1887
Volume 74, Issue 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

As demand for large‐scale seismic data interpretation tasks increases, machine learning‐based horizon autotracking methods have gained attention in the geological and geophysical fields. Although such methods have demonstrated time‐ and cost‐efficiency in large‐scale data interpretation, studies on the expansion of interpreted horizons into the reservoir characterization process are relatively limited. Hence, a reservoir characterization process that can incorporate the machine learning‐interpreted horizons and their structural uncertainties into the reservoir uncertainty assessment is necessary for an efficient reservoir modelling process. The proposed workflow consists of various modelling processes, including horizon construction where machine learning‐interpreted horizons are used instead of manually interpreted horizons, facies modelling and petrophysical modelling. The modelling algorithms are based on stochastic methods: sequential indicator simulation for facies models and Gaussian random function simulation for petrophysical properties. Each modelling process incorporates variables such as variogram parameters, facies ratios and modified porosity values. The results show promising performance in incorporating machine learning‐interpreted horizons into the uncertainty quantification process and analysing their impact by capturing the influence of structural uncertainties of horizons in the final reservoir pore volume.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.70119
2026-01-05
2026-02-15
Loading full text...

Full text loading...

References

  1. Al‐Mudhafar, W. J.2016. “Multiple‐Point Geostatistical Lithofacies Simulation of Fluvial Sand‐Rich De‐Positional Environment: A Case Study From Zubair Formation/South Rumaila Oil Field.” In the Offshore Technology Conference, 16OTC. OTC.
    [Google Scholar]
  2. J Al‐Mudhafar, W.2019. “Bayesian Kriging for Reproducing Reservoir Heterogeneity in a Tidal Depositional Environment of a Sandstone Formation.” Journal of Applied Geophysics160: 84–102.
    [Google Scholar]
  3. Al‐Mudhafar, W. J., D. N.Rao, and S.Srinivasan. 2018. “Geological and Production Uncertainty Assessments of the Cyclic CO2‐Assisted Gravity Drainage EOR Process: A Case Study from South Rumaila Oil Field.” Journal of Petroleum Exploration and Production Technology9: 1457–1474.
    [Google Scholar]
  4. Bahar, A., H.Ates, M.Kelkar, and M.Al‐Deeb. 2001. “Methodology to Incorporate Geological Knowledge in Variogram Modeling.” In The SPE Asia Pacific Oil and Gas Conference and Exhibition, 01APOGCE. SPE.
    [Google Scholar]
  5. Bartlett, P. L., and M. H.Wegkamp. 2008. “Classification With a Reject Option Using a Hinge Loss.” Journal of Machine Learning Research9: 1823–1840.
    [Google Scholar]
  6. Brown, A. R.2011. Interpretation of Three‐Dimensional Seismic Data. American Association of Petroleum Geologists.
    [Google Scholar]
  7. Bruhn, C. H., J. A. T.Gomes, C.Del Lucchese, and P. R.Johann. 2003. “Campos Basin: Reservoir Characterization and Management ‐ Historical Overview and Future Challenges.” In Offshore Technology Conference, Houston, Texas, May 2003 03OTC. OTC.
    [Google Scholar]
  8. Caers, J.2000. “Direct Sequential Indicator Simulation.” Geostats1: 39–48.
    [Google Scholar]
  9. Cho, Y., R. L.Gibson Jr, and D.Zhu. 2018. “Quasi 3D Transdimensional Markov‐Chain Monte Carlo for Seismic Impedance Inversion and Uncertainty Analysis.” Interpretation6: T613–T624.
    [Google Scholar]
  10. Cho, Y., D.Jeong, and H.Jun. 2020. “Semi‐Auto Horizon Tracking Guided by Strata Histograms Generated With Transdimensional Markov‐Chain Monte Carlo.” Geophysical Prospecting68: 1456–1475.
    [Google Scholar]
  11. Daly, C., S.Quental, and D.Novak. 2010. “A Faster, More Accurate Gaussian Simulation.” In Phe Proceedings of the Geocanada Conference, Calgary, AB, Canada, 10–14. Canadian Society of Exploration Geophysicists.
