The Slope-Attribute-Regularized High-Resolution Prestack Seismic Inversion

Huang, Guangtan; Chen, Xiaohong; Li, Jingye; Saad, Omar M.; Fomel, Sergey; Luo, Cong; Wang, Hang; Chen, Yangkang

doi:10.1007/s10712-021-09636-6

Cited by 15 publications

(2 citation statements)

References 79 publications

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“…Due to its excellent ability to solve nonlinear problems, it has become a popular topic in seismic exploration. It has been widely developed to predict seismic attributes and formation structures [38][39][40][41]. Objectively, reservoir heterogeneity leads to multiple solutions and uncertainty in solving petroleum geological problems, and it is difficult to obtain "textbook" tag data for machine learning.…”

Section: Discussionmentioning

confidence: 99%

“…Yang [18,19] proposed the nonlinear chaotic inversion for seismic traces by the phasing and quantitative description of state changes of successive linearization iterations. Combined with nonlinear optimization [20] and a random model, Huang et al [21] proposed nonlinear random inversion controlled by the seismic phase, which had high vertical resolution and relatively accurate prediction results with the advantages of both broadband constrained inversion [22] and model inversion [23]. The method was also used to track biological reefs and shoals by establishing a recognition model with the seismic facies and seismic profiles [24].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Marine Thin Shale Gas Reservoir with Seismic Phase-Controlled Nonlinear Stochastic Inversion

Xie

Huang

et al. 2023

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Due to the complicated interference sources and low signal-to-noise ratio of seismic records, conventional seismic inversion methods are difficult to accurately identify shale gas reservoirs with a thickness of less than 10 m. This presents great challenges to shale gas exploration and development in China. Seismic phase-controlled nonlinear stochastic inversion (SPCNSI) is related to the heterogeneity of underground media. With the constraints of the stratigraphic sequence or seismic facies models, the minimum value between the seismic model and seismic record can be solved through iterative processes. Based on the solved acoustic velocity in formation, the constraints for SPCNSI can be formed with the matching relationship between target layers and different sequences in three-dimensional space. The prediction resolution of an unconventional reservoir can be effectively improved by combining logging, geological and seismic information. The method is suitable for predicting thin shale gas reservoirs in complex geological structures. In this study, SPCNSI was developed to predict the thin-layer marine shale gas reservoir in the southeast of Chongqing; the horizontal and vertical distribution characteristics were proven in the Longmaxi Formation and the Wufeng Formation; thin layers with a thickness of less than 7 m were also discovered. According to the results, three sets of potential development areas were determined by the inversion method, and the accuracy and reliability of the method were verified by logging and productivity testing.

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Section: Discussionmentioning

confidence: 99%