Enhanced recovery

CO₂ enhanced oil recovery (EOR) provides an attractive and commercially established technique to store CO₂ underground. EOR modeling is crucial because complex simulation is required to predict the behavior of CO₂ and its interaction with the oil and reservoir rock.
The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.
The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

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