Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Comparative Study of the Supervised Learning Model for Rate of Penetration Prediction Using Drilling Efficiency Parameters

시추효율매개변수를 이용한 굴진율 예측 지도학습 모델 비교 연구

  • Han, Dong-Kwon (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University) ;
  • Sung, Yu-Jeong (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University) ;
  • Yang, Yun-Jeong (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University) ;
  • Kwon, Sun-Il (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
  • Received : 2021.07.15
  • Accepted : 2021.07.28
  • Published : 2021.08.31
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Abstract

Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.

굴진율은 시추작업에서 효율성을 극대화하기 위한 중요한 변수 중 하나이다. 시추효율을 극대화하기 위해서는 시추속도를 향상시키는 것이 필요한데 시추 엔지니어에게 시추 중 문제를 확인할 수 있는 실시간 굴진율 예측이 중요하다. 굴진율은 시추스트링 회전속도, 비트하중, 시추이수 유량과 높은 상관성을 가지고 있다. 이 논문에서는 시추효율매개변수 자료를 통해 학습한 데이터기반 지도학습 모델을 이용하여 굴진율을 예측하였다. 회귀모델의 성능 평가 지표를 통해 비교한 결과 RF 모델의 RMSE가 4.20, MAPE는 9.08%로 예측성능이 가장 우수한 것으로 확인되었다. 제안한 방법은 실시간 시추운전가이드 시스템 구축 시 굴진율 예측 기반 모델로 활용될 수 있다.

Keywords

Acknowledgement

This research was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2019-0-01561-002, Development and Application of Artificial Intelligence Techniques for Geospatial Information Analysis)

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