A novel CO2-EOR potential evaluation method based on BO-LightGBM algorithms using hybrid feature mining

Abstract

The main application of carbon capture, utilization, and storage (CCUS) in oil-and-gas field development engineering is carbon-dioxide enhanced oil recovery (CO2-EOR). However, accurate and rapid assessment of its application potential remains a major challenge. Traditional methods almost always evaluate whether a reservoir is suitable for the CO2-EOR technology by customizing static screening indicators and establishing threshold ranges. That is irrefutably time consuming, laborious, and subjective. In this study, a novel CO2-EOR potential assessment method based on hybrid feature mining and a light gradient boosting machine using Bayesian optimization (BO-LightGBM) is proposed. The fair cut tree and synthetic minority oversampling technique (FCT-SMOTE) were used to correct the missing and unbalanced oilfield data. This has ensured that the model has a balanced high classification accuracy for each type of reservoir. Hybrid feature selection was used to analyze oilfield data to improve the learning ability of the model and improve the calculation efficiency. To achieve multitasking scalability and rapidity, BO-LightGBM was adopted as a classifier for judging the CO2-EOR potential of reservoirs, and a regressor for predicting EOR factor. The proposed classification model has a prediction accuracy of 98.7%, and the proposed regression model has a root mean square error (RMSE) of 1.72, coefficient of determination (R2) of 0.95, and mean absolute percentage error (MAPE) of 18%. Overall, the CO2-EOR potential assessment method proposed in this study has the advantages of high accuracy, scalability, and speed. In addition, it should be emphasized that using static weight indicators to evaluate CO2-EOR potential may not be appropriate. Rather, machine learning (ML) methods to evaluate the application potential of CO2-EOR is recommended. This is because ML methods can divide the scope of multiple factors simultaneously, providing more intelligent and accurate results.