    [Google Scholar]
  12. Deisenroth, M. P., A. A.Faisal, and C. S.Ong. 2020. Mathematics for Machine Learning. Cambridge University Press.
    [Google Scholar]
  13. V Deutsch, C.2006. “A Sequential Indicator Simulation Program for Categorical Variables With Point and Block Data: BlockSIS.” Computers and Geosciences32: 1669–1681.
    [Google Scholar]
  14. A Dorn, G.1998. “Modern 3‐D Seismic Interpretation.” The Leading Edge17: 1262–1262.
    [Google Scholar]
  15. Dreyer, T., M.Whitaker, J.Dexter, H.Flesche, and E.Larsen. 2005. “From Spit System to Tide‐Dominated Delta: Integrated Reservoir Model of the Upper Jurassic Sognefjord Formation on the Troll West Field.” Geological Society, London, Petroleum Geology Conference Series6: 423–448.
    [Google Scholar]
  16. Duffy, O. B., R. E.Bell, C. A.‐L.Jackson, R. L.Gawthorpe, and P. S.Whipp. 2015. “Fault Growth and Interactions in a Multiphase Rift Fault Network: Horda Platform, Norwegian North Sea.” Journal of Structural Geology80: 99–119.
    [Google Scholar]
  17. Dumoulin, V., J.Shlens, and M.Kudlur. 2016. “A Learned Representation for Artistic Style.” Preprint, arXiv, December 5. https://doi.org/10.48550/arXiv.1610.07629.
  18. Fawad, M., M. J.Rahman, and N. H.Mondol. 2021. “Seismic Reservoir Characterization of Potential CO2 Storage Reservoir Sandstones in Smeaheia Area, Northern North Sea.” Journal of Petroleum Science and Engineering205: 108812.
    [Google Scholar]
  19. Fehmers, G. C., and C. F. W.Höcker. 2003. “Fast Structural Interpretation with Structure‐Oriented Filtering.” Geophysics68: 1286–1293.
    [Google Scholar]
  20. Fischer, H.2011. A History of the Central Limit Theorem: From Classical to Modern Probability Theory. Springer New York.
    [Google Scholar]
  21. Forney, G.1973. “The Viterbi Algorithm.” Proceedings of the IEEE61: 268–278.
    [Google Scholar]
  22. Forte, E., M.Dossi, M.Pipan, and A.Del Ben. 2016. “Automated Phase Attribute‐Based Picking Applied to Reflection Seismics.” Geophysics81: V141–V150.
    [Google Scholar]
  23. Goodfellow, I., J.Pouget‐Abadie, et al. 2014. “Generative Adversarial Nets.” Advances in Neural Information Processing Systems27: 2672–2680.
    [Google Scholar]
  24. Gunderson, K. L., Z.Zhang, B.Payne, S.Cheng, Z.Jiang, and A.Wang. 2022. “Machine Learning Applications to Seismic Structural Interpretation: Philosophy, Progress, Pitfalls, and Potential.” AAPG Bulletin106: 2187–2202.
    [Google Scholar]
  25. Haldorsen, H. H., and E.Damsleth. 1990. “Stochastic Modeling (Includes Associated Papers 21255 and 21299).” Journal of Petroleum Technology42: 404–412.
    [Google Scholar]
  26. Hansen, K.1992. “The Use of Sequential Indicator Simulation to Characterize Geostatistical Uncertainty; Yucca Mountain Site Characterization Project” SAND 91‐0758. Sandia National Laboratories.
  27. Harrigan, E., J.Kroh, W.Sandham, and T.Durrani. 1992. “Seismic Horizon Picking Using an Artificial Neural Network.” In Proceedings of 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP‐92), vol. 3, 105–108. IEEE.
    [Google Scholar]
  28. Holgate, N. E., C. A.‐L.Jackson, G. J.Hampson, and T.Dreyer. 2013. “Sedimentology and Sequence Stratigraphy of the Middle–Upper Jurassic Krossfjord and Fensfjord Formations, Troll Field, Northern North Sea.” Petroleum Geoscience19: 237–258.
