摘要
机械钻速是评估石油天然气钻井作业效率的重要指标。为准确预测新疆工区某油田钻井机械钻速,基于该工区的历史钻井数据,利用局部离群因子检测算法对数据进行预处理,建立了基于Stacking集成学习的机械钻速预测模型,该模型通过Stacking集成策略融合K近邻算法(KNN)、支持向量机算法(SVM)和随机森林算法(RF)进行预测验证。预测验证结果显示,分类准确度不高。运用遗传算法进行各基础模型参数优化。优化后,基于KNN、SVM、RF及Stacking集成4种算法,预测机械钻速准确率分别为73.7%、78.9%、81.6%及97.4%,其中Stacking集成模型预测准确率最高。基于Stacking集成学习的机械钻速预测方法开发了机械钻速预测软件,运用软件预测其他2套施工参数下的机械钻速,结果表明,预测机械钻速与实际机械钻速一致,且性能稳定,表明该模型拥有较强的泛化性和较高的预测精度。该智能算法可为新疆工区的该油田机械钻速预测与钻井施工参数优化提供一种新手段。
Rate of penetration(ROP)is an important indicator to evaluate the petroleum drilling performance.To accurately predict the ROP at an oilfield in the Xinjiang work area,the historical drilling data from the area were processed using the local outlier factor(LOF)algorithm,and an ROP prediction model based on Stacking ensemble learning was established.The model integrated by Stacking strategy with the K-nearest neighbor(KNN),support vector machine(SVM)or random forest(RF)algorithm showed inaccurate classification in the verification.The genetic algorithm was then adopted to optimize the parameters of the basic models.The optimized models integrating KNN,SVM,RF and Stacking algorithms yielded the prediction results with accuracy of 73.7%,78.9%,81.6%,and 97.4%,respectively.Clearly,the Stacking-based model gets the highest accuracy.Thus,a software was developed using the Stacking-based model.It was applied to predict the ROP under two sets of parameters.The results show that the predicted ROP matches well with the actual ROP,and the software works stable.This proves the generalization and accuracy of the Stacking-based model.This intelligent algorithm has provided a new means to predict ROP and optimize drilling parameters at the oilfield of Xinjing work area.
作者
高云伟
罗利民
薛凤龙
刘洋
严昊
郑双进
Gao Yunwei;Luo Limin;Xue Fenglong;Liu Yang;Yan Hao;Zheng Shuangjin(Shale gas Exploitation Technology Services Company,Sinopec Jianghan Oilfield Service Corporation;PetroChina Southwest Oil&Gas Field Company;School of Petroleum Engineering,Yangtze University)
出处
《石油机械》
北大核心
2024年第5期17-24,52,共9页
China Petroleum Machinery
基金
中国石油化工股份有限公司石油工程技术研究院科研项目“基于机器学习的机械钻速预测与施工参数优化方法研究”(35800000-22-ZC0607-0033)。
