Synchronous prestack inversion for automatic extracting the correlation of elastic parameters using block sparse Bayesian learning
- Ming Ma (University of Louisiana at Lafayette, School of Geosciences) | Rui Zhang (University of Louisiana at Lafayette, School of Geosciences) | Haoyang Gao (China University of Petroleum (Beijing))
- Document ID
- Society of Exploration Geophysicists
- SEG International Exposition and Annual Meeting, 15-20 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Exploration Geophysicists
- AVO/AVA, Machine learning, Inversion
- 0 in the last 30 days
- 5 since 2007
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As the basic manner of a conjunction between physical property of subsurface layers and received seismic signal, prestack inversion is widely deployed in the quantitative description of reservoir and the multi-scale analysis with various data types. The precision of inverted result always affects the trap evaluation and hydrocarbon identification directly. To extensively mine and apply information of lithology and reflection interface hidden in the prestack seismic cube, we reconstruct the inversion formula and propose an advanced technique to achieve a precise restoration of concerned elastic parameters. With the automatic estimation and utilization of the potential correlation among the P-, S-wave velocity, and density, new inversion approach can derive three reflectivities, namely, Rp, Rs, and Rr initially. As an algorithm belonged to the machine learning, the introduced block sparse Bayesian learning can invoke the covariance matrix that represents the relationship of three elastic parameters to guarantee the successful capture of model parameters, especially the density. Finally, the velocities and density models are calculated via the optimal multi-trace algorithm L-BGFS to guarantee the spatial continuity. Numerical and field data test illustrate the superb performance of the new approach.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:30 PM
Location: Poster Station 8
Presentation Type: Poster
|File Size||1 MB||Number of Pages||5|
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