon picking: SEG Technical Program Expanded Abstracts 2014, 1548–1553, doi: 10.1190/segam2014-0984.1.
Stark, T. J., 2004, Relative geologic time (age) volumes-Relating every seismic sample to a geologically reasonable horizon: The Leading Edge, 23(9), 928–932, doi:10.1190/1.1803505.
Wu, X., and D. Hale, 2015, Horizon volumes with interpreted constraints: Geophyisics, 80(2), 21–33, doi:10.1190/GEO2014-0212.1.
Wang Zecheng, Wang Tongshan,Wen Long, Jiang Hua, Zhang Baoming, 2016, Basic geological characteristics and accumulation conditions of Anyue giant gas field,Sichuan basin. China Offshore Oil and Gas, 28(2): 45–52, doi:10.11935/j.issn.1673-1506.2016.02.005.
Wei Guoqi, Yang Wei, Du Jinhu, Xu Chunchun, Zou Caineng, Xie Wuren, Wu Saijun, Zeng Fuying. Tectonic features of Gaoshiti-Moxi paleo-uplift and its controls on the formation of a giant gas field, Sichuan Basin, SW China. Petroleum Exploration and Development, 42(3):257–265, doi: 10.11698/PED.2015.03.01.
Yin, W., G. Guo, Y. Li, J. Zhu and C. Li, 2017, Wheeler conversion method based on layer-controlled horizon automatic tracking with ant-colony algorithm and its application: Journal of China University of Petroleum (Edition of Natural Sciences), 41(1):51–59, doi:10.3969/j.issn.1673-5005.2017.01.006.
Zhong, G., Y. Li, F. Wu, Y. Xiong, and Z. Tao, 2010, Identification of subtle seismic sequence boundaries by all-reflector tracking method: SEG Technical Program Expanded Abstracts 2010, 1545–1549, doi: 10.1190/1.3513135.
Zhang, L., Z. Hu, and L. Fu, 2016, New coherence algorithm based on higher order statistics: Progress in Geophysics (in Chinese), 31(4) : 1762–1766. doi: 10.6038/pg20160445.