完整论文中描述的研究开发了一种可解释的数据驱动方法，该方法使用五种方法——循环神经网络、XGBoost、数据处理组方法 (GMDH)、CatBoost 和遗传编程 (GP)——使用包含 700 多行数据的多维数据集创建模型，用于预测最小混溶压力 (MMP)。此外，还开发了两种可用于各种参数的稳健相关性。数据集中的大量特征使其适合研究不同参数在代表 CO 2提高采收率和碳捕获、使用和储存的条件下对 MMP 的影响。
大量研究揭示了用于 MMP 预测的经验模型的发展。然而，由于构建模型开发中使用的方程的方法，大多数相关性的准确度水平不够。然而，数据驱动技术利用各种数据来跟踪和确定因素之间的联系。
The study described in the complete paper develops an explainable data-driven method using five methods—recurrent neural networks, XGBoost, the group method of data handling (GMDH), CatBoost, and genetic programming (GP)—to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure (MMP). Moreover, two robust correlations are developed that can be used for a wide range of parameters. The vast range of features in the data set makes it suitable to study the effects of different parameters on MMP in conditions representative of CO2 enhanced oil recovery and carbon capture, use, and storage.
Numerous studies shed light on the development of empirical models used for MMP forecasting. Nevertheless, the accuracy levels of most correlations are inadequate because of the methods used to construct the equations used in the model’s development. Data-driven techniques, however, make use of diverse data to track and determine connections between factors.
