Palabras clave
Propiedades petrofísicas
Red neuronal hibrida integrada
Red neuronal convolucional
Mamba
Red neuronal hibrida integrada
Red neuronal convolucional
Mamba
Cómo citar
Capquique Choque, J. (2026). Red neuronal hibrida integrada para la determinación de propiedades petrofísicas de hidrocarburos en exploración sísmica. Revista Latinoamericana De Difusión Científica, 8(15), 317-333. https://doi.org/10.5281/zenodo.21494905
Resumen
La inteligencia artificial está avanzando de manera progresiva, solucionando problemas en base al aprendizaje de datos, siendo uno de sus modelos evolucionados las redes neuronales, que son aplicadas en la exploración sísmica de hidrocarburos, para generar resultados confiables, en la determinación de propiedades petrofísicas de la roca reservorio. Pero, la cuantificación de estas propiedades, debe ser más precisa, para evitar la perforación de pozos secos, los cuales generan grandes pérdidas económicas. El presente artículo, tiene los siguientes objetivos: 1) Generar las bases teóricas para el diseño arquitectónico de una de red neuronal hibrida integrada, constituida por una red neuronal convolucional (CNN), un modelo mamba y las redes neuronales informadas por la física (PINNs); 2) Establecer la metodología de entrenamiento de la red neuronal, para posteriormente realizar predicciones de propiedades petrofísicas, que son evaluadas mediante la validación y control. Esta red neuronal, procesa la información de la parte más relevante de los datos sísmicos, genera resultados con un sentido físico y geológico, además si existiese falta de datos, se obtendrían aplicando leyes físicas.
Citas
Allo, F., Coulon, J. P., Formento, J. L., Reboul, R., Capar, L., Darnet, M., Issautier, B., Marc, S., & Stopin, A. (2021). Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks. The Leading Edge, 40(10), 751–758. https://doi.org/10.1190/tle40100751.1
Alwon, S. (2018). Generative adversarial networks in seismic data processing. SEG Technical Program Expanded Abstracts 2018, 1991–1995. https://doi.org/10.1190/segam2018-2996002.1
Bosch, M., Mukerji, T., & Gonzalez, E. F. (2010). Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75(5), 75A165-75A176. https://doi.org/10.1190/1.3478209
Das, V., & Mukerji, T. (2019). Petrophysical properties prediction from pre-stack seismic data using convolutional neural networks. SEG Technical Program Expanded Abstracts 2019, 2328–2332. https://doi.org/10.1190/segam2019-3215122.1
Ding, Y., & Chen, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research, 4(1), 70–89. https://doi.org/10.71451/ISTAER2604
Goodfellow, Ian., Bengio, Yoshua., & Courville, Aaron. (2017). Deep learning. The MIT Press.
Gu, A., & Dao, T. (2024). Mamba: Linear-Time Sequence Modeling with Selective State Spaces.
Jagtap, A., & Karniadakis, G. (2020). Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations. Communications in Computational Physics, 28(5), 2002–2041. https://doi.org/10.4208/cicp.OA-2020-0164
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422–440. https://doi.org/10.1038/s42254-021-00314-5
Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020). Reformer: The Efficient Transformer.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Li, Z., He, Q., & Li, J. (2024). A survey of deep learning-driven architecture for predictive maintenance. Engineering Applications of Artificial Intelligence, 133, 108285. https://doi.org/10.1016/j.engappai.2024.108285
Malik, J., & Dixit, H. K. (2022). Application of Rock Physics Driven Deep Machine Learning for Hydrocarbon Exploration. Society of Petroleum Geophysicists (SPG India) Feature Article, 118–123.
Mukerji, T., Jørstad, A., Avseth, P., Mavko, G., & Granli, J. R. (2001). Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics. Geophysics, 66(4), 988–1001. https://doi.org/10.1190/1.1487078
Muller, A. P. O., Costa, J. C., Bom, C. R., Klatt, M., Faria, E. L., de Albuquerque, M. P., & de Albuquerque, M. P. (2023). Deep pre-trained FWI: where supervised learning meets the physics-informed neural networks. Geophysical Journal International, 235(1), 119–134. https://doi.org/10.1093/gji/ggad215
Okpo, E. (2026). Hybrid Machine Learning Framework for Seismic Velocity Inversion. https://doi.org/10.22541/essoar.177099432.27567143/v1
Pintea, S. L., Sharma, S., Vossepoel, F. C., van Gemert, J. C., Loog, M., & Verschuur, D. J. (2022). Seismic inversion with deep learning: A proposal for litho-type classification. Computational Geosciences, 26(2). https://doi.org/10.1007/s10596-021-10118-2
Posamentier, H. W., & Walker, R. G. (2014). Facies models revisited. GeoScienceWorld.
