Generalized Gaussian distribution-based pre-stack inversion: A case study of channel sands in the Lianggaoshan Formation, Sichuan Basin

Abstract

Tight sandstone channel systems occur in the second member of the Lianggaoshan Formation in the Sichuan Basin. These channels predominantly exhibit single bright-spot reflections on seismic sections. Due to the limited resolution of post-stack data and the constraints of attribute dimensionality, drilling results reveal complex internal lithological assemblages and pronounced lateral heterogeneity within these channels. Traditional post-stack methods struggle to effectively characterize internal channel details, thereby hindering subsequent exploration planning and reserve evaluation. To address these challenges, this study develops a pre-stack seismic inversion workflow based on generalized Gaussian distribution (GGD) prior constraints, aiming to enhance the identification and prediction of complex channel sand bodies. First, pre-stack amplitude versus offset (AVO) response analysis was conducted using typical wells to determine the effective incident angle range. By integrating optimal angle stacked data and AVO attribute extraction, the accuracy of characterizing the lateral distribution of channels was improved. Subsequently, a low-frequency facies-controlled model was constructed by integrating AVO attributes with elastic parameters. GGD prior constraints were incorporated into the pre-stack elastic parameter inversion, enabling detailed prediction of internal channel architecture. This technique yielded promising results in the Fuxing Block and was successfully validated in the Bazhong Block. It effectively enhanced the accuracy of identifying and predicting complex channels, providing technical support for the exploration, deployment, and resource evaluation of Jurassic tight sandstone channels in the Sichuan Basin.

Keywords

Channel sandstone / Lianggaoshan Formation / Amplitude versus offset attribute / Generalized Gaussian distribution / Low-frequency facies-controlled model

Acknowledgments

None.

Funding

None.

Conflict of interest

The authors declare no potential conflict of interest.

Author contributions

Conceptualization: Meng Yuan

Formal analysis: Chang Sun

Investigation: Jianlong Su

Methodology: Zhiwei Miao

Visualization: Lei Pan

Writing-original draft: Meng Yuan

Writing-review & editing: Meng Yuan

Availability of data

All data analyzed have been presented in the paper.

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