Abdel Azim Reda, Hamada Ghareb
Petroleum Engineering Department, American University of Kurdistan, Duhok 40025, Kurdistan Region of Iraq.
ACS Omega. 2022 Aug 16;7(34):29666-29674. doi: 10.1021/acsomega.2c01945. eCollection 2022 Aug 30.
The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient ( ) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.
准确测定泥质砂岩储层中的含水饱和度，对原地烃类估算以及可能含烃层段的选择具有重大影响。现有的用于估算含水饱和度的数值方程并不可靠，且依赖于实验室岩心分析。因此，本文尝试运用人工智能方法开发一个用于预测含水饱和度（Sw）的人工神经网络模型（ANN）。该ANN模型利用来自埃及苏伊士湾、尼罗河三角洲和西部沙漠地区油田的2700个岩心测量点进行开发和验证，输入参数包括地层深度、井径尺寸、声波时差、伽马射线（GRs）、浅电阻率（Rxo）、中子孔隙度（NPHI）、光电效应（PEF）、体积密度和深电阻率（Rt）。研究结果表明，ANN模型的优化过程通过如下方式分配收集到的数据来实现：80%用于训练，20%用于测试，其相关系数为0.973，均方误差（MSE）为0.048。此外，从ANN模型中提取出一个数学方程，该方程可用于以简单直接的方式估算地层含水饱和度。所开发的方程可与现有的测井商业软件结合使用，以提高含水饱和度预测的准确性。运用已发表的相关性方法（Waxman和Smits方法、双水模型以及有效模型）进行了对比研究，以展示所提出的ANN模型和提取方程的稳健性。结果表明，所提出的相关性方法和ANN模型在计算地层含水饱和度方面表现出色且精度更高，相关系数（ ）为0.973，均方误差（MSE）最低为0.048，平均绝对相对误差（AAPRE）最低为0.042，标准差（SD）为0.24。据我们所知，当前的研究以及所提出的ANN模型在估算地层含水饱和度方面建立了一个新的基础。
