Quantitative characterization and monitoring of reservoir properties, pressures, fluids and fractures with multicomponent and quasi-continuous full-waveform seismology
Quantitative characterization and monitoring of reservoir properties, pressures, fluids and fractures with multicomponent and quasi-continuous full-waveform seismology
批准号:
543578-2019
负责人:
Innanen, KristopherKAH
金额:
$7.14万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
海外基金