Gomaa Sayed, Soliman Ahmed Ashraf, Mohamed Abdulrahman, Emara Ramadan, Attia Attia Mahmoud
Petroleum Engineering Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE), Elshorouk city, Cairo 11837, Egypt.
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo 11837, Egypt.
ACS Omega. 2022 Jan 20;7(4):3549-3556. doi: 10.1021/acsomega.1c06044. eCollection 2022 Feb 1.
Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination ( ) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation.
含水饱和度评估被认为是地层评价、储量估计以及预测任何油气藏生产性能的最关键方面之一。在岩心实验室进行含水饱和度测量是一项耗时且昂贵的任务。许多科学家试图利用测井数据准确估计含水饱和度，测井数据能提供连续记录且无信息损失。因此，已经开发了许多模型来关联储层特征与含水饱和度。通过在工程挑战中扩展软计算方法的应用和发展，石油工程师将其应用于估计储层的岩石物理参数。本文通过使用从碳酸盐岩岩心样本获得的383个数据集，开发了两种技术来根据孔隙度、渗透率和地层电阻率指数估计含水饱和度。这些技术是非线性多元回归（NLMR）技术和人工神经网络（ANN）技术。所提出的人工神经网络模型在计算含水饱和度方面表现出色且精度更高，与使用非线性多元回归和阿尔奇方程的经验相关性相比，其决定系数（ ）高达0.99，平均相对误差低至1.92，平均绝对相对误差低至13.62，均方根误差低至0.066。据我们所知，当前的研究在利用人工神经网络模型估计含水饱和度方面建立了一个新的基础。
