Mohammadi Milad, Emami Niri Mohammad, Bahroudi Abbas, Soleymanzadeh Aboozar, Kord Shahin
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sci Rep. 2024 Aug 6;14(1):18264. doi: 10.1038/s41598-024-68167-3.
Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.
岩石分类技术一直依赖于电学或水力性质。本研究引入了一种用于储层岩石分类的新方法——水电指数（HEI），该方法结合了传统电学和水力岩石分类方法的优势，以更准确地表征碳酸盐岩储层。通过将渗透率与地层电阻率因数之比（K/FRF）相对于孔隙度进行归一化，HEI方法应用于两个碳酸盐岩岩心样本数据集：数据集1由来自怀俄明州和蒙大拿州比格霍恩盆地坦斯利普组的112个碳酸盐岩岩心样本组成，数据集2包括来自伊朗西南部阿斯马里组的81个碳酸盐岩岩心样本。统计分析证实了HEI在预测渗透率方面的有效性，两个数据集的决定系数都很高（数据集1和数据集2的决定系数（R）分别为0.965和0.904）。结果将岩石样本分为不同的岩石类型，数据集1为九种岩石类型，数据集2为四种岩石类型，并证明了HEI能够捕捉碳酸盐岩储层岩石中水力传导率与电阻率之间的关系。将HEI方法应用于验证数据集产生了高度准确的渗透率预测，数据集1和数据集2的决定系数平均值分别为0.883和0.859。HEI方法的验证进一步证实了（将20%的数据集留作验证，其余80%用于岩石分类方法（5折交叉验证））其在预测渗透率方面的准确性，突出了其在估计碳酸盐岩储层渗透率方面强大的预测能力。
