Integrating discrete distribution of lithofacies into the petrophysical property modeling is essential to preserve reservoir heterogeneity and improve flow modeling. Specifically, various studies have been implemented to model the permeability as a function of well logging data without taking into account the effect of lithofacies, which is rational to produce distinct regression lines given each facies type. In this paper, advanced statistical learning approaches were adopted as an integrated workflow to model the core permeability given well logging records and discrete lithofacies classification for a well in the South Rumaila oil field, located in Iraq. In particular, the probabilistic neural networks was first applied for modeling and predicting the discrete lithofacies classification given the well logging records: neutron porosity, shale volume, and water saturation. Next, smooth generalized additive model (SGAM) was used to model the core permeability as a function of the same well logging records. In addition, the predicted lithofacies was included as a discrete independent variable in the core permeability modeling to provide different regression lines given each lithotype. The SGAM was also modeled for three subset data given each separate lithofacies to verify the efficiency of SGAM and to provide more accurate prediction of permeability. The same procedure of SGAM was completely repeated by the generalized linear model (GLM) to prove the higher effectiveness of SGAM for permeability modeling and prediction. The root mean square prediction error in SGAM was lower than in GLM in all the combined and separate lithofacies models. In addition, the SGAM model overcame the multicollinearity between shale volume and water saturation variables by using the smoothed terms. Finally, making accurate permeability prediction for all wells in the field should ensure capturing the spatial variation and correlation between the data and then preserving the reservoir heterogeneity.

中文翻译：

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将岩相和测井数据整合到平滑的广义加性模型中以改善渗透率估算：南鲁迈拉油田祖拜尔地层

将岩相的离散分布整合到岩石物理特性模型中对于保持储层非均质性和改善流动模型至关重要。具体而言，已经进行了各种研究来将渗透率建模为测井数据的函数，而不考虑岩相的影响，这对于给出每种相类型都可以产生不同的回归线是合理的。在本文中，采用先进的统计学习方法作为集成工作流程，以给定位于伊拉克南鲁迈拉油田的一口井的测井记录和离散岩相分类来模拟岩心渗透率。尤其是，根据给定的测井记录：中子孔隙度，页岩体积和水饱和度。接下来，使用平滑广义加性模型（SGAM）对岩心渗透率进行建模，以作为相同测井记录的函数。另外，在岩心渗透率模型中将预测岩相作为离散的独立变量包括在内，以根据每种岩性提供不同的回归线。在给定每个岩相的情况下，还针对三个子集数据对SGAM进行了建模，以验证SGAM的效率并提供更准确的渗透率预测。广义线性模型（GLM）完全重复了SGAM的相同过程，以证明SGAM在渗透性建模和预测中具有更高的有效性。在所有组合和单独的岩相模型中，SGAM中的均方根预测误差均低于GLM。此外，SGAM模型通过使用平滑项克服了页岩体积和含水饱和度变量之间的多重共线性。最后，对现场的所有井进行准确的渗透率预测应确保捕获数据之间的空间变化和相关性，然后保留储层的非均质性。