地质学
曲率
油页岩
断层(地质)
岩石学
卡尔曼滤波器
地震属性
构造盆地
石油工程
地震学
计算机科学
古生物学
人工智能
几何学
数学
作者
Gang Chen,Hongyan Qi,Yong Song,Wei Li,Chenggang Xian,Yuzhang Liu,Tingming Tang,Minghui Lu,Zhenlin Wang,Yang Zhao
出处
期刊:Geophysics
[Society of Exploration Geophysicists]
日期:2022-12-07
卷期号:88 (2): B91-B99
被引量:6
标识
DOI:10.1190/geo2022-0345.1
摘要
The large-scale shale-oil production in the Jimsar sag of the Junggar Basin is being aggressively developed. To improve unconventional production and make precise suggestions for horizontal well developments, fault interpretations using seismic data play an important role. The target reservoir in the Jimsar sag of the Junggar Basin has a comprehensive network of faults, according to the drilled wells and fault identification from the broadband azimuth and high-density 3D seismic data. Several reasons such as the integration between source and reservoir, the substantial abundance of organic matter, and the major reservoir plasticity result in different fault types, which then create different impacts on horizontal wells. We have developed a fault-type identification tool using the curvature attributes via the 3D seismic Kalman filter to provide a more accurate description of fault types. In contrast with previous studies, we derive the Kalman-curvature formulation in terms of space and time, by using a dynamically iterative optimization model. Our strategy reveals that various fault types (open faults, semiopen faults, and sealed faults) represent their characteristics associated with the 3D Kalman-curvature attributes. The resultant fault divisions are valid by combining the drilled information of lost circulation and fracturing crosstalk of the horizontal wells. High-quality field results validate the robustness of our algorithms.
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