Civil and Environmental Engineering Department, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
Sci Rep. 2023 Mar 9;13(1):3956. doi: 10.1038/s41598-023-30708-7.
Carbonate rocks present a complicated pore system owing to the existence of intra-particle and interparticle porosities. Therefore, characterization of carbonate rocks using petrophysical data is a challenging task. Conventional neutron, sonic, and neutron-density porosities are proven to be less accurate as compared to the NMR porosity. This study aims to predict the NMR porosity by implementing three different machine learning (ML) algorithms using conventional well logs including neutron-porosity, sonic, resistivity, gamma ray, and photoelectric factor. Data, comprising 3500 data points, was acquired from a vast carbonate petroleum reservoir in the Middle East. The input parameters were selected based on their relative importance with respect to output parameter. Three ML techniques such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and functional network (FN) were implemented for the development of prediction models. The model's accuracy was evaluated by correlation coefficient (R), root mean square error (RMSE), and average absolute percentage error (AAPE). The results demonstrated that all three prediction models are reliable and consistent exhibiting low errors and high 'R' values for both training and testing prediction when related to actual dataset. However, the performance of ANN model was better as compared to other two studied ML techniques based on minimum AAPE and RMSE errors (5.12 and 0.39) and highest R (0.95) for testing and validation outcome. The AAPE and RMSE for the testing and validation results were found to be 5.38 and 0.41 for ANFIS and 6.06 and 0.48 for FN model, respectively. The ANFIS and FN models exhibited 'R' 0.937 and 0.942, for testing and validation dataset, respectively. Based on testing and validation results, ANFIS and FN models have been ranked second and third after ANN. Further, optimized ANN and FN models were used to extract explicit correlations to compute the NMR porosity. Hence, this study reveals the successful applications of ML techniques for the accurate prediction of NMR porosity.
碳酸盐岩由于存在颗粒内和颗粒间孔隙，因此呈现出复杂的孔隙系统。因此，使用岩石物理数据对碳酸盐岩进行特征描述是一项具有挑战性的任务。与 NMR 孔隙度相比，传统的中子、声波和中子密度孔隙度已被证明不太准确。本研究旨在通过使用常规测井（包括中子孔隙度、声波、电阻率、伽马射线和光电因子）中的三种不同机器学习 (ML) 算法来预测 NMR 孔隙度。数据来自中东一个大型碳酸盐岩石油储层，共采集了 3500 个数据点。输入参数是根据它们与输出参数的相对重要性选择的。使用三种 ML 技术，即自适应神经模糊推理系统 (ANFIS)、人工神经网络 (ANN) 和功能网络 (FN)，为开发预测模型。通过相关系数 (R)、均方根误差 (RMSE) 和平均绝对百分比误差 (AAPE) 评估模型的准确性。结果表明，所有三种预测模型都具有较高的准确性和一致性，与实际数据集相关时，无论是训练还是测试预测，均表现出较低的误差和较高的 R 值。然而，与其他两种研究的 ML 技术相比，ANN 模型的性能更好，基于最小的 AAPE 和 RMSE 误差（5.12 和 0.39）和最高的 R（0.95），用于测试和验证结果。ANFIS 和 FN 模型在测试和验证结果中的 AAPE 和 RMSE 分别为 5.38 和 0.41，6.06 和 0.48。ANFIS 和 FN 模型在测试和验证数据集的 R 分别为 0.937 和 0.942。基于测试和验证结果，ANN 模型排名第一，ANFIS 和 FN 模型分别排名第二和第三。此外，还优化了 ANN 和 FN 模型以提取明确的相关性，从而计算 NMR 孔隙度。因此，本研究表明，机器学习技术可成功应用于准确预测 NMR 孔隙度。
