RNN-based CO2 minimum miscibility pressure (MMP) estimation for EOR and CCUS applications

作者：Erfan Mohammadian, Mohamad Mohamadi‐Baghmolaei, Reza Azin, Fahimeh Hadavimoghaddam, Alexei Rozhenko, Bo Liu · 发表于：Fuel · 年份：2023 · DOI：10.1016/j.fuel.2023.130598 · 被引用次数：31 · 研究领域：Enhanced Oil Recovery Techniques、Hydrocarbon exploration and reservoir analysis、Reservoir Engineering and Simulation Methods

Accurate estimation of minimum miscibility pressure (MMP) is crucial for assessing the efficiency of most miscible and immiscible processes, specifically CO 2 -based enhanced oil recovery (EOR) methods and Carbon capture utilization and sequestration (CCUS). The experimental procedure for MMP prediction is often time-consuming and costly. On the other hand, the empirical models that have been historically used could work based on limited input parameters, ignore the importance of others and are not necessarily accurate. The novelty of the current study is using an explainable deep-learning approach, Recurrent neural network (RNN), to train a model using a multi-dimensional (22 features) dataset with 544 rows of data. The Dataset comprises mole fractions of injected gas (pure and impure CO 2 ). Out of those features, eight subsets of parameters (labelled X_1 to X_8) were used to develop models. The multi-dimensionality of the dataset makes it suitable to study the effects of various parameters on MMP, specifically in conditions of interest to CCUS-EOR applications. Among the multiple inputs tested, the model trained with X_1 and X_8 input parameters (including mole fraction of different hydrocarbon and nonhydrocarbon components and reservoir temperature) resulted in the most accurate estimations of MMP (R 2 = 0.99). To further enhance the explainability of the model, feature importance and shapely values analysis were conducted on the developed models, and the impact of each i...