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Explainable Artificial Intelligence for Seismic Reservoir Characterization: A Review of Machine Learning, SHAP-Based Interpretability, and Future Research Directions


Authors : Rabeta Sharmin

Volume/Issue : Volume 11 - 2026, Issue 7 - July

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DOI : https://doi.org/10.38124/ijisrt/26jul1412

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Abstract : The rapid advancement of Artificial Intelligence (AI) has transformed seismic reservoir characterization by enabling automated interpretation of complex seismic datasets and improving the prediction of subsurface reservoir properties. Machine learning techniques, including ensemble learning, support vector machines, artificial neural networks, and gradient boosting algorithms, have demonstrated significant improvements in porosity prediction, lithofacies classification, fault detection, and structural mapping compared with traditional deterministic approaches. However, despite their predictive capability, most AI models operate as black-box systems, limiting transparency and reducing confidence in critical exploration and reservoir management decisions. This limitation has motivated increasing interest in Explainable Artificial Intelligence (XAI), which seeks to provide interpretable and trustworthy explanations for machine learning predictions. Among existing XAI techniques, SHapley Additive exPlanations (SHAP) has emerged as one of the most robust and theoretically sound methods for quantifying feature importance and explaining complex nonlinear models. This review examines recent developments in AI-driven seismic interpretation, digital twin technologies, machine learning-based reservoir characterization, and SHAP-based interpretability. It discusses the evolution of seismic attribute analysis from conventional statistical methods to modern explainable AI frameworks and highlights recent advances in uncertainty-aware reservoir characterization and intelligent digital reservoir systems. Furthermore, current research challenges, including model transparency, geological consistency, uncertainty quantification, data heterogeneity, and real-time explainability, are critically analyzed. Finally, future research directions involving explainable deep learning, physics-informed AI, federated learning, and digital twin-enabled intelligent reservoir management are presented. This review provides a comprehensive reference for researchers and petroleum engineers seeking to develop transparent, reliable, and trustworthy AI solutions for next-generation seismic reservoir characterization.

Keywords : Explainable Artificial Intelligence, Seismic Reservoir Characterization, Machine Learning, SHAP, Digital Twin, Seismic Interpretation, Reservoir Engineering, Feature Attribution, Hydrocarbon Exploration.

References :

