Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rose correlations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.

中文翻译：

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Bahariya 地层渗透率估计的交替条件期望 (ACE) 算法

总结 非参数变换是一种数据驱动技术，可用于估计因变量（响应）和一组独立参数（预测变量）之间的最佳相关性。本研究介绍了一种使用非参数变换概念和交替条件期望 (ACE) 算法的系统方法，以使用常规测井和岩心数据估算有效气体渗透率。ACE 算法用于当前工作，使用 MATLAB®（The MathWorks, Inc., Natick, Massachusetts, USA）代码和开源图形 ACE (GRACE) 软件（Xue et al. 1997）来推导最优非参数预测渗透率的相关性。该方法应用于埃及的非均质地层 [Bahariya (BAH)]，以了解其特征并更准确地预测其渗透率。BAH 组被认为是整个埃及西部沙漠 (WD) 石油生产的主要来源之一。BAH 地层的累计石油产量估计约为 WD 总产量的 40%。BAH 组储层特征从高渗透性到夹有页岩和粉砂岩的致密砂岩。它通常描述低电阻率和低对比度 (LRLC) 对数行为。因此，区域和准确地确定横跨 WD 的 BAH 组不同岩石单元的储层渗透率是一个挑战。来自大约 100 口取心井和相应的 5 口井的常规测井数据，500 次岩心测量用于提供可用于大量储层的区域渗透率相关性。这项工作的方法包括两个主要步骤： 应用非参数变换技术来识别集体测井响应以得出最佳相关性 使用选定的测井响应预测渗透率剖面 该模型应用于许多处理 BAH 地层不同岩石物理特征的井. 建立的渗透率剖面显示了与实测岩心数据相关的可靠相关系数。训练数据点（收集数据库的 75%）的相关系数为 0.893，测试数据点（收集数据库的 25%）为 0.913。此外，训练和测试数据点的预测渗透率和实测渗透率之间的平均绝对百分比误差 (MAPE) 分别为 5.93% 和 4.14%。将使用 ACE 的渗透率预测与其他技术进行比较，例如 k-φ 交叉图、多元线性回归 (MLR)、Coates 和 Wyllie-Rose 相关性。这项工作被认为是对当前区域渗透率预测相关性的原始贡献，使用传统的测井记录进行储层表征和模拟应用。ACE 算法成功应用于 BAH 组，并证明了其识别建立严格模型所需的最佳预测因子的能力。分别。将使用 ACE 的渗透率预测与其他技术进行比较，例如 k-φ 交叉图、多元线性回归 (MLR)、Coates 和 Wyllie-Rose 相关性。这项工作被认为是对当前区域渗透率预测相关性的原始贡献，使用传统的测井记录进行储层表征和模拟应用。ACE 算法成功应用于 BAH 组，并证明了其识别建立严格模型所需的最佳预测因子的能力。分别。将使用 ACE 的渗透率预测与其他技术进行比较，例如 k-φ 交叉图、多元线性回归 (MLR)、Coates 和 Wyllie-Rose 相关性。这项工作被认为是对当前区域渗透率预测相关性的原始贡献，使用传统的测井记录进行储层表征和模拟应用。ACE 算法成功应用于 BAH 组，并证明了其识别建立严格模型所需的最佳预测因子的能力。这项工作被认为是对当前区域渗透率预测相关性的原始贡献，使用传统的测井记录进行储层表征和模拟应用。ACE 算法成功应用于 BAH 组，并证明了其识别建立严格模型所需的最佳预测因子的能力。这项工作被认为是对当前区域渗透率预测相关性的原始贡献，使用传统的测井记录进行储层表征和模拟应用。ACE 算法成功应用于 BAH 组，并证明了其识别建立严格模型所需的最佳预测因子的能力。