Application of a novel geometric seismic attribute for enhancing fault visualization in areas of potential carbon capture and storage

Diana K. Salazar Florez, Heather Bedle
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Abstract

Seismic fault interpretation is a critical task for any type of energy industry. Correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes, such as coherence and curvature, are routinely employed to enhance fault visualization in seismic data. However, they can show limitations for subseismic faulting. In this study, we highlight the usefulness of including novel aberrancy attributes for fault identification in multiattribute analysis and unsupervised machine learning (ML) techniques. We compare broadband coherence, curvature, multispectral coherence, and aberrancy when trying to map faults in a potential CO2 storage location. We also compare self-organizing maps and generative topographic mapping techniques when including and excluding aberrancy attributes. Our results show that integrating aberrancy attributes during multiattribute analysis and ML steps considerably enhanced the visualization of lineaments with strikes similar to those of fracture sets seen only with well-log data and that were not clearly captured by the conventional seismic attributes and ML scenarios excluding aberrancy attributes. We demonstrate the potential of these novel geometric seismic attributes to map subseismic faults. We also provide an example that can encourage interpreters to include them in their interpretation workflows.
应用新型几何地震属性增强潜在碳捕获和储存区域的断层可视化
对于任何类型的能源行业来说,地震断层解释都是一项至关重要的任务。正确绘制断层图对于项目的成功至关重要。常见的地震几何属性,如相干性和曲率,通常用于增强地震数据中断层的可视化。然而,它们在地震下断层方面可能会显示出局限性。在本研究中,我们强调了在多属性分析和无监督机器学习(ML)技术中加入新的畸变属性对断层识别的有用性。在尝试绘制潜在二氧化碳封存地点的断层图时,我们比较了宽带相干性、曲率、多光谱相干性和岩性。我们还比较了自组织地图和生成地形图绘制技术在包含和不包含岩性属性时的效果。我们的研究结果表明,在多属性分析和多重层析成像步骤中整合岩性属性,可大大提高线形的可视化程度,这些线形的走向与仅通过井录数据看到的断裂集类似,而传统的地震属性和多重层析成像方案(不包括岩性属性)并不能清晰地捕捉到这些线形。我们展示了这些新型几何地震属性在绘制地震下断层图方面的潜力。我们还提供了一个例子,鼓励解释人员将其纳入解释工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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