Investigation of Barree-Conway non-Darcy flow effects on coalbed methane production

Yang, Lei; Rui, Hongxing; Zhao, Qing‐Li

doi:10.1007/s11771-016-3398-0

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Typical methods for predicting CBM production can be broadly categorized into two approaches: physics-based and mathematical statistics-based 14 – 16 . The physics-based approach primarily relies on numerical simulation utilizing Darcy’s law 17 . This method necessitates the construction of detailed reservoir models that incorporate intricate geological characteristics and fluid flow dynamics, alongside the careful selection of relevant parameters 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

Enhanced coalbed methane well production prediction framework utilizing the CNN-BL-MHA approach

Li,

Xie

et al. 2024

Sci Rep

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As the mechanization of the CBM extraction process advances and geological conditions continuously evolve, the production data from CBM wells is deviating increasingly from linearity, thereby presenting a significant challenge in accurately predicting future gas production from these wells. When it comes to predicting the production of CBM, a single deep-learning model can face several drawbacks such as overfitting, gradient explosion, and gradient disappearance. These issues can ultimately result in insufficient prediction accuracy, making it important to carefully consider the limitations of any given model. It’s impressive to see how advanced technology can enhance the prediction accuracy of CBM. In this paper, the use of a CNN model to extract features from CBM well data and combine it with Bi-LSTM and a Multi-Head Attention mechanism to construct a production prediction model for CBM wells—the CNN-BL-MHA model—is fascinating. It is even more exciting that predictions of gas production for experimental wells can be conducted using production data from Wells W1 and W2 as the model’s database. We compared and analyzed the prediction results obtained from the CNN-BL-MHA model we constructed with those from single models like ARIMA, LSTM, MLP, and GRU. The results show that the CNN-BL-MHA model proposed in the study has shown promising results in improving the accuracy of gas production prediction for CBM wells. It’s also impressive that this model demonstrated super stability, which is essential for reliable predictions. Compared to the single deep learning model used in this study, its prediction accuracy can be improved up to 35%, and the prediction results match the actual yield data with lower error.

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Section: Introductionmentioning

confidence: 99%

Enhanced coalbed methane well production prediction framework utilizing the CNN-BL-MHA approach

Li,

Xie

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For example, Zhang et al [27] conducted a gas release test on gas-containing coal under conventional triaxial compression conditions, indirectly determining the dynamic effective stress during the gas release process by utilizing the constitutive equation and coal deformation. Yang et al [28] observed the non-Darcy flow of coalbed methane in fracture systems with high permeability and proposed mathematical and numerical models to examine the effects of non-Darcy flow using the Forchheimer non-Darcy model.…”

mentioning

confidence: 99%