Seismic-Q imaging for petroleum reservoir characterization

加拿大的重油生产涉及到巨大的经济影响、世界能源需求和将环境影响降至最低的相互矛盾的考虑。通过利用地质、地球物理和油藏生产数据，改进的石油油藏特征应该有利于所有这些目标。提高采收率(EOR)涉及一系列独特的挑战，包括对储集层岩性和稠油粘度的描述。在这项研究计划中，申请者将使用地震数据来表征地质和井间流体粘度。三次采油前地震资料的三维逆时深度偏移(RTM)将提供高分辨率的油藏地质图像。砂岩和页岩的地震划分将极大地改善井的布置和提高采收率过程的调度，如注汽。井间流体粘度的动态估计将优化三次采油过程本身。该项目的主要目标之一是通过地震-Q(反衰减)分析来估计井间粘度。这可以通过计算传输的地震波到达的Q层析图像来实现。然后，岩石物理模型和实验室测量可以将Q断层图转换为粘度断层图。虽然确定地震Q值具有挑战性，但人们认为，从时间推移的角度估计地震Q值应能提供有关油藏原油粘度在时间和空间上变化的有价值的信息。好的Q估计还可以通过在波动方程中包含Q的RTM方法来改善地震图像。常规地震分析对地质特征的分辨和储层流体粘度的地震估计都将大大有助于改善稠油油田的储集层特征。据我们所知，以前从未有过如此完整和创新地使用地球物理数据来增强油藏动态建模的情况。鉴于科学专业知识、先进软件的可用性和行业数据的可获得性，世界上几乎没有大学项目可以提供本提案中概述的地球物理储层表征方面的HQP培训。

The Canadian production of heavy oil involves the competing considerations of huge economic impact, world energy demands, and minimization of environmental impact. Enhanced petroleum reservoir characterization should benefit all of these objectives by utilizing geological, geophysical and reservoir production data. Enhanced oil recovery (EOR) involves a unique set of challenges that include the descriptions of the reservoir lithology and the heavy oil viscosity. In this research program, the applicant will use seismic data to characterize the geology and the fluid viscosity between boreholes. The 3-D reverse-time depth migration (RTM) of seismic data obtained prior to EOR will provide a high-resolution image of the reservoir geology. The seismic delineation of sand versus shale will greatly improve the placement of wells and the scheduling of EOR processes such as steam injection. The dynamic estimation of reservoir fluid viscosity between boreholes will optimize the EOR processes themselves. One of the project's main goals is to estimate interwell viscosity by the analysis of seismic-Q (inverse attenuation). This can be achieved by computing Q tomograms from transmitted seismic arrivals. Rock physics models and lab measurements can then convert Q tomograms to viscosity tomograms. While determining seismic-Q is challenging, it is believed that its estimation in a time-lapse sense should provide valuable information about changing oil viscosity in the reservoir in both time and space. Good estimates of Q can also improve the seismic images through use of RTM with Q included in the wave equation. Both the resolution of geological features with conventional seismic analysis and the seismic estimation of reservoir fluid viscosity should greatly aid in improving the reservoir characterization of heavy oil fields. To the best of our knowledge, there has never previously been such a complete and innovative use of geophysical data to enhance the dynamic modeling of oil reservoirs. Given the scientific expertise, availability of state-of-the-art software and access to industry data, there are few university programs anywhere that can offer the HQP training in geophysical reservoir characterization as outlined in this proposal.