Quantitative characterization and monitoring of reservoir properties, pressures, fluids and fractures with multicomponent and quasi-continuous full-waveform seismology

利用多分量和准连续全波形地震学对储层性质、压力、流体和裂缝进行定量表征和监测

• 批准号: 543578-2019
• 负责人: Innanen, Kristopher
• 金额: $ 32.13万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699652
• 关键词: Quantitative; characterization; monitoring; reservoir; properties

Full waveform (FWI) seismic methods have achieved spectacular industrial and academic successes in image-forming of complex offshore reservoirs, but as practical, regular-use tools for monitoring of onshore conventional and unconventional production, CO2 and wastewater injection, and EOR methods, basic and applied scientific progress is still required. Fortunately, in aid of making such progress, geophysicists now have at hand powerful new geo-computational tools, in the form of HPC and artificial intelligence technology, and powerful new instrumentation and seismic acquisition tools, in the form of permanent controllable sources, broadband geophones and distributed acoustic sensing (DAS, or fibre-optic) seismic sensors, and drillstring acoustic technology. We propose to create the next generation of practical, FWI reservoir characterization and monitoring tools, involving the determination of high resolution maps of rock physics properties - pressures, fluids, fractures and viscosities - through analysis of the elasticity, viscosity, and anisotropy of the complex modern reservoir environment. Our group has carried out significant, though initial, research in broadband and fibre-optic field and laboratory acquisition, practical multi-parameter elastic, viscoelastic and anisotropic FWI method development, rock-physics seismic inversion, near surface characterization, drillstring imaging, machine learning, and HPC methods for large computation / large data problems. We propose to grow and expand these early successes, creating a practical reservoir waveform package. FWI involving multicomponent and DAS data, elastic FWI-rock physics sensitivity analysis, surface wave analysis and processing, machine learning as a framework for seismic inversion, and new blended acquisition and deblending methodologies, are key expected outcomes of the research. These efforts will bring about both knowledge and technology creation in high-resolution geological mapping. This benefits Canada through technical HQP training, and research with outcomes (high resolution surveillance capabilities) that directly affect resource extraction efficiency, geohazard mitigation, and which contribute to reduction of land, water, and energy use.

全波形（FWI）地震方法在复杂海上油气藏成像方面取得了巨大的工业和学术成就，但作为监测陆上常规和非常规生产、CO2和废水注入以及EOR方法的实用常规工具，仍然需要基础和应用科学进步。幸运的是，为了帮助取得这样的进展，地震学家现在手头有强大的新的地理计算工具，以HPC和人工智能技术的形式，以及强大的新仪器和地震采集工具，以永久可控源，宽带地震检波器和分布式声学传感（DAS或光纤）地震传感器和钻柱声学技术的形式。我们建议创建下一代实用的FWI储层表征和监测工具，包括通过分析复杂的弹性、粘度和各向异性来确定岩石物理性质（压力、流体、裂缝和粘度）的高分辨率地图现代储层环境。 我们的团队已经在宽带和光纤领域以及实验室采集、实用多参数弹性、粘弹性和各向异性FWI方法开发、岩石物理地震反演、近地表表征、钻柱成像、机器学习和用于大计算/大数据问题的HPC方法方面进行了重要的（尽管是初步的）研究。我们建议发展和扩大这些早期的成功，创建一个实用的储层波形包。 FWI涉及多组分和DAS数据，弹性FWI岩石物理敏感性分析，表面波分析和处理，机器学习作为地震反演的框架，以及新的混合采集和去混合方法，是研究的主要预期成果。 这些努力将带来高分辨率地质测绘方面的知识和技术创造。这使加拿大受益于技术HQP培训和研究成果（高分辨率监测能力），直接影响资源开采效率，减轻地质灾害，并有助于减少土地，水和能源的使用。