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An overflow intelligent early-warning model based on downhole parameters measurement
Author(s): H. X. Deng; G. H. Wei; J. L. Li; L. Ge; X. Lai; Q. Huang
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Paper Abstract

In view of the distortion and hysteresis problem in surface overflow monitoring method, measuring the downhole near-bit parameters directly to research the overflow pre-warning model is an effective way to solve the problem. However, there are few theories about the intelligent overflow early-warning model on downhole parameters measurement currently. In recent years, the rapidly developing artificial intelligence technology has brought opportunities for the solution of the problem. In this paper, based on the study of overflow parameters and their characterization, an overflow intelligent early-warning model based on a layered fuzzy expert system is proposed, in which the drilling experts’ knowledge and experiences are used and overflow intelligent characterization combined to realize drilling overflow intelligent early-warning. The simulation experiment platform is used to verify the drilling overflow intelligent early warning system, which shows that the system can perform early-warning quickly and accurately, and has a good application prospect.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083624 (29 October 2018); doi: 10.1117/12.2514042
Show Author Affiliations
H. X. Deng, Southwest Petroleum Univ. (China)
G. H. Wei, Southwest Petroleum Univ. (China)
J. L. Li, China National Petroleum Corp. (China)
L. Ge, Southwest Petroleum Univ. (China)
X. Lai, Southwest Petroleum Univ. (China)
Q. Huang, Southwest Petroleum Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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