基于数字孪生技术的勘探钻井管理系统

O. Kalinin, M. Elfimov, T. Baybolov
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引用次数: 1

摘要

油气公司的数字化转型需要持续改进工作绩效管理。石油和天然气公司努力提高工作效率,不断开发和实施数字产品。实现这种复杂的解决方案需要深入了解当前的业务流程并对其进行转换。本文论述了勘探生产井数字化管理系统的实现。勘探生产井数字化管理系统基于数字孪生思想,是所有勘探生产井的单一窗口和单一数据源。数字管理系统涵盖了从计划阶段到执行和结果评估的整个施工过程,并协调了过程阶段和相关人员之间的数据交换。数字孪生体提供的透明度提高了效率,加快了建井过程。基于人工智能和机器学习技术的认知助手在每个阶段都得到了应用:在规划时,助手搜索模拟井,在钻井时分析其设计和复杂性,并为最优的井设计提供建议,提供最佳钻井泥浆密度,并推荐最合适的测井数据集,以覆盖地质剖面的不确定性。在执行阶段,使用许多ML助手来提高效率并降低钻井时的风险:基于机器学习的自动扭矩和阻力控制模型,用于控制井眼状况,以预测任何差异卡钻、关键位置和封隔的迹象;基于数据驱动的钻头位置和方向确定模型,用于预测钻井过程中BHA的位置,包括盲区;数据驱动模型,用于识别钻头上的岩石类型,以实现正确的地质导向应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploration Drilling Management System Based on Digital Twins Technology
Digital transformation of oil and gas companies requires consistent improvement of work performance management. Oil and gas companies strive to improve work efficiency and consistently develop and implement digital products. The realization of such complicated solutions requires deep diving into current business processes and transformation of them. This paper deals with implementation of digital management system for exploration and production wells. Digital management system for exploration and production wells is based on ideology of digital twin and act as a single window and single source of data for all exploration and production wells. Digital management system covers whole construction process started from planning stage to execution and results assessment and orchestrates the exchange of data between process phases and people involved in it. Transparency provided by the digital twin improves efficiency and accelerates well construction process. Cognitive assistants based on AI and ML techniques are implemented at every stage: while planning, the assistants search analogue wells, analyze its design and complications while drilling and provide recommendations for the most optimal well design, offers the optimum drilling mud density and recommends the most suitable set of logs to cover geological section uncertainty. At the execution stage, a number of ML assistants are used to increase efficiency and reduce risks while drilling: automatic method for anomaly detection while drilling to prevent complications while drilling, machine learning based model for automatic torque and drag control to control borehole condition to predict any signs of differential stuck, key sitting and pack-off, data-driven model for drilling bit position and direction determination to predict BHA position while drilling including a blind zone, data-driven model for the identification of the rock type at a drilling bit for correct geosteering application.
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