Abstract
It is conducive to obtain reservoir information by effectively using the sensitivity difference of multicomponent data in them. However, it is very troublesome to find the relationship between multicomponent seismic data and reservoir properties. Although artificial neural network (ANN) can potentially solve this problem, the major challenge of ANN application in prediction of gas-bearing distribution is the generation of an accurate model under a limited dataset. Therefore, this paper expands the sample dataset, and then uses ANN to predict the gas-bearing distribution of tight sandstone reservoir based on multicomponent seismic data. First, the sample dataset of borehole-side seismic gathers was constructed through local seismic waveform data and known drilling gas-bearing information. This method extracted more labels to delineate the gas reservoir characteristics. Then, multi-layer perceptron and radial basis function neural networks were used to predict the gas-bearing distribution from multi-component seismic data. In this process, the hyperparameters of ANN were carefully selected through different evaluation indices to obtain an effective prediction model. Finally, the gas-bearing distribution throughout the entire Fenggu structural area was obtained by the developed ANN models, and the performance of the two network models were comprehensively evaluated. The results showed that compared with the support vector machine model, single component seismic attributes, and conventional geostatistics methods, the proposed method was more accurate in predicting the gas-bearing distribution, which verifies the effectiveness of the proposed method. The results of this study would be helpful in making better decisions when applying the ANNs to other datasets in the future.
Article highlights
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Multicomponent composite attributes could highlight gas characteristics and improve prediction accuracy of ANN model.
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Different ANN models are constructed for reservoir prediction and compared the prediction performance with SVM model.
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Compared with the conventional geostatistical approach, ANN has high accuracy and reliability for reservoir prediction.
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Acknowledgements
This work was supported by the Natural Science Foundation of Shandong Province (Grant No. ZR202103050722) and National Natural Science Foundation of China (Grant No. 41174098). We thank the Sinopec Petroleum Exploration and Production Research Institute for providing data for this study. We would like to thank Editage (www.editage.cn) for English language editing. We would like to thank Jian Sun, Chao Fu, and other members of the research group for their contributions to this study.
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Yang, J., Lin, N., Zhang, K. et al. ANN-based multicomponent seismic data-driven prediction of gas-bearing distribution. Geomech. Geophys. Geo-energ. Geo-resour. 8, 84 (2022). https://doi.org/10.1007/s40948-022-00393-3
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DOI: https://doi.org/10.1007/s40948-022-00393-3