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计算地震构造解释与建模的实现讨论 被引量:10

Discussions on computational seismic structural interpretation and modeling
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摘要 三维地震构造解释与建模是油气勘探开发的关键步骤之一,随着三维地震数据体的规模不断增大,大量依赖于人工的传统方法在效率、精度和分辨率方面均难以满足生产需求;同时,随着计算机软硬件技术的发展,基于计算机辅助的自动化三维地震构造解释与建模是必然趋势,并且近10年来该领域取得了较大进展。介绍并讨论了一整套全自动三维地震构造解释与建模的计算机实现技术流程及其在多个实际数据中的成功应用案例。该流程主要包括:①三维地震断层检测、断层面构建、断距场估计和断层恢复等一系列断层解释功能的实现;②盐丘、火成岩和溶洞等各类地质体的识别与三维建模;③不整合面、层序界面检测与提取;④基于断层、地质体和不整合面等边界信息约束的层位体解释和Wheeler体构建;⑤融合所有构造和层位解释结果的构造建模和井震联合物性参数建模。对相关方法技术进行了综述,并将其与相应的实际地震数据应用情况相结合展开讨论,以呈现整个自动化地震构造解释与建模过程中所面临的计算机技术问题及其实现情况。其中,断层检测、地质体识别和层位提取等问题得到了较好的自动化实现,而断层面组合、构造恢复、精细层序解释和构造建模等方面依然高度依赖人工参与。深度学习方法对所有这些任务的自动化实现均具有较好的应用前景,但目前仍需要更好地解决训练样本缺乏的问题以及如何合理引入地质、物理先验信息约束等方面的问题。同时,由于缺乏对结果的合理评价、质控和使用的友好度,自动化方法可能会面临在实际场景应用中未被合理使用或获得不合理结果的风险。但是,在自动化智能化发展的大背景驱使下,计算构造解释与建模的发展前景令人期待。 3D seismic structural interpretation and modeling are key steps for oil and gas exploration.However,the traditional manual interpretation and modeling methods are not efficient or accurate enough to deal with the rapidly increasing 3D seismic datasets,which leaves significantly more data unutilized than utilized.On the contrary,with the rapid development of software and hardware technologies,numerous computer-assisted methods have been proposed in the past decade and have shown promising performances automating and accelerating seismic interpretation and modeling.We introduce a whole workflow of seismic structural interpretation and modeling,its computational implementation,and successful application to multiple field examples.This workflow involves the following five aspects:①detecting faults in 3D seismic images,constructing fault surfaces,estimating fault slips,and unfaulting and unfolding processes;②characterizing and recognizing kinds of geobodies including salt domes,igneous rock,channels,and paleokarst caves and 3D surface modeling of the geobodies;③detecting the positions of unconformities and sequence boundaries in 3D seismic images and constructing the corresponding surfaces;④volumetric horizon interpretation or seismic flattening(constructing a seismic Wheeler volume)using the interpreted faults,geobodies,and unconformities as boundary constraints;and⑤building rock-property models by integrating all the interpreted seismic structural and stratigraphic features and well-log measurements.Among these aspects,fault detection,geobody recognition,and horizon interpretation are better automated than the others,while fault surface construction,unfaulting and unfolding,fine stratigraphic interpretation and structural modeling still require significant human efforts.Deep learning methods show promise to further improve the automation of all the aspects of seismic structural interpretation and modeling.However,further improvement is needed to deal with missing labeled data,such as introducing prior geologic and geophysical constraints into the deep neural networks to improve their generalization in field applications.Meanwhile,the automatic methods may not be properly used in practice because their implementations may not be user-friendly to geologists and the automatically generated results are hard to quantitatively evaluate,and therefore represent a risk of full automatization without any human interactions.However,driven by the big trend of automation and artificial intelligence in many scientific fields,we can expect a significant development in computational seismic structural interpretation and modeling in the coming future.
作者 伍新明 杨佳润 朱振宇 丁继才 王清振 WU Xinming;YANG Jiarun;ZHU Zhenyu;DING Jicai;WANG Qingzhen(School of Earth and Space Sciences University of Science and Technology of China,Hefei 230026,China;CNOOC Research Institute Ltd.,Beijing 100010,China)
出处 《石油物探》 CSCD 北大核心 2022年第3期392-407,共16页 Geophysical Prospecting For Petroleum
基金 国家自然科学基金委面上项目(41974121)资助。
关键词 地震构造解释 层位 断层 地质体 不整合面 构造建模 图像处理 深度学习 seismic structural interpretation horizon fault geobody structural modeling image processing deep learning
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