钻井自动化

机器学习有助于实时标记异常压力损失

本文介绍了一种机器学习方法，可以准确地标记异常压力损失并识别其根本原因。

图 1'异常压力损失信念尖峰反应协议。
图 1'异常压力损失信念尖峰反应协议。
来源：SPE 224876。

钻井作业过程中出现的异常压力损失表明流体液压循环系统存在故障。这可能是由于钻杆（杆体或连接处）被冲蚀、井底钻具组合（BHA）部件或连接处出现问题、井下工具故障、地面泥浆泵故障或地层漏失造成的。本文介绍了一种基于机器学习的方法，可以准确标记异常压力损失并识别其根本原因。

异常压力损失检测方法

该异常压力损失检测方法基于先前开发的用于检测冲蚀和泵故障的概率方法。异常压力损失置信度结合了泵故障置信度和冲蚀置信度。

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Drilling automation

Machine Learning Helps Flag Abnormal Pressure Loss in Real Time

This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.

Fig. 1—Abnormal-pressure-loss-belief spike response protocol.
Fig. 1—Abnormal-pressure-loss-belief spike response protocol.
Source: SPE 224876.

Abnormal pressure loss during a drilling operation signals a failure in the fluid hydraulics-circulating system. This could be the result of a washout in the drillpipe (either in the body or connection), a problem with a bottomhole assembly (BHA) component or connection, a downhole tool failure, a surface mud pump problem, or losses into the formation. This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify the root causes.

Methodology for Detecting Abnormal Pressure Loss

The method for detecting abnormal pressure loss builds on a previously developed probabilistic approach to detecting washouts and pump failure. Abnormal-pressure-loss belief combines pump-failure belief and washout belief.

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