Abstract Water saturation is imperative in the evaluation of hydrocarbon reserves available. However, it is challenging to accurately determine the water saturation of complex reservoirs using conventional techniques. This is due to the fact that the conventional models are unable to fully account for the heterogeneity of the reservoir and their results are highly influenced by factors such as type of data, approach and shale distribution. Moreover, most computational intelligence methods developed to estimate water saturation have neglected the relationship that can exist between input variables and their impact on model performance. This is because well log parameters can exhibit relationships among each other which leads to the presence of multiple collinearities and increases the complexity of the model. Therefore, this paper for the first time adopted principal component analysis (PCA) as a dimensionality reduction method to improve the performance of optimized least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system-subtractive clustering method (ANFIS-SCM). The experimental results clearly depicted a superior performance from PCA based LSSVM (PCA-LSSVM) during training and testing. Also, PCA minimized the overfitting experienced by ANFIS-SCM by improving the model's generalization ability. On the whole, PCA-LSSVM provided the least prediction error and outperformed PCA based ANFIS-SCM (PCA-ANFIS-SCM) when estimating water saturation.

中文翻译：

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基于主成分分析 (PCA) 的混合模型用于准确估计水库水饱和度

摘要 水饱和度是评价可用油气储量的必要条件。然而，使用传统技术准确确定复杂储层的含水饱和度具有挑战性。这是因为常规模型无法充分考虑储层的非均质性，其结果受数据类型、方法和页岩分布等因素的影响很大。此外，大多数用于估计水饱和度的计算智能方法都忽略了输入变量之间可能存在的关系及其对模型性能的影响。这是因为测井参数可以表现出彼此之间的关系，从而导致存在多个共线性并增加模型的复杂性。所以，本文首次采用主成分分析（PCA）作为降维方法来提高优化最小二乘支持向量机（LSSVM）和自适应神经模糊推理系统减法聚类方法（ANFIS-SCM）的性能。实验结果清楚地描绘了在训练和测试期间基于 PCA 的 LSSVM (PCA-LSSVM) 的优越性能。此外，PCA 通过提高模型的泛化能力，最大限度地减少了 ANFIS-SCM 所经历的过拟合。总体而言，PCA-LSSVM 在估计水饱和度时提供了最小的预测误差，并且优于基于 PCA 的 ANFIS-SCM (PCA-ANFIS-SCM)。