Interpretation:1-40 (forthcoming)
Abstract |
The Lower Silurian shale-gas formation in the south of Sichuan Basin represents strong VTI feature. Successful characterization of shale-gas formation requires handling the great influence of anisotropy in the seismic wave propagation. Seismic AVO inversion for VTI media using PP-waves only is a difficult issue because more than three parameters need to be estimated and such an inverse problem is highly ill-posed. We have applied an AVO inversion method for VTI media based on a modified approximation of the PP-wave reflection coefficient. This approximation consists of only three model parameters: the acoustic impedance, shear modulus proportional to the anellipticity parameter, and the approximated horizontal P-wave velocity, which can be well-inverted and have great interpretation capability in shale-gas reservoir characterization. A statistical-rock-physics method was then applied to the inverted attributes for quantitative interpretation of shale-gas reservoir. Markov random field is combined with Bayesian rule to improve the continuity and accuracy of the interpretation results. Shales can be successfully discriminated from surrounding formations by using the attribute pair A-C, and the organic-rich gas-bearing shale can be successfully identified by using the attribute pair C-B. Comparison between the prediction results and well logs demonstrates the feasibility of the inversion and quantitative interpretation approaches.
|
Keywords | No keywords specified (fix it) |
Categories |
No categories specified (categorize this paper) |
DOI | 10.1190/int-2019-0050.1 |
Options |
![]() ![]() ![]() ![]() |
Download options
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Quantitative Interpretation — A Case Study: Part 1.Satinder Chopra, Ritesh Kumar Sharma, Hossein Nemati & James Keay - 2018 - Interpretation 6 (2):T313-T324.
Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Quantitative Interpretation — A Case Study: Part 1.Satinder Chopra, Ritesh Kumar Sharma, Hossein Nemati & James Keay - 2018 - Interpretation: SEG 6 (2):T313-T324.
Seismic Reservoir Characterization of Duvernay Shale with Quantitative Interpretation and Induced Seismicity Considerations — A Case Study.Satinder Chopra, Ritesh Kumar Sharma, Amit Kumar Ray, Hossein Nemati, Ray Morin, Brian Schulte & David D’Amico - 2017 - Interpretation: SEG 5 (2):T185-T197.
Integrated Shale-Gas Reservoir Characterization: A Case Study Incorporating Multicomponent Seismic Data.Laurie M. Weston Bellman - 2018 - Interpretation: SEG 6 (2):SE23-SE37.
Quantitative Prediction of Total Organic Carbon Content in Shale-Gas Reservoirs Using Seismic Data: A Case Study From the Lower Silurian Longmaxi Formation in the Chang Ning Gas Field of the Sichuan Basin, China.Sheng Chen, Wenzhi Zhao, Qingcai Zeng, Qing Yang, Pei He, Shaohua Gai & Yu Deng - 2018 - Interpretation: SEG 6 (4):SN153-SN168.
Quantitative Prediction of TOC Content in Shale Gas Reservoirs Using Seismic Data: A Case Study From the Lower Silurian Longmaxi Formation in the Chang Ning Gas Field of the Sichuan Basin, China.Sheng Chen, Wenzhi Zhao, Qingcai Zeng, Qing Yang, Pei He, Shaohua Gai & Yu Deng - forthcoming - Interpretation:1-40.
Deepwater Reservoir Prediction Using Broadband Seismic-Driven Impedance Inversion and Seismic Sedimentology in the South China Sea.Yaneng Luo, Handong Huang, Yadi Yang, Qixin Li, Sheng Zhang & Jinwei Zhang - 2018 - Interpretation: SEG 6 (4):SO17-SO29.
Interpretation of Fractures and Joint Inversion Using Multicomponent Seismic Data — Marcellus Shale Example.Shukun Yuan, Michael V. DeAngelo & Bob A. Hardage - 2014 - Interpretation: SEG 2 (2):SE55-SE62.
Quantitative Identification of Microfractures in the Marine Shale Reservoir of the Wufeng-Longmaxi Formation Using Water Immersion Tests and Image Characterization.Hu Wang, Zhiliang He, Yonggui Zhang, Kun Su & Ruyue Wang - 2018 - Interpretation: SEG 6 (4):SN23-SN30.
Quantitative Characterization and Characteristic Analysis of Pore Structure of Shale-Gas Reservoir in the Sichuan Basin, China.Huaimin Dong, Jianmeng Sun, Jinjiang Zhu, Zhenzhou Lin, Likai Cui, Weichao Yan & Zhu Xiong - 2019 - Interpretation 7 (4):SJ23-SJ32.
Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Fracability Evaluation- a Case Study: Part 2.Ritesh Kumar Sharma, Satinder Chopra, James Keay, Hossein Nemati & Larry Lines - forthcoming - Interpretation:1-36.
Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Fracability Evaluation — Part 2: A Case Study.Ritesh Kumar Sharma, Satinder Chopra, James Keay, Hossein Nemati & Larry Lines - 2018 - Interpretation: SEG 6 (2):T325-T336.
Reservoir-Oriented Wave-Equation Based Seismic AVO Inversion.A. Gisolf, P. R. Haffinger & P. Doulgeris - forthcoming - Interpretation: SEG:1-60.
Seismic Multiattribute Analysis for Shale Gas/Oil Within the Austin Chalk and Eagle Ford Shale in a Submarine Volcanic Terrain, Maverick Basin, South Texas.Osareni C. Ogiesoba & Ray Eastwood - 2013 - Interpretation: SEG 1 (2):SB61-SB83.
3D Structural-Orientation Vector Guided Autotracking for Weak Seismic Reflections: A New Tool for Shale Reservoir Visualization and Interpretation.Haibin Di, Dengliang Gao & Ghassan AlRegib - 2018 - Interpretation: SEG 6 (4):SN47-SN56.
Analytics
Added to PP index
2019-10-04
Total views
3 ( #1,278,944 of 2,403,166 )
Recent downloads (6 months)
1 ( #552,147 of 2,403,166 )
2019-10-04
Total views
3 ( #1,278,944 of 2,403,166 )
Recent downloads (6 months)
1 ( #552,147 of 2,403,166 )
How can I increase my downloads?
Downloads