Machine learning modeling of permeability in 3D heterogeneous porous media using a novel stochastic pore-scale simulation approach
Machine learning modeling of permeability in 3D heterogeneous porous media using a novel stochastic pore-scale simulation approach
复制标题
基于随机孔隙尺度模拟方法的三维非均质多孔介质渗透率机器学习建模
DOI：
10.1016/j.fuel.2022.124044
复制
发表时间：
2022
期刊：
影响因子：
7.4
通讯作者：
O. Ishola;J. Vilcáez
中科院分区：
文献类型：
--
作者：
O. Ishola;J. Vilcáez
岩石渗透率的准确预测对于资源勘探和环境管理至关重要。为了改进现有的渗透率预测方法，本研究采用了随机孔隙尺度模拟方法。这种方法的实施所需的岩石物理性质是岩石样品的孔隙度和孔径分布（PSD），可以很容易地从压汞毛细管压力测量获得。四个碳酸盐岩和五个硅质岩岩心的方法进行了测试。为了考虑可以与相同PSD和孔隙度相关联的各种可能的孔隙连通性情况，所采用的随机孔隙尺度模拟方法涉及生成具有相同PSD和孔隙度但不同随机孔隙连通性的数百个3D孔隙微结构。渗透率是通过对通过生成的3D孔隙微结构进行孔隙尺度流动模拟获得的渗透率分布求平均值来计算的。渗透率的估计更接近实测渗透率与这种方法比五个确定性的经验模型方程。使用机器学习将所需的孔隙规模模拟次数减少了157倍，并重现了从孔隙规模流动模拟中估计的渗透率，平均绝对百分比误差为10%。
Accurate predictions of rock permeability is critical for resource exploration and environmental management. To improve on existing approaches to permeability prediction, this study employed a stochastic pore-scale simulation approach. The petrophysical properties needed for the implementation of this approach are porosity and pore size distribution (PSD) of rock samples which can be obtained easily from mercury injection capillary pressure measurements. The approach was tested on four carbonate and five siliciclastic rock cores. To consider a wide range of possible pore connectivity scenarios that can be associated to the same PSD and porosity, the employed stochastic pore-scale simulation approach involves the generation of hundreds of 3D pore microstructures of the same PSD and porosity but different stochastic pore connectivity. Permeability is calculated by averaging the permeability distribution obtained from pore-scale flow simulations through the generated 3D pore microstructures. Permeability estimations were closer to measured permeability with this approach than with five deterministic empirical model equations. Machine learning was used to reduce the required number of pore-scale simulations by 157 times and reproduced permeability estimated from pore-scale flow simulations with a mean absolute percentage error of 10%.