Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model

Zhang, Rui; Zhang, Chengkai; Song, Xianzhi; Li, Zukui; Su, Yinao; Li, Gensheng; Zhu, Zhaopeng

doi:10.1016/j.geoen.2023.212279

Cited by 4 publications

(1 citation statement)

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“…The aim was to provide scientific reference and guidance for environmental monitoring and control solutions in the agricultural farming industry, further improving the intelligent level of rabbit shed environment monitoring and control, and achieving precise, efficient, and sustainable farming practices. Bert, Seq2seq, and transformer are the most common time series prediction models that have been widely applied in real-life scenarios, and many scholars have verified their predictive abilities with good results (Chen et al, 2022;Huang et al, 2023;Rosmaliati et al, 2023;Takeshi et al, 2022;Yang et al, 2022;Zhang et al, 2024). The Bert model is capable of capturing contextual information, but it has a large computational load.…”

Section: Experimental Settingsmentioning

confidence: 99%

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Liu,

Yang,

Zhang

et al. 2024

International Journal of Data Warehousing and Mining

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The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.

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Section: Experimental Settingsmentioning

confidence: 99%

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Liu,

Yang,

Zhang

et al. 2024

International Journal of Data Warehousing and Mining

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Fluid classification with dynamic graph convolution network by local linear embedding well logging data

Sun,

Pang,

Zhang

et al. 2024

Physics of Fluids

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Fluid prediction is pivotal in exploration, aiding in the identification of targets and estimating reserve potential. To enhance well logging data processing, we employ local linear embedding (LLE) for dimensionality reduction. LLE effectively reduces data dimensionality by identifying local linear relationships and preserving essential local structure in a low-dimensional space, which is particularly advantageous for log data that often contains formation-specific information, including fluid content. The process of dimensionality reduction through LLE retains vital stratigraphic information, which is key for insightful subsequent analyses. Next, we utilize a dynamic graph convolutional network (DGCN) integrated with a multi-scale temporal self-attention (TSA) module for fluid classification on the reduced data. This multi-scale temporal self-attention module is specifically designed to capture time series information inherent in well logging data, allowing the model to autonomously learn and interpret temporal dependencies and evolutionary patterns in the data. This enhances the accuracy of fluid prediction, particularly in the context of varying rock layer characteristics over time. Our methodology, combining LLE with DGCN-TSA, has demonstrated high accuracy in applications such as Tarim Oilfield logging data analysis. It amalgamates advanced technologies with a robust generalization ability. In practical applications, this approach provides steadfast support for oil and gas exploration, significantly contributing to the refinement of fluid prediction accuracy.

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Adaptive Drilling Optimization Based on Online Machine Learning

Hao

2024

Preprint

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Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model

Cited by 4 publications

References 40 publications

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Fluid classification with dynamic graph convolution network by local linear embedding well logging data

Adaptive Drilling Optimization Based on Online Machine Learning

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