Application of comprehensive fault detection technology combining deep learning with edge enhancement in detecting ultra-deep strike-slip faults in Shunbei block
CHEN Jun'an1, CHEN Haidong2, GONG Wei1, LIAO Maohui1
1. Northwest Branch, Sinopec, Urumchi, Xinjiang 830011, China; 2. School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China
Abstract：Karst fracture-cave reservoirs are well-deve-loped in the Shunbei block of the Tarim Basin，and high-productivity wells have emerged in recent years. A large number of studies have confirmed that the development of strike-slip faults with high angles plays a decisive role in the migration and accumulation of oil and gas reservoirs. Due to the deep burial of fault-controlled reservoir small fault throws，and hard closure，the SNR of seismic data in the Shunbei block is low，and the characteristic of fault planes is not clear，which make the detection and spatial interpretation of strike-slip faults difficult. Given the difficulties faced by studies on ultra-deep strike-slip fault detection，this paper proposes a comprehensive detection technology combining deep learning with edge enhancement for multi-scale faults. Specifically，the paper divides the strike-slip faults into main faults，associated secondary faults，and small-scale fractures by scale and carries out targeted studies. According to the seismic response characteristics of different fracture modes including forward main faults，associated secondary faults，and small-scale fractures and method experimental tests，it is believed that U-Net convolutional neural network deep learning technology can be used to identify main faults，and amplitude gradient vector disordered detection technology can be applied to identify associated secondary faults. In addition，the Aberrance enhancement attribute can be adopted to identify small-scale fractures. The proposed technology has been applied to detect strike-slip faults in the Shunbei block and achieved remarkable effects.
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