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  • How can the hole cleaning belief be used in the field?

    We will start by saying that a properly developed well program takes into consideration adequate hole cleaning. If the rig crew follow the well program, and well has no surprise in store, the hole cleaning belief will not be something to be concerned about all. Sometimes miscommunication between the office and the rig can lead to wrong or insufficient hole cleaning actions. It is in such scenarios that the hole cleaning belief becomes quite useful. Since the model automatically tracks what the rig crew is doing, they can be alerted if hole cleaning operations are not sufficient. Additionally, since the model also outputs possible root causes for the bad hole (see last week’s post on root cause identification), corrective actions can be appropriately taken. We suggest that when the hole cleaning belief goes below 0.5, the rig crew pick up the phone and discuss with the office as to what needs to be done to keep the hole clean. When it comes to hole cleaning, prevention is better than cure.

    The first four slides in this post discuss operations that generally positively affect hole cleaning. If the rig operations are such that there is sufficient circulation, sufficient rotation, reaming/back reaming, etc., the belief stays high. Now, we have to date not incorporated the packoff belief into the hole cleaning model. This is because packoffs can also occur quite unexpectedly due to wellbore stability issues. We present the packoff belief network in Slide 5 and will go into it in detail later this year when we get into the topic of stuck pipe risk assessment. To have confidence that you don’t have a hole cleaning problem, both the hole cleaning belief and the packoff belief needs to be monitored and kept within a certain threshold.

    Click below for slides on this topic:

    Hole Cleaning – Field Use

    We hope you found this series on hole cleaning monitoring useful. We will go into automated rig state detection next week. So stay tuned, to keep learning.