基于主成分分析和梯度增强决策树算法的套管损伤预测方法

M. Song, Xiangguang Zhou
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引用次数: 5

摘要

套管损伤检测与预防是油气田开发项目中的一项重要工作。套管损坏将直接影响到油气产量、注水效果和油、气、水井生命周期。随着各油田套管损坏井数量的逐年增加,在油田开发过程中,对套管损坏状况的检查和预防变得越来越重要。随着大数据技术的发展,可以基于历史数据和实时数据预测套管损坏情况,优化套管维修间隔。提出了一种基于主成分分析(PCA)和梯度增强决策树(GBDT)算法的套管损伤预测方法。建立了一个三节点Spark大数据平台,在中国中东某油田的数据集上对本文提出的方法进行了测试。首先,根据数据分析和专业知识,选取了对套管损伤影响最大的10个参数,包括套管外径、壁厚、射孔密度等;其次,利用主成分分析法对参数进行降维。第三,利用GBDT建立套管损伤风险评估算法模型,并对模型参数进行训练和优化。第四,应用该模型进行套管损伤预测。利用精度和ROC曲线下面积(Area Under ROC Curve, AUC)对所提出方法进行评价,并与决策树、逻辑回归和Naïve贝叶斯等传统方法进行比较,实验结果表明,与传统方法相比,所提出方法的精度为86.3%,AUC为1,均高于传统方法,具有更好的预测精度和性能。该方法还可用于正常井变成套管损坏井的概率预测,为现场作业提供决策支持。该方法运用数据驱动思想,为油田作业提供决策支持,为油田智能化建设奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Casing Damage Prediction Method Based on Principal Component Analysis and Gradient Boosting Decision Tree Algorithm
The examination and prevention of casing damage is an important work in oil and gas field development projects. Casing damage will directly influence the production of oil and gas, the effect of water injection and the life cycle of oil, gas and water well. With the number of casing damage wells increasing year by year in each oil field, inspections and precautions of casing damage status have become more and more important during development of the filed. With the development of big data technologies, we can predict the casing damage based on historical and real-time data, and optimize the maintenance intervals of casing. In this paper, we proposed a casing damage prediction method based on principal component analysis (PCA) and gradient boosting decision tree (GBDT) algorithm. Building a three-nodes Spark big data platform, we tested our proposed method on a dataset in an oil-field in mid-east China. Firstly, based on data analysis and expertise, selected 10 parameters which affect the casing damage most, including casing outside diameter, wall thickness, perforation density etc. Secondly, using PCA to reduce the dimension of parameters. Thirdly, building the algorithm model of casing damage risk assessment by GBDT, training and optimizing the model parameters. Fourthly, using the proposed model for casing damage prediction. Using precision and AUC(Area Under ROC Curve) to evaluate the proposed method, and compared with traditional methods, including decision tree, logistic regression and Naïve Bayes, the experiment results show that compared with traditional methods, the proposed method gain 86.3% precision and AUC is 1, both higher than traditional methods, which means it has better prediction precision and performance. The proposed method also can apply to predict the probability of a normal well becoming a casing damage well, and finally can provide the decision support for on-site operations. The proposed method using data-driven idea to provide the decision support for oilfield operations, and lay the foundation for the construction of intelligence oilfield.
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