Title: Early detection of overflow based on genetic algorithm for capturing multiple feature changes in managed pressure drilling

Authors: Meng Wang; Zhiyong Chang; Mengxuan Cao; Jiasheng Fu; Xiaosong Han

Addresses: Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Software, Jilin University, Changchun, 130012, China ' Key Laboratory of Bionic Engineering, College of Biological and Agricultural Engineering, Jilin University, Changchun, 130012, China ' Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Software, Jilin University, Changchun, 130012, China ' CNPC Engineering Technology R&D Company Limited, National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, 102206, China ' Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun, 130012, China

Abstract: Drilling overflows can result in significant losses of money and personnel. So early detection of overflows is of great practical importance. In this paper, six managed pressure drilling features related to overflow are selected, outlet and inlet flow difference, standpipe pressure, methane, ethane, pump flush and hook height. Then the variance, slope and mean of each feature within a statistical time window are calculated. Their thresholds are optimised to detect the overflow point according to the change of statistics by an improved multi-objective genetic algorithm. Logistic chaotic mapping is used to initial the genetic algorithm, and the Lévy flight is employed to improve the mutation operator. Experiments show that the new algorithm achieves an average overflow recall rate of 93.7%. The method is able to provide early warning for drilling engineers, thus further safeguarding wellbore safety. [Received: April 7, 2024; Accepted: June 6, 2024]

Keywords: overflow detection; genetic algorithm; multiple feature; managed pressure drilling.

DOI: 10.1504/IJOGCT.2025.145448

International Journal of Oil, Gas and Coal Technology, 2025 Vol.37 No.3, pp.255 - 274

Received: 26 Mar 2024
Accepted: 06 Jun 2024
Published online: 01 Apr 2025 *

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