The determination of porosity and permeability distribution along the reservoir is very important and can be determined by different field and laboratory experiments, i.e., core flooding experiments, seismic and well log data, well testing. At the field level, however, information regarding spatial distribution of porosity and permeability is very sparse, and additional techniques such as geostatistics and correlations may be used. The literature presents a variety of correlations between permeability and porosity, considering different parameters, such as probability distribution functions and tortuosity of porous media. General behavior of porous media, however, can be described with a normal distribution for porosities and log-normal distribution for permeabilities. This paper proposes the use of a simplified empirical equation to represent the correlation between porosity and permeability. Methodologies to derive the empirical parameters from experimental data, or desired ranges of porosities and permeability are proposed and applied. Considerations regarding range of validity of this correlation are made by the use of a steady-state single-phase reservoir simulation. Results show that the procedures for the creation of maps of permeability, obtained from the empirical correlation, provide a reasonable distribution of values and represent well the observed data from the laboratory. The procedure for the creation of synthetic fields, obtained by fixing values of maximum and minimum permeabilities, also shows good results and can be a faster way to create synthetic field cases. Regarding the application of these correlations for upscaling, results show that the correlation remains valid when the scale is increased. Numerical dispersion can, however, be observed. The errors obtained, however, increase significantly when permeability ranges increase, meaning that the correlation can only be used with confidence when no significant variations in porosity and permeability are present. Although the literature shows other methodologies for estimating upscaling values of permeability, the approach proposed here is easier and faster to be implemented and may be used in a complementary way for field-level upscaling.

确定沿储层的孔隙度和渗透率分布非常重要，可以通过不同的现场和实验室实验来确定，即岩心驱油实验、地震和测井数据、试井。然而，在现场层面，关于孔隙度和渗透率的空间分布的信息非常稀少，可能会使用其他技术，如地质统计学和相关性。考虑到不同的参数，例如多孔介质的概率分布函数和曲折度，文献提出了渗透率和孔隙度之间的各种相关性。然而，多孔介质的一般行为可以用孔隙率的正态分布和渗透率的对数正态分布来描述。本文建议使用简化的经验方程来表示孔隙度和渗透率之间的相关性。提出并应用了从实验数据或所需的孔隙率和渗透率范围推导出经验参数的方法。通过使用稳态单相油藏模拟来考虑这种相关性的有效性范围。结果表明，从经验相关性中获得的渗透率图的创建程序提供了合理的值分布并很好地代表了实验室的观测数据。通过固定最大和最小渗透率值获得的合成油田创建过程也显示出良好的结果，并且可以是创建合成油田案例的更快方法。关于这些相关性在放大中的应用，结果表明，当规模增加时，相关性仍然有效。然而，可以观察到数值分散。然而，当渗透率范围增加时，获得的误差会显着增加，这意味着只有当孔隙度和渗透率不存在显着变化时，才能有信心地使用相关性。尽管文献显示了用于估计渗透率升级值的其他方法，但这里提出的方法更容易、更快地实施，并且可以以补充方式用于现场级升级。当渗透率范围增加时显着增加，这意味着只有在孔隙度和渗透率不存在显着变化时才能可靠地使用相关性。尽管文献显示了估计渗透率升级值的其他方法，但这里提出的方法更容易、更快速地实施，并且可以以补充方式用于现场级升级。当渗透率范围增加时显着增加，这意味着只有在孔隙度和渗透率不存在显着变化时才能可靠地使用相关性。尽管文献显示了用于估计渗透率升级值的其他方法，但这里提出的方法更容易、更快地实施，并且可以以补充方式用于现场级升级。