Real-Time Underreamer Vibration Predicting, Monitoring, and Decision-Making Using Hybrid Modeling and a Process Digital Twin

IF 1.3 4区 工程技术 Q3 ENGINEERING, PETROLEUM SPE Drilling & Completion Pub Date : 2023-01-06 DOI:10.2118/208795-pa
Jibin Shi, Laetitia Dourthe, Denis Li, Li Deng, Leonardo Louback, Fei Song, Nick Abolins, Fernando Verano, Pusheng Zhang, Joshua Groover, Diego Gomez Falla, Ke Li
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引用次数: 2

Abstract

Summary In hole enlargement while drilling (HEWD) operations, underreamers are used extensively to enlarge the pilot hole. Reamer wipeout failure can cause additional bottomhole assembly (BHA) trips, which can cost operators millions of dollars. Excessive reamer shock and vibration are leading causes of reamer wipeout; therefore, careful monitoring of reamer vibration is important in mitigating such a risk. Currently, downhole vibration sensors and drilling dynamics simulations (DDSs) are used to comprehend and reduce downhole vibration, but vibration sensors cannot be placed exactly at the reamer to monitor the vibrations in real time. DDSs are difficult to calibrate and are computationally expensive for use in real time; therefore, the real-time reamer vibration status is typically unknown during drilling operations. A process digital twin using a hybrid modeling approach is proposed and tested to address the vibration issue. Large amounts of field data are used in advanced DDSs to calibrate the HEWD runs. For each HEWD section, calibrated DDSs are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software platform as a process digital twin. During drilling, the surrogate model uses downhole measurement while drilling (MWD) data as inputs to predict reamer vibration. Wipeout risk levels are calculated and sent to the operators for real-time decision-making to reduce the possibility of reamer wipeout. Large volumes of reamer field data, including field recorded vibration and reamer dull conditions were used to validate the digital twin workflow. Then, the process digital twin was implemented and tested in two reamer runs in the Gulf of Mexico. A downhole high-frequency sensor was placed 8 ft above the reamer cutting structure in one field run, and the recorded sensor vibration data and corresponding reamer dull conditions showed a very good match with the real-time digital twin predictions in a low-vibration scenario. Cases in high vibration are needed to fully validate the feasibility and accuracy of the digital twin. State-of-the-art downhole sensors, DDS packages, large amounts of field data, and a hybrid approach are the solutions to building, calibrating, and field testing the reamer digital twin to ensure its effectiveness and accuracy. Such a hybrid modeling approach can not only be applied to reamers but also to other critical BHA components.
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使用混合建模和过程数字孪生进行实时扩眼器振动预测、监测和决策
在随钻扩眼(HEWD)作业中,扩眼器被广泛用于扩大先导孔。扩眼器清除失败可能会导致额外的底部钻具组合(BHA)起下钻,这可能会给作业者造成数百万美元的损失。扩眼器的冲击和振动过大是造成扩眼器冲蚀的主要原因;因此,仔细监测扩眼器振动对于降低此类风险非常重要。目前,井下振动传感器和钻井动力学模拟(dds)被用于理解和减少井下振动,但振动传感器无法精确地放置在扩眼器上以实时监测振动。dds难以校准,而且实时使用的计算成本很高;因此,在钻井作业中,扩眼器的实时振动状态通常是未知的。提出了一种采用混合建模方法的过程数字孪生,并对其进行了测试,以解决振动问题。在先进的dds中使用了大量的现场数据来校准HEWD的运行。对于每个HEWD段,使用校准的dds来了解扩眼器和井下振动传感器的井下振动。利用机器学习技术建立了扩眼器振动与传感器振动之间的代理回归模型。该代理模型在钻井监测软件平台中作为过程数字孪生实现。在钻井过程中,代理模型使用井下随钻测量(MWD)数据作为输入,预测扩眼器振动。计算完完井风险等级后,发送给作业者进行实时决策,以降低扩眼器发生完井的可能性。大量的扩眼器现场数据,包括现场记录的振动和扩眼器钝化情况,用于验证数字孪生工作流程。然后,在墨西哥湾进行了两次扩眼器下入测试。在一次现场作业中,在扩眼器切割结构上方8英尺处放置了一个井下高频传感器,记录的传感器振动数据和相应的扩眼器钝化情况与低振动情况下的实时数字孪生预测非常吻合。为了充分验证数字孪生的可行性和准确性,需要在高振动的情况下进行实验。最先进的井下传感器、DDS套件、大量的现场数据以及混合方法是构建、校准和现场测试扩眼器数字孪生的解决方案,以确保其有效性和准确性。这种混合建模方法不仅可以应用于扩眼器,还可以应用于其他关键的BHA组件。
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来源期刊
SPE Drilling & Completion
SPE Drilling & Completion 工程技术-工程:石油
CiteScore
4.20
自引率
7.10%
发文量
29
审稿时长
6-12 weeks
期刊介绍: Covers horizontal and directional drilling, drilling fluids, bit technology, sand control, perforating, cementing, well control, completions and drilling operations.
期刊最新文献
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