    [Google Scholar]
  29. Holgate, N. E., C. A.‐L.Jackson, G. J.Hampson, and T.Dreyer. 2015. “Seismic Stratigraphic Analysis of the Middle Jurassic Krossfjord and Fensfjord Formations, Troll Oil and Gas Field, Northern North Sea.” Marine and Petroleum Geology68: 352–380.
    [Google Scholar]
  30. Hoyes, J., and T.Cheret. 2011. “A Review of ‘Global’ Interpretation Methods for Automated 3D Horizon Picking.” The Leading Edge30: 38–47.
    [Google Scholar]
  31. Isola, P., J.‐Y.Zhu, T.Zhou, and A. A.Efros. 2017. “Image‐to‐Image Translation With Conditional Adversarial Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125–1134. IEEE.
    [Google Scholar]
  32. Jun, H., Y.Cho, and J.Noh. 2019. “Trans‐Dimensional Markov Chain Monte Carlo Inversion of Sound Speed and Temperature: Application to Yellow Sea Multichannel Seismic Data.” Journal of Marine Systems197: 103180.
    [Google Scholar]
  33. Jung, Y., J.‐W.Lee, M. J.Lee, et al. 2024. “ML‐Driven Semi‐Auto Multi‐Scenario Horizon Interpretation With Uncertainty Quantification.” Geophysics90, no. 2: IM47–IM58.
    [Google Scholar]
  34. Kapur, L., L. W.Lake, K.Sepehrnoori, D. C.Herrick, and C. T.Kalkomey. 1998. “Facies Prediction From Core and Log Data Using Artificial Neural Network Technology.” In SPWLA Annual Logging Symposium, SPWLA–1998. SPWLA.
    [Google Scholar]
  35. Lasseter, T. J., J. R.Waggoner, and L. W.Lake. 1986. “Reservoir Heterogeneities and Their Influence on Ultimate Recovery.” In Reservoir Characterization, edited by L. W.Lake and H. B.Carroll, 545–559. Academic Press.
    [Google Scholar]
  36. Lee, J., M. J.Lee, H.Park, H.Jun, and Y.Cho. 2024. “U‐Net++ Based Subshallow Gas‐Scattered Image Conditioning: Small‐Scale Case Study of Seismic Data Acquired in the Korean East Sea.” IEEE Transactions on Geoscience and Remote Sensing62: 1–13.
    [Google Scholar]
  37. Lee, J.‐W., M.Je Lee, D.‐J.Min, and Y.Cho. 2025. “Reviving Legacy Seismic Data via Machine Learning Technique‐Part 1: Expanding 3‐D Seismic Survey Coverage With Gated Convolution GAN.” IEEE Transactions on Geoscience and Remote Sensing63: 1–18.
    [Google Scholar]
  38. Leggett, M., W. A.Sandham, and T. S.Durrani. 1994. “3D Seismic Horizon Tracking Using an Artificial Neural Network.” Paper presented at the 56th EAGE Meeting. European Association of Geoscientists and Engineers.
  39. Li, H., P.Xiong, J.An, and L.Wang. 2018. “Pyramid Attention Network for Semantic Segmentation.” Preprint, ArXiv, May 25. https://doi.org/10.48550/arXiv.1805.10180.
  40. Liu, Y., P. G.Rigsby, R.Sinha, S.Peterson, J.Thomas, and G.Zimmerman. 2011. “Geologic Modeling and Uncertainty Analysis of a Gulf of Mexico Reservoir.” In Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World, 77–88. American Association of Petroleum Geologists.
    [Google Scholar]
  41. Lucia, F. J.1999. Carbonate Reservoir Characterization. Springer Berlin Heidelberg.
    [Google Scholar]
  42. Ma, Y. Z.2011. “Uncertainty Analysis in Reservoir Characterization and Management: How Much Should We Know About What We Don't Know?” In Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World, 1–16. American Association of Petroleum Geologists.
    [Google Scholar]
  43. Mao, X., Q.Li, H.Xie, R. Y.Lau, Z.Wang, and S.Paul Smolley. 2017. “Least Squares Generative Adversarial Networks.” in Proceedings of the IEEE International Conference on Computer Vision, 2794–2802. IEEE.