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
Sheriff, R. E., & Geldart, L. P. (1995). Exploration seismology (2nd Edition). Cambridge University Press.
Stanier, A., Chacón, L., & Le, A. (2020). A cancellation problem in hybrid particle-in-cell schemes due to finite particle size. Journal of Computational Physics, 420, 109705. https://doi.org/10.1016/j.jcp.2020.109705
Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898717921
Taufik, M. H., Huang, X., & Alkhalifah, T. (2025). Latent Representation Learning in Physics‐Informed Neural Networks for Full Waveform Inversion. Earth and Space Science, 12(9). https://doi.org/10.1029/2024EA004107
Trahan, C., Loveland, M., & Dent, S. (2024). Quantum Physics-Informed Neural Networks. Entropy, 26(8), 649. https://doi.org/10.3390/e26080649
Trahan, S., Mosegaard, K., & Jakobsen, M. (2024). Deep Neural Network Application for 4D Seismic Inversion to Changes in Pressure and Saturation.
Vashisth, D., & Mukerji, T. (2022). Direct estimation of porosity from seismic data using rock- and wave-physics-informed neural networks. The Leading Edge, 41(12), 840–846. https://doi.org/10.1190/tle41120840.1
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 5998–6008.
Veeken, P. (2007). Seismic Stratigraphy, Basin Analysis and Reservoir Characterisation, vol. 37 (Vol. 37). Pergamon.
Waheed, U. B., Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2020). Eikonal Solution Using Physics-Informed Neural Networks. EAGE 2020 Annual Conference & Exhibition Online, 1–5. https://doi.org/10.3997/2214-4609.202011041
Waheed, U. bin, Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2021). PINNeik: Eikonal solution using physics-informed neural networks. Computers & Geosciences, 155, 104833. https://doi.org/10.1016/j.cageo.2021.104833
Wang, N., Chen, Y., & Zhang, D. (2025). A comprehensive review of physics-informed deep learning and its applications in geoenergy development. The Innovation Energy, 2(2), 100087. https://doi.org/10.59717/j.xinn-energy.2025.100087
Wei, L., Gan, L., Yang, H., Li, X., Hao, G., & Jiang, X. (2025). Advances in theoretical and technical approaches for seismic prediction of reservoir permeability. Journal of Seismic Exploration, 34(4), 1. https://doi.org/10.36922/JSE025310050
Yilmaz, Ö. (2001). Seismic Data Analysis. Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560801580
Zhang, M., Cheng, Y., & Lei, Z. (2026). Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs. Engineering Applications of Artificial Intelligence, 167, 113605. https://doi.org/10.1016/j.engappai.2025.113605
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99. https://doi.org/10.1007/s10462-024-10721-6
Alwon, S. (2018). Generative adversarial networks in seismic data processing. SEG Technical Program Expanded Abstracts 2018, 1991–1995. https://doi.org/10.1190/segam2018-2996002.1
Bosch, M., Mukerji, T., & Gonzalez, E. F. (2010). Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75(5), 75A165-75A176. https://doi.org/10.1190/1.3478209
Das, V., & Mukerji, T. (2019). Petrophysical properties prediction from pre-stack seismic data using convolutional neural networks. SEG Technical Program Expanded Abstracts 2019, 2328–2332. https://doi.org/10.1190/segam2019-3215122.1
Ding, Y., & Chen, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research, 4(1), 70–89. https://doi.org/10.71451/ISTAER2604
Goodfellow, Ian., Bengio, Yoshua., & Courville, Aaron. (2017). Deep learning. The MIT Press.
Gu, A., & Dao, T. (2024). Mamba: Linear-Time Sequence Modeling with Selective State Spaces.
