Li, Kun (China University of Petroleum (East China)) | Zhu, Ming (Shenzhen Branch of CNOOC Ltd) | Du, Jiayuan (Shenzhen Branch of CNOOC Ltd) | Liu, Daoli (Shenzhen Branch of CNOOC Ltd) | Yin, Xingyao (China University of Petroleum (East China)) | Zong, Zhaoyun (China University of Petroleum (East China))
Lithology prediction and geofluid discrimination are the ultimate objectives of rock physical analysis and prestack seismic inversion. For prestack Bayesian estimation and geostatistical simulation, the prior probability density distribution of model parameters are usually influenced by subsurface lithologies and geofluid facies, which consist of several Gaussian probability components with different means and covariances. With the assumption of Gaussian mixture a priori, one improved prestack EVA inversion (elastic impedance variation with angle) conditioned by seismic and well data in mixed-domain is proposed to realize the estimation of discrete lithofacies and continuous geofluid parameters. The peaks number of prior Gaussian probability density is the same as classifications of sedimentary lithologies. For the resolution of seismic inversion, sequential simulation algorithm is utilized to sample the posterior probability distributions. Besides, the low frequency regularization and nonlinear bounding constraint strategy are introduced into the proposed method, which can enhance the stability of prestack EVA inversion and overcome the unrealistic solutions of elastic parameters. Finally, model tests and the applications on field prestack seismic data can verify the effectiveness and practicability in geofluid discrimination of the proposed algorithm.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral