ANN Model Replication Dataset

This study addresses a gap in applying machine learning for polymer gel viscosity prediction in improved oil recovery (IOR). A baseline ANN model was developed using 418 experimental datasets from crosslinked PAM/PEI gels under varied conditions, achieving excellent predictive accuracy and demonstrating potential for enhanced oilfield water control.<b>Repository Description</b>This repository contains all the underlying data and materials necessary to reproduce the study "Development of an Artificial Neural Network Model for Predicting the Viscosity of Crosslinked Polyacrylamide and Polyethylenimine Polymer Gel for Oilfield Water Control." The work was performed using MATLAB 2022b, and the model was developed with MATLAB’s Neural Network Toolbox.<b>Included Materials:</b><br><b>Raw Experimental Data</b>:All 418 experimental datasets are provided. These datasets include measurements of polymer gel viscosity under varying conditions—shear rate, salinity, ammonium chloride concentration, and silica nanoparticle content.<b>Model Input Data and Parameters:</b>The repository contains the complete variable dataset used as input for the ANN model. This includes detailed descriptions of the input variables, along with the corresponding raw data.<b>Model Architecture and Replication Details:</b>The ANN model is a two-layer feed-forward neural network featuring a single hidden layer with 10 neurons. The model was trained using the Levenberg-Marquardt algorithm. The hidden layer uses the tangent sigmoid (Tansig) activation function, and the output layer employs a linear (Purelin) activation function. Detailed model parameters, including neuron weights and biases, are included to facilitate replication.<b>Software and Commands:</b>MATLAB 2022b was used to develop the model. The “nftool” command from MATLAB’s Neural Network Toolbox was specifically employed to access the Neural Net Fitting app and build the model. All commands, settings, and parameters are documented within the repository.<br>No data involving human participants were collected, and all experimental data adhered to the relevant institutional and legal standards.<br>This comprehensive dataset and detailed methodology ensure that other researchers can fully interpret, replicate, and build upon this work in the field of improved oil recovery本研究针对提高采收率（Improved Oil Recovery，IOR）领域中机器学习在聚合物凝胶黏度预测应用上的研究空白展开。研究基于418组交联聚丙烯酰胺/聚乙烯亚胺（PAM/PEI）凝胶在不同实验条件下的数据集，构建了基准人工神经网络（Artificial Neural Network，ANN）模型，该模型展现出优异的预测精度，在油田控水领域具备应用潜力。<b>仓库说明</b>本仓库包含复现论文《用于油田控水的交联聚丙烯酰胺与聚乙烯亚胺聚合物凝胶黏度预测的人工神经网络模型构建》所需的全部原始数据与材料。本研究采用MATLAB 2022b完成，模型基于MATLAB神经网络工具箱开发。<b>包含材料</b><br><b>原始实验数据</b>:所有418组实验数据集均已提供，这些数据集涵盖了不同条件下的聚合物凝胶黏度测量结果，实验变量包括剪切速率、矿化度、氯化铵浓度以及二氧化硅纳米颗粒含量。<b>模型输入数据与参数</b>:本仓库包含用于人工神经网络模型训练的完整变量数据集，涵盖输入变量的详细说明与对应原始数据。<b>模型架构与复现细节</b>:本人工神经网络模型为两层前馈神经网络，包含1个含10个神经元的隐藏层。模型采用莱文贝格-马夸特（Levenberg-Marquardt）算法进行训练，隐藏层使用正切S型（Tansig）激活函数，输出层采用线性（Purelin）激活函数。仓库中还包含模型的详细参数，包括神经元权重与偏置，以方便研究复现。<b>软件与运行命令</b>:本研究使用MATLAB 2022b开发模型，具体通过MATLAB神经网络工具箱的"nftool"命令调用神经网络拟合应用程序完成模型构建。仓库中记录了全部命令、设置与参数。<br>本研究未收集任何涉及人类受试者的数据，所有实验数据均符合相关机构规范与法律标准。<br>本综合数据集与详细的方法论可确保其他研究人员能够充分理解、复现并拓展本提高采收率领域的相关研究工作。