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Prediction of the rate of penetration using logistic regression algorithm of machine learning model

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Abstract

Drilling engineering, as one of the main means and key links of oil and gas exploration and development, has the characteristics of intensive capital and technology, high investment and high risk. Thus, rational drilling design and optimization of the rate of penetration (ROP) have a very significant impact on the benefits of oil and gas exploration and development. This paper proposes a method for evaluating the accuracy of ROP prediction based on logistic regression model. Based on the existing drilling data and logging data in an oilfield in Xinjiang, these data are processed and statistically analyzed by MATLAB, and the classification prediction model of ROP is trained before drilling. The experimental results show that on the premise of sufficient drilling data and effective processing, compared with support vector regression, the use of logistic regression algorithm to predict ROP has higher accuracy, and its goodness of fit R2 range is within 0.80–0.86, which meets the actual drilling needs; the factors affecting the accuracy of the prediction of the ROP should include the role of lithology, and it has great influence on the selection of the characteristic data corresponding to different lithologies. During the drilling process, the different drilling parameters measured while drilling can be used to modify the real-time drilling rate prediction model. This method is expected to accurately predict the ROP before drilling, make a reasonable drilling plan and optimize the drilling parameters in real time in order to achieve the purpose of reducing drilling costs.

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Acknowledgements

This research was funded by State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, grant number “PRP/open-1610” and “National Natural Science Foundation of China, grant number 51804267.”

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Authors and Affiliations

  1. Southwest Petroleum University, Chengdu, 610500, People’s Republic of China

    Shuang Deng, Minghui Wei, Mingze Xu & Wei Cai

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  1. Shuang Deng
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  2. Minghui Wei
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  3. Mingze Xu
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  4. Wei Cai
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Correspondence to Minghui Wei.

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Responsible Editor: Santanu Banerjee

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Deng, S., Wei, M., Xu, M. et al. Prediction of the rate of penetration using logistic regression algorithm of machine learning model. Arab J Geosci 14, 2230 (2021). https://doi.org/10.1007/s12517-021-08452-x

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