Jagtap, A., & Karniadakis, G. (2020). Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations. Communications in Computational Physics, 28(5), 2002–2041. https://doi.org/10.4208/cicp.OA-2020-0164
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422–440. https://doi.org/10.1038/s42254-021-00314-5
Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020). Reformer: The Efficient Transformer.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Li, Z., He, Q., & Li, J. (2024). A survey of deep learning-driven architecture for predictive maintenance. Engineering Applications of Artificial Intelligence, 133, 108285. https://doi.org/10.1016/j.engappai.2024.108285
Malik, J., & Dixit, H. K. (2022). Application of Rock Physics Driven Deep Machine Learning for Hydrocarbon Exploration. Society of Petroleum Geophysicists (SPG India) Feature Article, 118–123.
Mukerji, T., Jørstad, A., Avseth, P., Mavko, G., & Granli, J. R. (2001). Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics. Geophysics, 66(4), 988–1001. https://doi.org/10.1190/1.1487078
Muller, A. P. O., Costa, J. C., Bom, C. R., Klatt, M., Faria, E. L., de Albuquerque, M. P., & de Albuquerque, M. P. (2023). Deep pre-trained FWI: where supervised learning meets the physics-informed neural networks. Geophysical Journal International, 235(1), 119–134. https://doi.org/10.1093/gji/ggad215
Okpo, E. (2026). Hybrid Machine Learning Framework for Seismic Velocity Inversion. https://doi.org/10.22541/essoar.177099432.27567143/v1
Pintea, S. L., Sharma, S., Vossepoel, F. C., van Gemert, J. C., Loog, M., & Verschuur, D. J. (2022). Seismic inversion with deep learning: A proposal for litho-type classification. Computational Geosciences, 26(2). https://doi.org/10.1007/s10596-021-10118-2
Posamentier, H. W., & Walker, R. G. (2014). Facies models revisited. GeoScienceWorld.
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
Sheriff, R. E., & Geldart, L. P. (1995). Exploration seismology (2nd Edition). Cambridge University Press.
Stanier, A., Chacón, L., & Le, A. (2020). A cancellation problem in hybrid particle-in-cell schemes due to finite particle size. Journal of Computational Physics, 420, 109705. https://doi.org/10.1016/j.jcp.2020.109705
Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898717921
Taufik, M. H., Huang, X., & Alkhalifah, T. (2025). Latent Representation Learning in Physics‐Informed Neural Networks for Full Waveform Inversion. Earth and Space Science, 12(9). https://doi.org/10.1029/2024EA004107
Trahan, C., Loveland, M., & Dent, S. (2024). Quantum Physics-Informed Neural Networks. Entropy, 26(8), 649. https://doi.org/10.3390/e26080649
Trahan, S., Mosegaard, K., & Jakobsen, M. (2024). Deep Neural Network Application for 4D Seismic Inversion to Changes in Pressure and Saturation.
Vashisth, D., & Mukerji, T. (2022). Direct estimation of porosity from seismic data using rock- and wave-physics-informed neural networks. The Leading Edge, 41(12), 840–846. https://doi.org/10.1190/tle41120840.1
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 5998–6008.
Veeken, P. (2007). Seismic Stratigraphy, Basin Analysis and Reservoir Characterisation, vol. 37 (Vol. 37). Pergamon.
Waheed, U. B., Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2020). Eikonal Solution Using Physics-Informed Neural Networks. EAGE 2020 Annual Conference & Exhibition Online, 1–5. https://doi.org/10.3997/2214-4609.202011041
Waheed, U. bin, Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2021). PINNeik: Eikonal solution using physics-informed neural networks. Computers & Geosciences, 155, 104833. https://doi.org/10.1016/j.cageo.2021.104833
Wang, N., Chen, Y., & Zhang, D. (2025). A comprehensive review of physics-informed deep learning and its applications in geoenergy development. The Innovation Energy, 2(2), 100087. https://doi.org/10.59717/j.xinn-energy.2025.100087
Wei, L., Gan, L., Yang, H., Li, X., Hao, G., & Jiang, X. (2025). Advances in theoretical and technical approaches for seismic prediction of reservoir permeability. Journal of Seismic Exploration, 34(4), 1. https://doi.org/10.36922/JSE025310050
Yilmaz, Ö. (2001). Seismic Data Analysis. Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560801580
Zhang, M., Cheng, Y., & Lei, Z. (2026). Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs. Engineering Applications of Artificial Intelligence, 167, 113605. https://doi.org/10.1016/j.engappai.2025.113605
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99. https://doi.org/10.1007/s10462-024-10721-6
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