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- Title
基于 DCC—LSTM 的钻井液微量漏失智能监测方法.
- Authors
孙伟峰; 卜赛赛; 张德志; 李威桦; 刘 凯; 戴永寿
- Abstract
When lost circulation happens in the process of well drilling, the existing intelligent lost circulation monitoring methods can hardly obtain the long-term characteristics of data sequence and thus cannot realize the timely monitoring and prewarning of minor lost circulations, causing more serious lost circulation. In order to address this problem, this paper proposes a DCC-LSTM based intelligent minor lost circulation monitoring method which takes the advantage of the characteristic mapping capacity of Dilated and Causal Convolution (DCC) network and the sequential characteristic extraction capacity of Long Short-Term Memory (LSTM) network. This method makes up for the shortage of LSTM in long-term memory attenuation, and realizes the accurate monitoring and prediction of minor lost circulation. And the following research results are obtained. First, in the DCC-LSTM based intelligent minor lost circulation monitoring model, the long-term characteristics of monitoring parameters are extracted by using the DCC network and then mapped into a short data sequence, and the long-term variation trend of monitoring parameters is obtained by applying the LSTM network to process the characteristic short data sequence, so that the accurate monitoring of minor lost circulation is realized. Second, the optimal number of layers in the network can be obtained by using the method for determining the number of layers in the DCC network. The new structure of DCC network can reduce the long-term sequential trend information forgotten by LSTM by 24%. Third, compared with other lost circulation monitoring methods, the DCC-LSTM network can monitor early minor lost circulations accurately, with the advanced prewarning time increasing by 26 minutes, the monitoring accuracy rate increasing from 96.9% to 99.4%, and the false alarm rate decreasing from 6.4% to 1.1%. In conclusion, this method can acquire the long-term trend variation characteristics of monitoring parameters, and the field test demonstrates its obvious advantages over other methods. It provides a feasible method for the monitoring and prediction of minor lost circulation, and is of great significance to guiding the prevention and control of lost circulation risk during well drilling.
- Subjects
LONG-term memory; CIRCULATION models; FALSE alarms; DATA mapping; MULTICASTING (Computer networks); RIGHT to be forgotten; SCARCITY
- Publication
Natural Gas Industry, 2023, Vol 43, Issue 9, p141
- ISSN
1000-0976
- Publication type
Article
- DOI
10.3787/j.issn.1000-0976.2023.09.014