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Deep Learning Reservoir Porosity Prediction Using Integrated Neural Network

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Abstract

Reservoir porosity is a crucial factor in reservoir characterization. As the availability of cores is limited, porosity needs to be predicted from indirect measurements like well logs. However, porosity prediction is a challenging task due to the spatial interdependence between sediments and the spatial coupling between the well logs data. Therefore, in the present study, to fully mine the effective information contained in log data and improve the accuracy of porosity prediction, we developed a novel deep learning method based on the one-dimensional convolutional neural network and bidirectional gated recurrent unit neural network. This new method, called an integrated neural network, was conceived to perform the porosity prediction task. The convolutional neural network was constructed for learning local correlations and extracting hierarchical correlations, while the bidirectional gated recurrent unit neural network was adopted to learn the variation trends and context information with depth from the learned local features. The designed integrated networks, combinations of the convolutional neural network and the bidirectional gated recurrent unit neural network, could exploit the strengths of both networks and overcome the limitations of each. Using this approach, porosity can be predicted from series of input log data fully considering the correlation of different log series and porosity, the variation trend, and context information with depth. The experimental results on the actual reservoir dataset revealed that, compared with bidirectional gated recurrent unit neural network, the integrated neural network’s average RMSE and MAE decreased by 10.81% and 9.85%, respectively. The results demonstrate the effectiveness of the new method in porosity prediction when only logging data are available.

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Acknowledgements

This research is financially supported by the National Natural Science Foundation of China (Grant No. 42030812, 42042046, 41974160, and 41430323).

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Authors and Affiliations

  1. Key Laboratory of Earth Exploration and Information Techniques, Chengdu University of Technology, Ministry of Education, Chengdu, 610059, China

    Jun Wang & Junxing Cao

  2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, 610059, China

    Jun Wang & Junxing Cao

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Jun Wang contributed to the conceptualization, methodology, software, writing—original draft, and validation. Junxing Cao was involved in the writing—review and editing, funding acquisition, and supervision.

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Wang, J., Cao, J. Deep Learning Reservoir Porosity Prediction Using Integrated Neural Network. Arab J Sci Eng 47, 11313–11327 (2022). https://doi.org/10.1007/s13369-021-06080-x

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