  1. M. R. Islam, "System Dynamics of Leadership Influence in Sustainable Supply Chains," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 509–514, 2025, doi: 10.30574/wjaets.2025.17.3.1584.
  2. M. R. Islam, "Digital Leadership and Circular Economy Performance in Sustainable Supply Chains," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 503–508, 2025, doi: 10.30574/wjaets.2025.17.3.1583.
  3. M. R. Islam, "Circular economy leadership for sustainable industrial transformation: A holistic framework for resilient and resource-efficient growth," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 253–262, 2025, doi: 10.30574/wjaets.2025.17.3.1540.
  4. Y. A. Bipasha, M. R. Islam, and M. F. Ahmed, "A blockchain and machine learning framework for secure and transparent digital supply chain management," International Journal of Innovative Science and Research Technology, vol. 11, no. 3, pp. 945–952, Mar. 2026, doi: 10.38124/IJISRT/26MAR767.
  5. M. R. Islam and A. Halim, "Developing a challenge-driven project management framework for sustainable development: An MCDM-based evaluation and prioritization approach," International Journal of Science and Research Archive, vol. 18, no. 01, pp. 177–187, 2026, doi: 10.30574/ijsra.2026.18.1.0027.
  6. M. B. Uddin, M. R. Islam, M. N. Uddin, and A. Halim, "Next-generation plastic recycling: Breakthrough developments and the path toward a circular economy," World Journal of Advanced Engineering Technology and Sciences, vol. 16, no. 01, pp. 513–527, 2025, doi: 10.30574/wjaets.2025.16.1.1237.
  7. R. A. Elbarouni, "A review of AI-driven seismic interpretation, digital twin technology and intelligent reservoir characterization for hydrocarbon exploration," World Journal of Advanced Engineering Technology and Sciences, vol. 09, no. 01, pp. 513–519, 2023, doi: 10.30574/wjaets.2023.9.1.0147.
  8. R. A. Elbarouni, "AI-driven digital twin framework for real-time seismic reservoir monitoring and predictive hydrocarbon production optimization," World Journal of Advanced Engineering Technology and Sciences, vol. 11, no. 02, pp. 712–720, 2024, doi: 10.30574/wjaets.2024.11.2.0122.
  9. R. A. Elbarouni, "Advanced 3D seismic interpretation techniques for accurate subsurface structural mapping and reservoir characterization," International Journal of Science and Research Archive, vol. 18, no. 03, pp. 992–1002, 2026, doi: 10.30574/ijsra.2026.18.3.0539.
  10. R. A. Elbarouni, "Seismic attribute-based fault detection and structural mapping in 3D seismic data for hydrocarbon exploration," World Journal of Advanced Engineering Technology and Sciences, vol. 18, no. 03, pp. 320–329, 2026, doi: 10.30574/wjaets.2026.18.3.0166.
  11. R. A. Elbarouni, "Integrated seismic and petrophysical analysis for improved hydrocarbon reservoir characterization," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 196–207, 2026, doi: 10.30574/wjaets.2026.20.1.0364.
  12. R. A. Elbarouni, "Machine learning-based seismic attribute analysis for porosity prediction and lithofacies classification in clastic hydrocarbon reservoirs," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 231–240, 2026, doi: 10.30574/wjaets.2026.20.1.0366.
  13. R. A. Elbarouni, "Uncertainty-aware reservoir characterization using probabilistic seismic–petrophysical integration," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 219–230, 2026, doi: 10.30574/wjaets.2026.20.1.0365.
  14. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
  15. J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
  16. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794.
  17. S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017, pp. 4765–4774.
  18. M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1135–1144.
  19. C. Molnar, Interpretable Machine Learning, 2nd ed. Munich, Germany, 2022.
  20. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  21. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  22. O. Yilmaz, Seismic Data Analysis. Tulsa, OK, USA: Society of Exploration Geophysicists, 2001.
  23. A. R. Brown, Interpretation of Three-Dimensional Seismic Data, 7th ed. Tulsa, OK, USA: American Association of Petroleum Geologists (AAPG), 2011.
  24. K. J. Marfurt, R. L. Kirlin, S. L. Farmer, and M. S. Bahorich, "3-D seismic attributes using a semblance-based coherency algorithm," Geophysics, vol. 63, no. 4, pp. 1150–1165, 1998.
  25. R. S. Chopra and K. J. Marfurt, "Seismic attributes—A historical perspective," Geophysics, vol. 70, no. 5, pp. 3SO–28SO, 2005.
  26. H. Hall, K. J. Marfurt, and M. Alfarraj, "Machine learning applications in seismic interpretation: A review," Interpretation, vol. 10, no. 2, pp. SE1–SE20, 2022.

The rapid advancement of Artificial Intelligence (AI) has transformed seismic reservoir characterization by enabling automated interpretation of complex seismic datasets and improving the prediction of subsurface reservoir properties. Machine learning techniques, including ensemble learning, support vector machines, artificial neural networks, and gradient boosting algorithms, have demonstrated significant improvements in porosity prediction, lithofacies classification, fault detection, and structural mapping compared with traditional deterministic approaches. However, despite their predictive capability, most AI models operate as black-box systems, limiting transparency and reducing confidence in critical exploration and reservoir management decisions. This limitation has motivated increasing interest in Explainable Artificial Intelligence (XAI), which seeks to provide interpretable and trustworthy explanations for machine learning predictions. Among existing XAI techniques, SHapley Additive exPlanations (SHAP) has emerged as one of the most robust and theoretically sound methods for quantifying feature importance and explaining complex nonlinear models. This review examines recent developments in AI-driven seismic interpretation, digital twin technologies, machine learning-based reservoir characterization, and SHAP-based interpretability. It discusses the evolution of seismic attribute analysis from conventional statistical methods to modern explainable AI frameworks and highlights recent advances in uncertainty-aware reservoir characterization and intelligent digital reservoir systems. Furthermore, current research challenges, including model transparency, geological consistency, uncertainty quantification, data heterogeneity, and real-time explainability, are critically analyzed. Finally, future research directions involving explainable deep learning, physics-informed AI, federated learning, and digital twin-enabled intelligent reservoir management are presented. This review provides a comprehensive reference for researchers and petroleum engineers seeking to develop transparent, reliable, and trustworthy AI solutions for next-generation seismic reservoir characterization.

Keywords : Explainable Artificial Intelligence, Seismic Reservoir Characterization, Machine Learning, SHAP, Digital Twin, Seismic Interpretation, Reservoir Engineering, Feature Attribution, Hydrocarbon Exploration.

Paper Submission Last Date
31 - August - 2026

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