基于多孔岩石数字图像的多相流模拟软件包LBPM
The LBPM software package for simulating multiphase flow on digital images of porous rocks
摘要:
在数字岩石图像上直接模拟孔隙尺度的双流体流动,为理解地质油藏中表面润湿现象对流动和输运的作用提供了一种有前途的工具。我们提出的计算协议,模仿传统的特殊岩芯分析实验室(SCAL)的实验,这是在开源LBPM软件包中实现的。协议被描述为模拟非定常位移,在固定饱和度的稳态流,并模仿离心实验。这些方法可用于推断相对渗透率和毛细管曲线,以及基于第一原理理解双流体流动行为。应用形态学工具评估图像分辨率,建立初始条件,并根据流体的分布实例化表面润湿图。描述了测量双流体流动的基本方面的内部分析工具,包括流体连通性和表面测量,这些工具用于跟踪模拟过程中发生的流动行为的瞬态方面。通过将这些组件与双流体格子Boltzmann模型相结合来开发计算效率高的工作流程,以定义混合方法,该混合方法可以通过使用形态学工具来加速计算,从而逐步发展孔隙尺度流体分布。我们表明,所描述的方法可以应用于恢复预期的趋势,由于表面润湿性能的基础上流动模拟本海默砂岩。
英文摘要:
Direct pore scale simulations of two-fluid flow on digital rock images provide a promising tool to understand the role of surface wetting phenomena on flow and transport in geologic reservoirs. We present computational protocols that mimic conventional special core analysis laboratory (SCAL) experiments, which are implemented within the open source LBPM software package. Protocols are described to simulate unsteady displacement, steady-state flow at fixed saturation, and to mimic centrifuge experiments. These methods can be used to infer relative permeability and capillary curves, and otherwise understand two-fluid flow behavior based on first principles. Morphological tools are applied to assess image resolution, establish initial conditions, and instantiate surface wetting maps based on the distribution of fluids. Internal analysis tools are described that measure essential aspects of two-fluid flow, including fluid connectivity and surface measures, which are used to track transient aspects of the flow behavior as they occur during simulation. Computationally efficient workflows are developed by combining these components with a two-fluid lattice Boltzmann model to define hybrid methods that can accelerate computations by using morphological tools to incrementally evolve the pore-scale fluid distribution. We show that the described methods can be applied to recover expected trends due to the surface wetting properties based on flow simulation in Benntheimer sandstone.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。