4.5 Article

Cuttings Bed Height Prediction in Microhole Horizontal Wells with Artificial Intelligence Models

期刊

ENERGIES
卷 15, 期 22, 页码 -

出版社

MDPI
DOI: 10.3390/en15228389

关键词

cuttings bed height; artificial intelligence model; horizontal well; dimensionless model; solid-liquid flow

资金

  1. National Natural Science Foundation of China [52104009]
  2. National Key Research and Development Program [2021YFA0719101]
  3. Fundamental Research Funds for the Central Universities [2-9-2019-99]
  4. Foundation of State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing [PRP/open-2111]

向作者/读者索取更多资源

This study employs artificial intelligence models to predict the cuttings bed height in the well-bore. The experiment results show that the ANN model performs the best among other artificial intelligence models.
Inadequate drill cuttings removal can cause costly problems such as excessive drag, lower rate of penetration, and even mechanical pipe sticking. Cuttings bed height is usually used to evaluate hole-cleaning efficiency in horizontal wells. In this study, artificial intelligence models, including artificial neural network (ANN), support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM), were employed to predict cuttings bed height in the well-bore. A total of 136 different tests were conducted, and cuttings bed height under different conditions were measured in our previous study. By training four different artificial intelligence models with the experiment data, it was found that the ANN model performed best among other artificial intelligence models. The ANN model outperformed the dimensionless cuttings bed height model proposed in our previous study. Due to the amount of data points, the memory ability of RNN and LSTM models has not been entirely played compared with the ANN model.

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