    [Google Scholar]
  44. Massonnat, G. J., F. G.Alabet, and C. B.Guidicelli. 1993. “Anguille Marine, a Deep Sea Fan Reservoir Offshore Gabon: From Geology to Stochastic Modeling.” In SEG Technical Program Expanded Abstracts 1993, 345–345. Society of Exploration Geophysicists.
    [Google Scholar]
  45. Moore, W. R., Y. Z.Ma, J.Urdea, and T.Bratton. 2011. “Uncertainty Analysis in Well‐Log and Petrophysical Interpretations.” In Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World, 17–28. American Association of Petroleum Geologists.
    [Google Scholar]
  46. Nowozin, S., B.Cseke, and R.Tomioka. 2016. “f‐GAN: Training Generative Neural Samplers Using Variational Divergence Minimization.” Advances in Neural Information Processing Systems29: 271–279.
    [Google Scholar]
  47. Oluwadare, O. A., O. T.Osunrinde, S. J.Abe, and B. T.Ojo. 2017. “3‐D Geostatistical Model and Volumetric Estimation of ‘Del’ Field, Niger Delta.” Journal of Geology and Geophysics6: 6291.
    [Google Scholar]
  48. Oliver, M. A., and R.Webster. 2015. Basic Steps in Geostatistics: The Variogram and Kriging. Springer International Publishing.
    [Google Scholar]
  49. Orellana, N., J.Cavero, I.Yemez, V.Singh, and J.Sotomayor de la Serna. 2014. “Influence of Variograms in 3D Reservoir‐Modeling Outcomes: An Example.” The Leading Edge33: 890–902.
    [Google Scholar]
  50. Pardo‐Iguzquiza, E., and M.Chica‐Olmo. 1993. “The Fourier Integral Method: An Efficient Spectral Method for Simulation of Random Fields.” Mathematical Geology25: 177–217.
    [Google Scholar]
  51. Patrick, D. Doherty, G. S.Soreghan, and John P.Castagna. 2002. “Outcrop‐Based Reservoir Characterization: A Composite Phylloid‐Algal Mound, Western Orogrande Basin (New Mexico).” AAPG Bulletin86, no. 5: 779–795.
    [Google Scholar]
  52. Peters, B., J.Granek, and E.Haber. 2019. “Multiresolution Neural Networks for Tracking Seismic Horizons From Few Training Images.” Interpretation7: SE201–SE213.
    [Google Scholar]
  53. Rahman, M. J., M.Fawad, and N. H.Mondol. 2020. “Organic‐Rich Shale Caprock Properties of Potential CO2 Storage Sites in the Northern North Sea, Offshore Norway.” Marine and Petroleum Geology122: 104665.
    [Google Scholar]
  54. Ronneberger, O., P.Fischer, and T.Brox. 2015. “U‐Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2015: Proceedings of the 18th International Conference, Munich, Germany, October 5‐9, 2015, Part III 18, 234–241. Springer.
    [Google Scholar]
  55. Safaei‐Farouji, M., H.Vo Thanh, D.Sheini Dashtgoli, et al. 2022. “Application of Robust Intelligent Schemes for Accurate Modelling Interfacial Tension of CO2 Brine Systems: Implications for Structural CO2 Trapping.” Fuel319: 123821.
    [Google Scholar]
  56. Sandsdalen, C., M.Barbieri, K.Tyler, and J.Aasen. 1996. “Applied Uncertainty Analysis Using Stochastic Modelling.” In The European 3‐D Reservoir Modelling Conference 96E3DR. SPE.
    [Google Scholar]
  57. Skurtveit, E., J. C.Choi, J.Osmond, M.Mulrooney, and A.Braathen. 2019. “3D Fault Integrity Screening for Smeaheia CO2 Injection Site.” SSRN Electronic Journal.
  58. Slatt, R. M.2006. Stratigraphic Reservoir Characterization For Petroleum Geologists, Geophysicists, and Engineers. Elsevier.
    [Google Scholar]
  59. Statoil . 2016. “Subsurface Evaluation of Smeaheia as part of 2016 Feasibility Study on CO2 Storage in the Norwegian Continental Shelf.” Technical Report. Statoil.
