Kick Prediction Method Based on Artificial Neural Network Model

Yulai Zhao, Zhiqiang Huang (), Fubin Xin, Guilin Qi and Hao Huang ()
Additional contact information
Yulai Zhao: College of Petroleum Engineering, Yangtze University, Wuhan 430199, China
Zhiqiang Huang: College of Petroleum Engineering, Yangtze University, Wuhan 430199, China
Fubin Xin: College of Petroleum Engineering, Yangtze University, Wuhan 430199, China
Guilin Qi: College of Petroleum Engineering, Yangtze University, Wuhan 430199, China
Hao Huang: College of Petroleum Engineering, Yangtze University, Wuhan 430199, China

Energies, 2022, vol. 15, issue 16, 1-11

Abstract: Kick is one of the most important drilling problems, and because its occurrence makes drilling engineering extremely complex, it is essential to predict the possibility of kick as soon as possible. In this study, k-means clustering was combined with four artificial neural networks: regularized RBFNN, generalized RBFNN, GRNN, and PNN, to estimate the kick risk. To reduce data redundancy and normalize the drilling data, which contain kick conditions, k-means clustering was introduced. The output layer weights were then determined using a brute-force search with different Gaussian function widths, resulting in a series of artificial neural networks composed of different clustering samples and different Gaussian function widths. The results showed that the prediction accuracy of regularized RBFNN + k-means model was the highest, that of the GRNN + k-means model was the lowest. The kick prediction accuracy for regularized RBFNN, generalized RBFNN, GRNN, and PNN were 75.90%, 65.20%, 51.70%, and 70.16%, respectively. This method can be used to enhance the speed and accuracy of kick risk prediction in the field while facilitating the use of and advances in risk warning technology for deep and high-temperature and high-pressure wells.

Keywords: kick; neural network; k-means clustering; normalized RBFNN; data learning and training (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/16/5912/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/16/5912/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:16:p:5912-:d:888626

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().