PSO-based ViT-Seismic: A Vision Transformer Approach for Gas Detection in Seismic Images
Resumo
Seismic reflection is one of the geophysical methods most used in the oil and gas (O&G) industry for hydrocarbon prospecting. In particular, for some Brazilian onshore fields, such a method has been used for estimating the location and volume of gas accumulations. However, the analysis and interpretation of seismic data is time-consuming due to the large amount of information and the noisy nature of the acquisitions. In order to help geoscientists in those tasks, computational tools based on machine learning have been proposed considering Direct Hydrocarbon Indicators (DHIs). In this study, we present a methodology for detection of gas accumulations based on vision Transformer neural network (ViT) and Particle Swarm Optimization (PSO) scheme. In the best scenario, the proposed method achieved a sensitivity of 75.14%, a specificity of 96.14% and an accuracy of 95.60%. We present some tests performed on Parnaíba Basin which demonstrate that the proposed method is promising for gas exploration.
Referências
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2015). Tensorflow: Large-scale machine learning on heterogeneous systems. Software available from https://tensorflow.org.
Almeida, F. d., Carneiro, C. D. R., et al. (2004). Inundações marinhas fanerozóicas no brasil e recursos minerais associados. Mantesso Neto, V.; Bartorelli, A.; Carneiro, CDR, pages 43-60.
Andrade, F., Fernando Santos, L., Gattass, M., Quevedo, R., Michelon, D., Siedschlag, C., and Ribeiro, R. (2021). Gas reservoir segmentation in 2d onshore seismics using lstm-autoencoder. In First International Meeting for Applied Geoscience & Energy, pages 1651-1655. Society of Exploration Geophysicists.
Chevitarese, D. S., Szwarcman, D., e Silva, R. G., and Brazil, E. V. (2018). Deep learning applied to seismic facies classification: A methodology for training. In Saint Petersburg 2018, volume 2018, pages 1-5. European Association of Geoscientists & Engineers.
Chollet, F. et al. (2015). Keras. https://keras.io.
de Miranda, F. S., Vettorazzi, A. L., da Cruz Cunha, P. R., Aragão, F. B., Michelon, D., Caldeira, J. L., Porsche, E., Martins, C., Ribeiro, R. B., Vilela, A. F., et al. (2018). Atypical igneous-sedimentary petroleum systems of the parnaíba basin, brazil: seismic, well logs and cores. Geological Society, London, Special Publications, 472(1):341-360.
Di, H., Shafiq, M. A., and AlRegib, G. (2017). Seismic-fault detection based on multiattribute support vector machine analysis. In SEG Technical Program Expanded Abstracts 2017, pages 2039-2044. Society of Exploration Geophysicists.
Di, H., Wang, Z., and AlRegib, G. (2018). Seismic fault detection from post-stack amplitude by convolutional neural networks. In 80th EAGE Conference and Exhibition 2018, volume 2018, pages 1-5. European Association of Geoscientists & Engineers.
Dias, D., Diniz, P., Marin, L., Cipriano, C., Gattass, M., Santos, L., Quevedo, R., Michelon, D., Siedschlag, C., and Ribeiro, R. (2021). Automatic gas detection in land seismic using transformer neural network. In 17th International Congress of the Brazilian Geophysical Society. Brazilian Geophysical Society.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020a). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2020b). An image is worth 16x16 words: Transformers for image recognition at scale. CoRR, abs/2010.11929.
Drumnond, C. and Holte, R. (2003). Class imbalance and cost sensitivity: Why undersampling beats oversampling. In ICML-KDD 2003 Workshop: Learning from Imbalanced Datasets, volume 3.
Duda, R. (1973). Pattern classification and scene analysis. Wiley-Interscience Publication, 512.
Guitton, A., Wang, H., and Trainor-Guitton, W. (2017). Statistical imaging of faults in 3d seismic volumes using a machine learning approach. In SEG Technical Program Expanded Abstracts 2017, pages 2045-2049. Society of Exploration Geophysicists.
Júnior, D. A. D., da Cruz, L. B., Diniz, J. O. B., da Silva, G. L. F., Junior, G. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2021). Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost. Expert Systems with Applications, 183:115452.
Le, L. T., Nguyen, H., Zhou, J., Dou, J., Moayedi, H., et al. (2019). Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique pso-xgboost. Applied Sciences, 9(13):2714.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
Miranda, L. J. V. (2017). Pyswarms, a research-toolkit for particle swarm optimization in python.
Pochet, A., Diniz, P. H., Lopes, H., and Gattass, M. (2018). Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geoscience and Remote Sensing Letters, 16(3):352-356.
Santos, L., Jordao, F., Gattass, M., Quevedo, R., Lima, M. J., Michelon, D., Siedschlag, C., Ribeiro, R., and Pereira, S. (2021). Natural gas detection in onshore data using transfer learning from a lstm pre-trained with offshore data. Society of Exploration Geophysicists.
Santos, L. F., Gattass, M., Silva, A., Miranda, F., Siedschlag, C., and Ribeiro, R. (2020). Natural gas detection in onshore data using transfer learning from a lstm pre-trained with offshore data. In SEG Technical Program Expanded Abstracts 2020, pages 1190- 1195. Society of Exploration Geophysicists.
Santos, L. F. T. (2019). Detector de assinaturas de gás em levantamentos sísmicos utilizando lstm. Master's thesis, Pontifícia Universidade Católica do Rio de Janeiro.
