Gad Mohammed, Mahmoud Ahmed Abdulhamid, Panagopoulos George, Kiomourtzi Paschalia, Kirmizakis Panagiotis, Elkatatny Salaheldin, Waheed Umair Bin, Soupios Pantelis
Department of Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
ACS Omega. 2025 Jan 3;10(1):557-566. doi: 10.1021/acsomega.4c07175. eCollection 2025 Jan 14.
Water saturation plays a vital role in calculating the volume of hydrocarbon in reservoirs and defining the net pay. It is also essential for designing the well completion. Innacurate water saturation calculation can lead to poor decision-making, significantly affecting the reservoir's development and production, potentially resulting in reduced hydrocarbon oil recovery. Various techniques to estimate the water saturation in both clean and shaly formations. However, the most widely used approaches in the petroleum industry rely on petrophysical models, including Archie's equation, Waxman-Smits, Simandoux, Indonesia, and dual-water models. Most of these methods are only valid for clean sands or carbonate, while the presence of clay significantly limits the accuracy of these models. On the other hand, the estimation of the water saturation through core analysis does not usually cover a large interval of the well, is highly costly, and requires much time. In this study, an empirical equation for predicting water saturation based on the weight and biases of the artificial neural networks (ANN) was developed. 334 data points of the shale volume, formation deep resistivity, porosity, and permeability and their corresponding water saturation collected from the Epsilon Field in Greece were considered for optimizing the ANN model. The ANN model was trained on 252 data sets, where the water saturation was predicted with an average absolute percentage error (AAPE) of 0.90%. Then, an empirical equation was developed based on the optimized ANN model and its weights and biases. The developed equation predicted the water saturation for the remaining 82 data sets (testing data) with an AAPE of 1.08%. The newly established empirical correlation enhances the precision of water saturation prediction and provides a cost-effective means to acquire a continuous water saturation profile, a critical asset for oilfield management and hydrocarbon exploration.
含水饱和度在计算油藏中的烃类体积和确定有效厚度方面起着至关重要的作用。它对于完井设计也至关重要。不准确的含水饱和度计算可能导致决策失误，严重影响油藏的开发和生产，可能导致烃类采收率降低。有多种技术可用于估算清洁地层和含泥质地层中的含水饱和度。然而，石油工业中最广泛使用的方法依赖于岩石物理模型，包括阿尔奇方程、韦克斯曼 - 斯米茨模型、西曼杜克斯模型、印度尼西亚模型和双水模型。这些方法大多仅对清洁砂岩或碳酸盐岩有效，而粘土的存在会显著限制这些模型的准确性。另一方面，通过岩心分析估算含水饱和度通常不能覆盖井的较大层段，成本高昂且耗时。在本研究中，基于人工神经网络（ANN）的权重和偏差开发了一个预测含水饱和度的经验方程。考虑了从希腊埃普西隆油田收集的334个页岩体积、地层深电阻率、孔隙度、渗透率数据点及其相应的含水饱和度，用于优化ANN模型。ANN模型在252个数据集上进行训练，预测含水饱和度的平均绝对百分比误差（AAPE）为0.90%。然后，基于优化后的ANN模型及其权重和偏差开发了一个经验方程。所开发的方程对其余82个数据集（测试数据）预测含水饱和度的AAPE为1.08%。新建立的经验关联提高了含水饱和度预测的精度，并提供了一种经济有效的方法来获取连续的含水饱和度剖面，这是油田管理和烃类勘探的关键资产。
