基于E-ASW-LA模型的井下振动模式识别

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中图分类号：TE927文献标识码：A DOI：10.12473/CPM.202408017

Abstract：Accurately identifying downhole vibration mode can provide corresponding solutions for specific types of anomalies.To this end，a downhole vibration mode identification method based on E-ASW-LA model was proposed.The model comprises a sliding window layer，a feature extraction layer and a clasification/recognition layer.First，adynamic window wassetbasedon the variance characteristics ofthe preprocessed downhole vibrationdata，obtaining windows with differentlengths.Second，in thefeature extraction layer，the samples in windows were decomposed by empirical mode decomposition （EMD）to obtain the features that can characterize the vibration modeof thesamples，followed by dimensionalityreduction processing using thePCA algorithm.Third， these features were fed into a LSTM neural network to learn temporal dependencies，and the attntion mechanism was used to assgn weights to the features.Finally，the vibration mode was predicted basedon the weighted features.The test results show that the model effctively captures the key features of the samples，accurately reveals the inherent patterns of downhole vibration modes，and achieves an identification accuracy of 95.53% .The research conclusions provide important decision-making basis for optimizing drilling parameters and operation process schemes.

Keywords：downhole vibration mode identification；vibration data；sliding window；empirical mode decom. position；attention mechanism

0 引言

在钻井过程中，复杂的地质条件、不当的钻井参数设置等，都会导致井下异常振动。（剩余15160字）