Research and Application of Cuttings Flow Prediction Model for Horizontal Well
DOI:
https://doi.org/10.54097/ajst.v4i3.5048Keywords:
Borehole cleaning, Cuttings weighing, BP neural network.Abstract
In the process of horizontal well drilling and logging, real-time and continuous monitoring of returned cuttings is needed to monitor the cleanliness of the hole and reduce complex drilling accidents, such as bit mud bag, wall collapse, stuck drilling, and leakage, which may affect drilling construction and logging operations. However, when the cuttings metering device fails or the acquisition data packet is lost, the cuttings data can be incomplete and discontinuous, which poses a challenge for well cleaning monitoring. In order to solve the problem of incomplete and discontinuous cuttings data caused by instrument failure or collection data packet loss of the cuttings weighing device in operation, this paper studies and compares three cuttings flow measurement prediction models to solve the problem of abnormal data caused by the failure of the cuttings weighing device or collection data packet loss. It provides a reliable means for reducing nonproductive time, monitoring and evaluating borehole cleaning.
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