Oil recovery during in situ combustion is majorly controlled by hydrocarbon oxidation and pyrolysis reactions, which govern fuel formation and heat evolution. Fuel deposition, in turn, can be accurately predicted in part through crude oil pyrolysis using thermogravimetry analysis (TGA). The theoretical models based on TGA runs, however, may be limited to crude oil samples at hand. In this study, we develop more general models to predict the residual mass during crude oil pyrolysis based on multi-layer perceptron (MLP), cascade forward (CFNN), generalized regression (GRNN), and radial basis function (RBF) neural networks. More than 2000 experimental data spanning wide range of weight percentages of asphaltenes and resins as well as ^oAPI gravities, heating rates, and temperatures are used. Moreover, six optimization algorithms; including Bayesian regularization (BR), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), conjugate gradient backpropagation with Fletcher-Reeves updates (CGF), resilient backpropagation (RB), and conjugate gradient backpropagation with Polak-Ribiére updates (CGP) are used to improve the performance and prediction ability of the MLP and CFNN neural networks. The CFNN model optimized with the LM algorithm best fits all the experimental data with a mean absolute percent relative error of 1.04%. Lastly, a mathematical correlation is developed utilizing the group method of data handling (GMDH) to estimate the residual mass of crude oil pyrolysis in TGA. Despite its simplicity, the correlation also provides very good estimates. Sensitivity analysis showed that temperature followed by asphaltenes and resin content showed the highest effect on mass loss during crude oil pyrolysis. Outliers estimation applying the Leverage approach suggested only 1% of the data points could be doubtful.

原位燃烧过程中的油回收主要受烃氧化和热解反应控制，这些反应控制着燃料的形成和放热。反过来，可以使用热重分析（TGA）通过原油热解来部分准确地预测燃料沉积。但是，基于TGA运行的理论模型可能仅限于手头的原油样品。在这项研究中，我们基于多层感知器（MLP），级联正向（CFNN），广义回归（GRNN）和径向基函数（RBF）神经网络，开发了更通用的模型来预测原油热解过程中的残留质量。超过2000的实验数据跨越宽范围的沥青质的重量百分比和树脂以及的^ö使用API​​重力，加热速率和温度。此外，还有六种优化算法；包括贝叶斯正则化（BR），比例共轭梯度（SCG），Levenberg-Marquardt（LM），具有Fletcher-Reeves更新（CGF）的共轭梯度反向传播，弹性反向传播（RB）和具有Polak-Ribiére更新（CGP）的共轭梯度反向传播）用于提高MLP和CFNN神经网络的性能和预测能力。用LM算法优化的CFNN模型最适合所有实验数据，平均绝对百分比相对误差为1.04％。最后，利用数据处理的分组方法（GMDH）建立了数学相关性，以估算TGA中原油热解的残留量。尽管相关性很简单，但它也提供了很好的估计。敏感性分析表明，温度，沥青质和树脂含量对原油热解过程中的质量损失影响最大。应用杠杆方法的离群值估计表明，只有1％的数据点是可疑的。