#xD;AUTOMATIC 3D FAULT DETECTION AND CHARACTERIZATION – A COMPARISON BETWEEN SEISMIC ATTRIBUTE METHODS AND DEEP LEARNING#xD;

IF 1.1 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
L. S. B. Oliveira, B. Alaei, A. Torabi, K. M. L. Oliveira, D. L. Vasconcelos, F. Bezerra, F. Nogueira
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引用次数: 0

Abstract

Seismic interpretation is crucial for identifying faults, fluid concentrations, and flow migration pathways in the oil and gas industry. Algorithms have been developed to identify faults using seismic data and attributes such as changes in amplitude, phase, polarity, and frequency. Despite technological advancements, challenges remain in seismic interpretation due to noise, quality of data, and fault dimensions. Deep learning has recently been applied to image faults from seismic data, making the process faster and more reliable. This paper evaluates the performance of Deep Neural Networks (DNN) in fault interpretation by comparing the results with traditional seismic attributes in onshore seismic data. Our results indicate that the DNN reveals more structural detail, which is essential in characterizing the 3D fault geometry. In addition, DNN results show better continuity, fewer false positives, and are less affected by noise in the onshore seismic data used in this case. The 3D fault model from DNN identifies faults and their fault segments with greater variability of strikes and reveals more minor faults. Based on the DNN fault model, we characterized the 3D geometry of a new fault in the Rio do Peixe Basin without noise influence.
#xD;三维故障自动检测与表征地震属性方法与深度学习的比较;
地震解释对于识别石油和天然气行业中的断层、流体浓度和流动迁移路径至关重要。已经开发了使用地震数据和属性(如振幅、相位、极性和频率的变化)来识别断层的算法。尽管技术进步,但由于噪声、数据质量和断层尺寸的原因,地震解释仍然面临挑战。深度学习最近被应用于地震数据中的断层图像,使这一过程更快、更可靠。本文通过将深度神经网络(DNN)的结果与陆上地震数据中的传统地震属性进行比较,来评估其在断层解释中的性能。我们的结果表明,DNN揭示了更多的结构细节,这对于表征三维断层几何形状至关重要。此外,在这种情况下使用的陆上地震数据中,DNN结果显示出更好的连续性、更少的假阳性,并且受噪声的影响更小。DNN的三维断层模型识别了走向变化较大的断层及其断层段,并揭示了更多的小断层。基于DNN断层模型,我们在没有噪声影响的情况下,对Rio do Peixe盆地一条新断层的三维几何形状进行了表征。
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来源期刊
CiteScore
2.50
自引率
8.30%
发文量
126
期刊介绍: ***Jointly published by the American Association of Petroleum Geologists (AAPG) and the Society of Exploration Geophysicists (SEG)*** Interpretation is a new, peer-reviewed journal for advancing the practice of subsurface interpretation.
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