  60. Stewart, D., M.Schwander, and L.Bolle. 1995. Jurassic Depositional Systems of the Horda Platform, Norwegian North Sea: Practical Consequences of Applying Sequence Stratigraphic Models, 291–323. Elsevier.
    [Google Scholar]
  61. van den Oord, A., N.Kalchbrenner, L.Espeholt, O.Vinyals, A.Graves, etal. 2016. “Conditional Image Generation with pixelCNN Decoders.” Advances in Neural Information Processing Systems29: 4797–4805.
    [Google Scholar]
  62. Vo Thanh, H., and K.‐K.Lee. 2022. “Application of Machine Learning to Predict CO2 Trapping Performance in Deep Saline Aquifers.” Energy239: 122457.
    [Google Scholar]
  63. Vo Thanh, H., Y.Sugai, R.Nguele, and K.Sasaki. 2019. “Integrated Workflow in 3D Geological Model Construction for Evaluation of CO2 Storage Capacity of a Fractured Basement Reservoir in Cuu Long Basin, Vietnam.” International Journal of Greenhouse Gas Control90: 102826.
    [Google Scholar]
  64. Vo Thanh, H., Q.Yasin, W. J.Al‐Mudhafar, and K.‐K.Lee. 2022. “Knowledge‐Based Machine Learning Techniques for Accurate Prediction of CO2 Storage Performance in Underground Saline Aquifers.” Applied Energy314: 118985.
    [Google Scholar]
  65. Whipp, P. S., C. A.Jackson, R. L.Gawthorpe, T.Dreyer, and D.Quinn. 2014. “Normal Fault Array Evolution Above a Reactivated Rift Fabric; A Subsurface Example from the Northern Horda Platform, Norwegian North Sea.” Basin Research26: 523–549.
    [Google Scholar]
  66. Woo, S., J.Park, J.‐Y.Lee, and I. S.Kweon. 2018. “CBAM: Convolutional Block Attention Module.” In Computer Vision – ECCV 2018, edited by V.Ferrari, M.Hebert, C.Sminchisescu, and Y.Weiss, 3–19. Springer International Publishing.
    [Google Scholar]
  67. Wu, H., B.Zhang, T.Lin, D.Cao, and Y.Lou. 2019. “Semiautomated Seismic Horizon Interpretation Using The Encoder‐Decoder Convolutional Neural Network.” Geophysics84: B403–B417.
    [Google Scholar]
  68. Yan‐bin, B., M.Cheng‐dou, S.Hong‐ping, and Z.Yu‐zhe. 2003. “Application of Variation Function in Description of Plane Heterogeneity of Reservoir.” Xinjiang Petroleum Geology24: 251.
    [Google Scholar]
  69. Yang, L., and S. Z.Sun. 2020. “Seismic Horizon Tracking Using A Deep Convolutional Neural Network.” Journal of Petroleum Science and Engineering187: 106709.
    [Google Scholar]
  70. Zagayevskiy, Y., and C. V.Deutsch. 2014. “A Methodology for Sensitivity Analysis Based on Regression: Applications to Handle Uncertainty in Natural Resources Characterization.” Natural Resources Research24: 239–274.
    [Google Scholar]
  71. Zhang, K., N.Lin, D.Zhang, J.Zhang, J.Yang, and G.Tian. 2022. “Automatic Tracking for Seismic Horizons Using Convolution Feature Analysis and Optimization Algorithm.” Journal of Petroleum Science and Engineering208: 109441.
    [Google Scholar]
  72. Zhang, Y., and S.Srinivasan. 2003. “Markov Chain Monte Carlo for Reservoir Uncertainty Assessment.” In The Canadian International Petroleum Conference, 03CIPC. Petroleum Society of Canada.
    [Google Scholar]
  73. Zhu, J.‐Y., T.Park, P.Isola, and A. A.Efros. 2017. “Unpaired Image‐to‐Image Translation Using Cycle‐Consistent Adversarial Networks.” In Proceedings of the IEEE International Conference on Computer Vision, 2223–2232. IEEE.
    [Google Scholar]
/content/journals/10.1111/1365-2478.70119
Loading
/content/journals/10.1111/1365-2478.70119
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): geomodelling; geostatistics; horizon interpretation; uncertainty; volumetric analysis

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error