Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East

Wei, Chenji; Huang, Ruijie; Ding, Mingming; Yang, Jian; Xiong, Lihui

doi:10.1016/j.petrol.2022.110442

Cited by 8 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many other scholars have tapped the learning ability of convolutional neural networks for spatial features and long-short-term-memory networks for temporal data, creating ConvLSTM models to implement subsurface flow prediction, and reservoir development. Wei et al (2022) exploited the ConvLSTM to accurately predict the future saturation distribution of carbonate reservoirs based on logging data and dynamic production data.…”

Section: Model Architecture-based Embedding Mechanismmentioning

confidence: 99%

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Xie

Wang

et al. 2023

Adv. Geo-Energy Res.

View full text Add to dashboard Cite

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These "data + physics" dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.

show abstract

Section: Model Architecture-based Embedding Mechanismmentioning

confidence: 99%

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Xie

Wang

et al. 2023

Adv. Geo-Energy Res.

View full text Add to dashboard Cite

show abstract

“…In recent years, AI technologies have revolutionized various industries, with their impact being particularly notable in the field of yield prediction within the oil and gas sector. These technologies have moved beyond traditional modeling paradigms, aligning more closely with actual production scenarios and their unique characteristics 21 , 22 . Specifically, cutting-edge algorithms like Support Vector Machines, Naive Bayes, and Long Short-Term Memory Networks (LSTMs) leverage their exceptional data processing and analytical abilities to efficiently sift through vast datasets, pinpointing key influencers and constructing sophisticated nonlinear models for precise yield forecasts 14 , 23 , 24 .…”

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

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.

show abstract

A deep learning based surrogate model for reservoir dynamic performance prediction

Wang,

Xiang,

Wang

et al. 2024

Geoenergy Science and Engineering

View full text Add to dashboard Cite

Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East

Cited by 8 publications

References 37 publications

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

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

A deep learning based surrogate model for reservoir dynamic performance prediction

Contact Info

Product

Resources

About