| 基于机器学习算法的井漏预测与诊断理论模型研究 |
| 投稿时间：2021-11-12 修订日期：2022-01-18 点此下载全文 |
| 引用本文：张欣,田英英,韩泽龙,等.基于机器学习算法的井漏预测与诊断理论模型研究[J].钻探工程,2022,49(2):58-66.ZHANG Xin,TIAN Yingying,HAN Zelong,et al. Research on lost circulation prediction and diagnosis theoretical model based on machine learning algorithm[J]. Drilling Engineering, 2022,49(2):58-66. |
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| 基金项目:中国地质调查局地质调查项目“×××先导试验区试采实施（中国地质科学院勘探技术研究所）”（编号：DD20190227）、“×××先导试验区资源评价与试采（中国地质科学院勘探技术研究所）”（编号：DD20211349）；南方海洋科学与工程广东省实验室（广州）人才团队引进重大专项“天然气水合物钻采船单筒双井及大洋钻探技术研究”（编号：GML2019ZD0504） |
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| 中文摘要:在钻井作业中发生的井漏事故具有突发性、难治理等特点，导致现场堵漏工程难度大、成功率低、成本高等。为了“快、准、狠”地解决该类难题，必须准确预测漏失并提出堵漏措施。本文利用梯度提升决策树算法（GBDT）进行了井漏预测模型研究，通过分析若干机器学习算法，论证了GBDT作为井漏钻前预测模型的可行性；利用案例推理技术建立井漏案例诊断库，使用欧几里得算法和LB_Keogh算法进行井漏案例的检索，提出相应的井漏处理措施，通过实例验证了相似性计算的可靠性。 |
| 中文关键词:井漏 井漏预测 梯度提升决策树算法（GBDT） 案例推理 井漏诊断 |
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| Research on lost circulation prediction and diagnosis theoretical model based on machine learning algorithm |
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| Abstract:The lost circulation incident in drilling operation has the characteristics of sudden occurrence and difficulty in treatment， which leads to low success rate of plugging and high cost. In order to solve this kind of problem quickly， accurately and efficiently， we must accurately predict lost circulation and put forward plugging measures. In this paper， the gradient boosted decision tree algorithm （GBDT） is used to study the loss circulation prediction model with the feasibility of GBDT as a lost circulation prediction model verified by analyzing some machine learning algorithms. The lost circulation case decision base is established by using case-based reasoning technology， and the Euclidean algorithm and LB_Keogh algorithm are used to search lost circulation cases to put forward corresponding lost circulation treatment measures. The reliability of similarity calculation has been verified through field cases. |
| keywords:lost circulation lost circulation prediction gradient boosted decision tree （GBDT） case-based reasoning （CBR） lost circulation diagnosis |
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