The prediction of unconfined compressive strength (UCS) of oil well cement class “H” based on the artificial neural network (ANN) modeling approach is presented in this study. 195 cement samples were embedded with varying dosages of strength enhancing pre-dispersed nanoparticles consisting of nanosilica (nano-SiO₂), nanoalumina (nano-Al₂O₃), and nanotitanium dioxide (nano-TiO₂) at various simulated wellbore temperatures. The efficacy of the pre-dispersed nanoparticle solutions was analyzed by transmission electron microscope (TEM) images. Nano-SiO₂ and nano-Al₂O₃ displayed excellent dispersibility throughout the solution. However, nano-TiO₂ readily agglomerates which, at high concentrations, is detrimental to the UCS of cement. 70% of the data set was used to train the ANN model, 15% was used for validation, and 15% was used to test the model. The model consisted of one input layer with five nodes, one hidden layer with 12 nodes, and one output layer with one node. 12 nodes in the hidden layer resulted in the lowest mean squared error (MSE). The model parameters were saved and used after seven epochs during training, at which point the validation error began to increase leading to overfitting. The statistical performance measures consisting of MSE, the square root of the coefficient of determination (R), and the mean absolute percentage error (MAPE) showed values close to zero, one, and less than five percent, respectively. The statistical performance measures of the ANN model displayed superior results when compared to the measures obtained by the multi-linear (MLR) and random forest (RF) regression algorithms. The developed ANN model displays high predictive accuracy and can replace, or be used in combination with, destructive UCS tests which can save the petroleum industry time, resources, and capital.

本研究基于人工神经网络（ANN）建模方法，对“ H”类油井水泥的无侧限抗压强度（UCS）进行了预测。在不同的模拟井眼温度下，用不同剂量的强度增强预分散纳米颗粒包埋了195个水泥样品，这些颗粒包括纳米二氧化硅（nano-SiO ₂），纳米氧化铝（nano-Al ₂ O ₃）和纳米二氧化钛（nano-TiO ₂）。通过透射电子显微镜（TEM）图像分析了预分散的纳米颗粒溶液的功效。纳米SiO ₂和纳米Al ₂ O ₃在整个溶液中显示出优异的分散性。但是，纳米TiO ₂高浓度时容易结块，对水泥的UCS有害。数据集的70％用于训练ANN模型，15％用于验证，而15％用于测试模型。该模型由一个包含五个节点的输入层，一个包含12个节点的隐藏层和一个包含一个节点的输出层组成。隐藏层中的12个节点导致最低的均方误差（MSE）。在训练期间的七个时间段后，保存并使用了模型参数，此时验证误差开始增加，导致过拟合。由MSE，确定系数的平方根（R）和平均绝对百分比误差（MAPE）组成的统计性能指标显示值分别接近零，百分之一和少于百分之五。与通过多线性（MLR）和随机森林（RF）回归算法获得的度量相比，ANN模型的统计性能度量显示出优异的结果。改进的人工神经网络模型具有较高的预测准确性，可以替代破坏性的UCS测试或与之结合使用，从而节省石油行业的时间，资源和资金。