Wrona, T., Pan, I., Gawthorpe, R. L., and Fossen, H. (2018). Seismic facies analysis using machine learning. Geophysics, 83(5):O83-O95.
Zhao, T. (2018). Seismic facies classification using different deep convolutional neural networks. In SEG Technical Program Expanded Abstracts 2018, pages 2046-2050. Society of Exploration Geophysicists.
Almeida, F. d., Carneiro, C. D. R., et al. (2004). Inundações marinhas fanerozóicas no brasil e recursos minerais associados. Mantesso Neto, V.; Bartorelli, A.; Carneiro, CDR, pages 43-60.
Andrade, F., Fernando Santos, L., Gattass, M., Quevedo, R., Michelon, D., Siedschlag, C., and Ribeiro, R. (2021). Gas reservoir segmentation in 2d onshore seismics using lstm-autoencoder. In First International Meeting for Applied Geoscience & Energy, pages 1651-1655. Society of Exploration Geophysicists.
Chevitarese, D. S., Szwarcman, D., e Silva, R. G., and Brazil, E. V. (2018). Deep learning applied to seismic facies classification: A methodology for training. In Saint Petersburg 2018, volume 2018, pages 1-5. European Association of Geoscientists & Engineers.
Chollet, F. et al. (2015). Keras. https://keras.io.
de Miranda, F. S., Vettorazzi, A. L., da Cruz Cunha, P. R., Aragão, F. B., Michelon, D., Caldeira, J. L., Porsche, E., Martins, C., Ribeiro, R. B., Vilela, A. F., et al. (2018). Atypical igneous-sedimentary petroleum systems of the parnaíba basin, brazil: seismic, well logs and cores. Geological Society, London, Special Publications, 472(1):341-360.
Di, H., Shafiq, M. A., and AlRegib, G. (2017). Seismic-fault detection based on multiattribute support vector machine analysis. In SEG Technical Program Expanded Abstracts 2017, pages 2039-2044. Society of Exploration Geophysicists.
Di, H., Wang, Z., and AlRegib, G. (2018). Seismic fault detection from post-stack amplitude by convolutional neural networks. In 80th EAGE Conference and Exhibition 2018, volume 2018, pages 1-5. European Association of Geoscientists & Engineers.
Dias, D., Diniz, P., Marin, L., Cipriano, C., Gattass, M., Santos, L., Quevedo, R., Michelon, D., Siedschlag, C., and Ribeiro, R. (2021). Automatic gas detection in land seismic using transformer neural network. In 17th International Congress of the Brazilian Geophysical Society. Brazilian Geophysical Society.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020a). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2020b). An image is worth 16x16 words: Transformers for image recognition at scale. CoRR, abs/2010.11929.
Drumnond, C. and Holte, R. (2003). Class imbalance and cost sensitivity: Why undersampling beats oversampling. In ICML-KDD 2003 Workshop: Learning from Imbalanced Datasets, volume 3.
Duda, R. (1973). Pattern classification and scene analysis. Wiley-Interscience Publication, 512.
Guitton, A., Wang, H., and Trainor-Guitton, W. (2017). Statistical imaging of faults in 3d seismic volumes using a machine learning approach. In SEG Technical Program Expanded Abstracts 2017, pages 2045-2049. Society of Exploration Geophysicists.
Júnior, D. A. D., da Cruz, L. B., Diniz, J. O. B., da Silva, G. L. F., Junior, G. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2021). Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost. Expert Systems with Applications, 183:115452.
Le, L. T., Nguyen, H., Zhou, J., Dou, J., Moayedi, H., et al. (2019). Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique pso-xgboost. Applied Sciences, 9(13):2714.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
Miranda, L. J. V. (2017). Pyswarms, a research-toolkit for particle swarm optimization in python.
Pochet, A., Diniz, P. H., Lopes, H., and Gattass, M. (2018). Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geoscience and Remote Sensing Letters, 16(3):352-356.
Santos, L., Jordao, F., Gattass, M., Quevedo, R., Lima, M. J., Michelon, D., Siedschlag, C., Ribeiro, R., and Pereira, S. (2021). Natural gas detection in onshore data using transfer learning from a lstm pre-trained with offshore data. Society of Exploration Geophysicists.
Santos, L. F., Gattass, M., Silva, A., Miranda, F., Siedschlag, C., and Ribeiro, R. (2020). Natural gas detection in onshore data using transfer learning from a lstm pre-trained with offshore data. In SEG Technical Program Expanded Abstracts 2020, pages 1190- 1195. Society of Exploration Geophysicists.
Santos, L. F. T. (2019). Detector de assinaturas de gás em levantamentos sísmicos utilizando lstm. Master's thesis, Pontifícia Universidade Católica do Rio de Janeiro.
Wrona, T., Pan, I., Gawthorpe, R. L., and Fossen, H. (2018). Seismic facies analysis using machine learning. Geophysics, 83(5):O83-O95.
Zhao, T. (2018). Seismic facies classification using different deep convolutional neural networks. In SEG Technical Program Expanded Abstracts 2018, pages 2046-2050. Society of Exploration Geophysicists.
Publicado
28/09/2022
Como Citar
DIAS JUNIOR, Domingos A.; CRUZ, Luana B.; DINIZ, João O. Bandeira; SILVA, Aristófanes Corrêa; PAIVA, Anselmo C.; GATTASS, Marcelo.
PSO-based ViT-Seismic: A Vision Transformer Approach for Gas Detection in Seismic Images. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 10. , 2022, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 11-20.
DOI: https://doi.org/10.5753/ercemapi.2022.225389.
