Holistic autonomous model for early detection of downhole drilling problems in real-time

Elmgerbi, Asad; Thonhauser, Gerhard

doi:10.1016/j.psep.2022.06.035

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…The performance and longevity of roller cone bits are directly influenced by the effectiveness of these metal seals [4]. However, when subjected to varying downhole temperature and pressure conditions, as well as external mudflow [5,6], the metal seals are susceptible to a range of issues, including an inadequate opening force between end faces, an excessive temperature rise, and severe wear [7,8]. Previous studies have shown that the utilization of surface texture technology can enhance the load-bearing capacity of the liquid film on end faces and the anti-wear performance of conventional mechanical seals [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Field Coupling Numerical Analysis and Experimental Validation of Surface-Textured Metal Seals in Roller Cone Bits

Ma,

Li,

Yuan

et al. 2024

Lubricants

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Metal seals play a crucial role in ensuring the dependable functioning of the bearing system in roller cone bits. Due to the intricate nature of downhole conditions, the microstructure design of sealing end faces, specifically the surface texture, is rarely used in metal seals. This study focuses on examining the applicability of surface texture technology in metal seals for cone bits to enhance the lubrication characteristics and friction and wear properties of liquid film between end faces. A multi-field coupling model of surface-textured metal seals was established and experimentally verified. The liquid film carrying the capacity and sealing performance of five different shapes of surface-textured end faces (circle, ellipse I (horizontal), ellipse II (vertical), triangle, and chevron) were investigated under varying rotational speeds and environmental pressures. The influences of texture parameters, including depth and area ratio, on the lubrication characteristics and friction reduction effects of surface-textured metal seals were investigated, and the microscopic wear morphology characteristics of the sealing end faces were analyzed. The results show that surface textures can effectively improve the liquid film state and comprehensive performance of metal seals. Specifically, triangle textures significantly enhance the hydrodynamic pressure effect and weaken the abrasive wear and adhesive wear of the end faces. However, when the environmental pressure is p ≥ 30 MPa, the surface textures gradually lose the advantages of dynamic lubrication but can still reduce friction. The proper texture depth and area ratio can achieve zero leakage (obtained theoretically) and minimal friction in metal seals.

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Section: Introductionmentioning

confidence: 99%

Multi-Field Coupling Numerical Analysis and Experimental Validation of Surface-Textured Metal Seals in Roller Cone Bits

Ma,

Li,

Yuan

et al. 2024

Lubricants

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“…Additionally, reconstruction analysis on individual drilling parameters was conducted to explain the trained model's predictions. Asad E et al [14] detected formation issues by constructing a risk prediction window, helping to take corrective actions on site in advance. Testing on historical datasets showed successful detection within an average duration of 120 min before the incidents occurred.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM

Zhang,

Dong,

Yang

et al. 2024

Processes

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Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement.

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“…Then the drilling risk monitoring model is established to model the response of pressure and flow rate. Elmgerbi A et al [16] can automatically analyze real-time drilling data and accurately detect and verify the presence of the most common downhole drilling problems after its effective start-up. Wanjun H U et al [17] established a sample database of various safety risks, designed a two-layer convolutional neural network architecture according to the form of gas drilling monitoring data samples, extracted and learned the change rules and related characteristics of multiple monitoring parameters, and according to the training results of the neural network, Select different kinds of safety risk samples to improve the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Early Gas Kick Warning Based on Temporal Autoencoder

et al. 2023

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The timing of the data is not taken into account by the majority of risk warnings today. However, identifying temporal fluctuations in data, which is a vital method for detecting risk, is neglected by the majority of intelligent gas kick warning models now in use. To accurately and early detect the gas kick risk, a temporal series gas kick detection method based on sequence-to-sequence depth autoencoder is proposed in this paper. A depth autoencoder model based on bidirectional long short-term memory (BiLSTM-AE) network is established to encode and compress input series, and decode and reconstruct the output series. Firstly, the BiLSTM-AE network is trained on normal drilling data based on unsupervised learning. Then, the model is tested by gas kick data, and the mean square error of reconstruction is calculated. The results show that the BiLSTM-AE model is more robust and generalized, and its accuracy is 95%. Experimental preliminary results show that this approach is capable of extracting bidirectional temporal information from risk sequence data, but long short-term memory (LSTM) and autoencoder models based on multilayer perceptron (MLP-AE) are unable to do so. By taking into account the temporal characteristics of the data, this study offers a strategy to integrate prior knowledge and significantly enhances the accuracy and stability of the model.

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Holistic autonomous model for early detection of downhole drilling problems in real-time

Cited by 6 publications

References 20 publications

Multi-Field Coupling Numerical Analysis and Experimental Validation of Surface-Textured Metal Seals in Roller Cone Bits

Multi-Field Coupling Numerical Analysis and Experimental Validation of Surface-Textured Metal Seals in Roller Cone Bits

Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM

Early Gas Kick Warning Based on Temporal Autoencoder

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