应用归纳学习算法的井完整性异常多类分类:人工举升井的分析方法

M. S. Yakoot, A. Ragab, O. Mahmoud
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引用次数: 2

摘要

井的完整性已经成为一个重要的领域,越来越受到关注,并在行业研究中被大量发表。保持单口井的完整性非常重要,以确保井在其指定寿命(或更长时间)内按预期运行,并将所有风险保持在合理可行或指定的最低水平。如今,机器学习(ML)和人工智能(AI)模型在油气行业得到了广泛应用。机器学习的概念是基于强大的算法和强大的数据库。由于数据库中有大量的井失效和井屏障完整性测试和分析数据,因此开发有效的井完整性(WI)异常分类模型现在是可行的。在埃及苏伊斯湾进行了近10年的800口井的WI测试,收集了大约9000个数据点。此外,这些数据已由经验丰富的工程师进行质量控制和质量保证。数据中包含不同形式的WI故障。所述贡献参数集共包括23个屏障元件。数据被结构化并输入到11种不同的ML算法中,以构建一个自动系统工具,用于计算任何井的强加风险类别。对已部署的模型进行比较分析,以推断可依赖的最佳预测模型。11种模型包括监督和集成学习算法,如随机森林、支持向量机(SVM)、决策树和可扩展的增强技术。在11个模型中,结果表明极端梯度增强(XGB)、分类增强(CatBoost)和决策树是最可靠的算法。此外,本文还引入了新的模型混淆矩阵评价指标,克服了现有评价指标在评价模型时不考虑领域知识的问题。这种创新的模式将有助于有效利用公司资源,并将人力投入到高风险井中。因此,业务,安全,环境和业务绩效的逐步改善。通过完整性和ML的结合,这篇论文将成为油井完整性数据库管理程序设计和创建的一个里程碑。
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Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells
Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.
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