Research on a Carbon Emission Prediction Method for Oil Field Transfer Stations Based on an Improved Genetic Algorithm—The Decision Tree Algorithm

Abstract

The background of “dual carbon” is accelerating low-carbon transformation in the energy field, and oil field enterprises are facing challenges in energy conservation and emissions reduction for sustainable development. However, oil field gathering and transfer station systems, which are crucial components of the onshore transportation system, face challenges in energy conservation and emissions reduction. Therefore, it is necessary to predict the carbon emissions of oil field gathering and transfer station systems. To improve the accuracy of carbon emission prediction for the system, this study proposes an improved GA-decision tree (IGA-decision tree) algorithm. First, chaotic mapping was introduced to initialize the population, ensuring a uniform distribution of initial particles in the search space and enhancing population diversity. Second, the firefly perturbation strategy was employed to avoid the problem of genetic algorithms becoming trapped in local optima during the later stages of the search. The results show that the enhanced GA-decision tree algorithm effectively avoided being stuck in local optima while performing global searches. When predicting the carbon emissions of oil field gathering and transfer stations, the improved GA-decision tree (IGA-decision tree) algorithm outperformed traditional decision tree and GA-decision tree algorithms in terms of error and convergence efficiency. It achieved a root mean square error (RMSE) value of 74.5181 and a correlation coefficient (R2) of 0.99, indicating a high level of fitness and good convergence, as well as high prediction accuracy. This algorithm contributes to carbon accounting and energy conservation efforts in oilfield gathering and the transfer station system, filling the research gap in carbon emissions prediction for the system within the framework of energy internet projects.

Mining and Analysis of Production Characteristics Data of Tight Gas Reservoirs

“…This paper applies data mining methods to the study of global tight reservoir development characteristics. Classification predictions are made using Boosting and Bagging models [19,20], which enhance the generalization ability of the models and avoid prediction defects caused by using a single model. Through comparative analysis of three ensemble models, it is concluded that the LightGBM model has the best predictive effect for different categories of tight reservoirs, and based on this, the relationship between production characteristics of tight reservoirs of different reserve sizes is determined through feature importance analysis.…”