Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin.

Tian, Fei; Zheng, Wenhao; Zhao, Aosai; Liu, Jingyue; Liu, Yunchen; Zhou, Hui; Cao, Wenjing

Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings.

CARBONATE reservoirs; HYDROCARBON reservoirs; PRINCIPAL components analysis; SIGNAL-to-noise ratio; K-means clustering

Applied Sciences (2076-3417), 2024, Vol 14, Issue 18, p8235

2076-3417

Academic Journal

10.3390/app14188235