Real-time monitoring and early warning of well leakage based on big data analysis

Xu, Yinghui; Wang, Liu-peng; Zhang, Yang; Li, Mingzhen

doi:10.1088/1742-6596/1894/1/012084

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Teachers can see the trend of students' achievements by observing students' achievements, participation in classes, enthusiasm, etc. in the student system [7] . There may be a failure of the course, so as to avoid failing or retaking the course.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Design and development of information analysis system for vocational students

Du¹,

xiucui²,

rui³

2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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With the advent of the era of big data and artificial intelligence, the network has developed rapidly Students in various hardware and platforms of more and more information, how to mine from the massive data to useful information, and can predict the future development of things, so that we need to solve one of the urgent problems, through the collection of big data, preprocessing, analysis to get the results we want, and then decision analysis and judgment, is the focus of this article.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Design and development of information analysis system for vocational students

Du¹,

xiucui²,

rui³

2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

View full text Add to dashboard Cite

show abstract

“…For instance, Liu Biao et al [3] analyzed various parameters and formation characteristics during the drilling process and used support vector regression to build an intelligent prediction model for lost circulation, achieving early identification of lost circulation risks. Yingzhuo X et al [4] developed an innovative lost circulation prediction model based on deep learning and conducted a quantitative analysis of the impact of each feature on the model's prediction results. Song Yan [5] proposed an intelligent recognition method for lost circulation risk status based on extreme learning machines, achieving high-accuracy identification of lost circulation risks.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring Model for Lost Circulation Based on Unsupervised Time Series Autoencoder

Wu,

Wang,

Zhang

et al. 2024

Processes

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Lost circulation, a common risk during the drilling process, significantly impacts drilling safety and efficiency. The presence of data noise and temporal evolution characteristics pose significant challenges to the accurate monitoring of lost circulation. Traditional supervised intelligent monitoring methods rely on large amounts of labeled data, which often do not consider temporal fluctuations in data, leading to insufficient accuracy and transferability. To address these issues, this paper proposes an unsupervised time series autoencoder (BiLSTM-AE) intelligent monitoring model for lost circulation, aiming to overcome the limitations of supervised algorithms. The BiLSTM-AE model employs BiLSTM for both the encoder and decoder, enabling it to comprehensively capture the temporal features and dynamic changes in the data. It learns the patterns of normal data sequences, thereby automatically identifying anomalous risk data points that deviate from the normal patterns during testing. Results show that the proposed model can efficiently identify and monitor lost circulation risks, achieving an accuracy of 92.51%, a missed alarm rate of 6.87%, and a false alarm rate of 7.71% on the test set. Compared to other models, the BiLSTM-AE model has higher accuracy and better timeliness, which is of great significance for improving drilling efficiency and ensuring drilling safety.

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“…Ahmed et al (2020) employed artificial neural network models to foresee lost circulation in both naturally occurring and artificially produced fractures [16,17]. Mardanirad et al (2021) used a comparison between different DL (deep learning) algorithms, CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) for the classification of mud loss intensity in the Azadegan oil field, which showed the superior accuracy of the LSTM compared to other DL algorithms [18][19][20]. Jafarizadeh et al (2022) used a fusion of an optimization algorithm and a modular neural network to address the problem of mud loss.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying Improved WOA-BiLSTM

Liu,

Jia,

et al. 2023

Processes

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Drilling hazards can be significantly decreased by anticipating potential mud loss and then putting the right well control measures in place. Therefore, it is critical to provide early estimates of mud loss. To solve this problem, an enhanced WOA (Whale Optimization Algorithm) and a BiLSTM (Bidirectional Long Short Term Memory) optimization based prediction model of lost circulation prior to drilling has been created. In order to minimize the noise in the historical comprehensive logging data, a wavelet filtering technique was first used. Then, according to the nonlinear Spearman rank correlation coefficient between mud loss and logging parameter values from large to small, seven characteristic parameters were preferred, and the sliding window was used to extract the relevant data. Secondly, the number of neurons in the first and second hidden layers, the maximum training time, and the initial learning rate of the BiLSTM model were optimized using the enhanced WOA method. The BiLSTM network was given the acquired superparameters in order to improve the model’s ability to predict occurrences. Finally, the model was trained and tested using the processed data. In comparison to the LSTM model, BiLSTM model, and WOA-BiLSTM model, respectively, the improved WOA-BiLSTM early mud loss prediction in southwest Chinese oil fields suggested in this study beat the others, receiving 22.3%, 18.7%, and 4.9% higher prediction accuracy, respectively.

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Real-time monitoring and early warning of well leakage based on big data analysis

Cited by 4 publications

References 1 publication

Design and development of information analysis system for vocational students

Design and development of information analysis system for vocational students

Intelligent Monitoring Model for Lost Circulation Based on Unsupervised Time Series Autoencoder

Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying Improved WOA-BiLSTM

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