Gomaa Sayed, Soliman Ahmed Ashraf, Mansour Mohamed, El Salamony Fares Ashraf, Salem Khalaf G
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
Petroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE), El Sherouk City, Cairo, Egypt.
Sci Rep. 2025 Apr 26;15(1):14619. doi: 10.1038/s41598-025-97235-5.
Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. Initially, statistical methods were employed to analyze the input data before applying machine learning techniques. These models take into consideration the mobility ratio (M), reservoir permeability variation (V), water-oil production ratio (WOR), and initial water saturation (S). 1054 datasets were utilized to develop machine-learning models. ANN-based correlation was developed to estimate the overall oil recovery of waterflooding. The ANN proposed model achieves a high coefficient of determination (R) of 0.999 and a low root-mean-square error (RMSE) of 0.0063 on the validation dataset. On the other hand, the other machine learning models like RF, K-NN, and SVM achieve accurate estimation of overall oil recovery (R), where the coefficients of determination (R) values are 0.97, 0.95, and 0.80 and the RMSE scores are 0.0282, 0.0405, and 0.0629 on the validation dataset, respectively. The innovative application of such ML models demonstrates significant improvements in prediction accuracy and reliability, offering a robust solution for optimizing oil recovery processes. These machine learning models provide the industry and research with efficient and economical tools for accurately estimating oil recovery in waterflooding operations within heterogeneous reservoirs.
注水是应用最广泛的提高采收率技术。预测油藏注水后的总采收率对于有效的油藏管理和恰当的决策至关重要。机器学习（ML）技术提供了丰富且快速的工具，有助于预测采收率，而通过模拟研究来完成这一过程既耗时又昂贵。本文应用了四种机器学习模型：人工神经网络（ANN）、随机森林（RF）、K近邻（K-NN）和支持向量机（SVM）来估计注水的总采收率（R）。最初，在应用机器学习技术之前，采用统计方法对输入数据进行分析。这些模型考虑了流度比（M）、储层渗透率变化（V）、水油产量比（WOR）和初始含水饱和度（S）。利用1054个数据集来开发机器学习模型。开发了基于人工神经网络的相关性来估计注水的总采收率。所提出的人工神经网络模型在验证数据集上实现了高达0.999的决定系数（R）和低至0.0063的均方根误差（RMSE）。另一方面，其他机器学习模型如随机森林、K近邻和支持向量机也实现了对总采收率（R）的准确估计，在验证数据集上的决定系数（R）值分别为0.97、0.95和0.80，均方根误差分数分别为0.0282、0.0405和0.0629。此类机器学习模型的创新性应用在预测准确性和可靠性方面有显著提高，为优化采收率过程提供了强有力的解决方案。这些机器学习模型为行业和研究提供了高效且经济的工具，用于准确估计非均质油藏注水作业中的采收率。
