摘要
随着“2060年碳中和目标”的提出,中国油田开发领域CO_(2)气驱采油技术又一次得到了广泛关注。CO_(2)气驱采油既可以实现对碳资源的地下封存,又能够对油田三次采油起到主要作用。但是其驱油效果会受到CO_(2)和原油混相与否的制约,所以需要对CO_(2)-原油体系最小混相压力(minimum miscibility pressure,MMP)进行精准预测。而传统预测方法时间成本及误差过大,人工智能算法因其计算效率及准确率高便脱颖而出。使用随机森林算法对MMP主控因素进行分析,筛选出CO_(2)、H_(2)S、C_(1)、C_(2)-C_(5)、N_(2)的摩尔分数及油藏温度、平均临界温度等特征变量,采用MLP、GA-RBF、RF、PSO-GBDT、AdaBoost SVR 5种智能算法建立MMP预测模型。为此,使用了160行的数据库进行预测分析,采用5种不同评估指标及可视化图像对不同模型结果进行对比分析,并验证模型的准确性。最终测试效果证明,在数据有限的情况下,PSO-GBDT模型具有最佳的MMP预测效果,PSO-GBDT平均绝对百分比误差(mean absolute percentage error,MAPE)为4.89%,均方根误差(root mean square error,RMSE)为0.83,测试集R^(2)为0.96。此模型精度最高,灵活性、鲁棒性最强。
With the proposal of“carbon neutralization goal in 2060”,CO_(2) flooding technology has attracted wide attention again in the field of oilfield development in China.CO_(2) flooding can not only realize underground storage of carbon resources,but also play a major role in tertiary oil recovery.However,its oil displacement effect is restricted by the miscibility of CO_(2) and crude oil,so it is necessary to accurately predict the minimum miscibility pressure(MMP)of CO_(2)-crude oil system.Due to the large time cost and error of traditional prediction methods,artificial intelligence algorithm stands out because of its high computational efficiency and accuracy.Random forest algorithm was used to analyze the main control factors of MMP,and the mole fraction of CO_(2),H_(2)S,C_(1),C_(2)-C_(5),N_(2),reservoir temperature,mean critical temperature and other characteristic variables were screened out.Five intelligent algorithms including MLP,GA-RBF,RF,PSO-GBDT and AdaBoost SVR were utilized to establish the MMP prediction model.The final test results show that the PSO-GBDT model has the best MMP prediction effect under the condition of limited data.The mean absolute percentage error(MAPE)of PSO-GBDT is 4.89%,the root mean square error(RMSE)is 0.83,and R^(2) of the test set is 0.96.This model has the highest accuracy,flexibility and robustness.
作者
沈斌
杨胜来
SHEN Bin;YANG Sheng-lai(College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China;College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China)
出处
《科学技术与工程》
北大核心
2022年第18期7866-7872,共7页
Science Technology and Engineering
基金
国家科技重大专项(2016ZX05016-006-004)。
