Abstract
This research introduces a method for seismic-well tie using a modified Dynamic Time Warping, DTW algorithm with fuzzy features. The Seismic-Fuzzy DTW technique aligns synthetic seismograms to seismic traces by considering waveform similarity and geological features. It uses acoustic impedance models and membership results from fuzzy model-based inversion. Traditional seismic-well tie methods frequently prioritize amplitude matching above geological consistency. The proposed approach rethinks the well tie target by stressing the high correlation of fuzzy acoustic impedance features. The results show improvements over the traditional DTW-based technique. The result’s correctness, however, depends on the accuracy of the fuzzy seismic inversion data. It is proposed that more research be conducted into potential mismatches, noise effects, and complex geological structures. The algorithm’s effectiveness could be improved by incorporating more data types and optimizing its behavior under different geological settings. Overall, this unique approach yields promising results by combining seismic and well data to improve seismic interpretation outcomes.













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The data used in this study are from the OpendTect open datasets and can be provided upon request.
References
AL-DOSSARY S, MARFURT KJ (2006) 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics 71:P41–P51
AMIRI A, BAGHERI M, RIAHI MA (2020) Simplified automatic seismic to well tying using smooth dynamic time warping technique in R. Iran J Oil Gas Sci Technol 9:85–92
Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd international conference on knowledge discovery and data mining, pp 359–370
BEZDEK JC, EHRLICH R, FULL W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203
BIANCO E (2016) Tutorial: wavelet estimation for well ties. Lead Edge 35:541–543
Buland A, Omre H (2003) Bayesian wavelet estimation from seismic and well data. Geophysics 68:2000–2009
De Macedo IA, Da Silva CB, De Figueiredo J, Omoboya B (2017) Comparison between deterministic and statistical wavelet estimation methods through predictive deconvolution: seismic to well tie example from the North Sea. J Appl Geophys 136:298–314
Di H, Abubakar A (2023) Automating seismic-well tie via self‐supervised learning. Geophys Prospect 71:698–712
Donselaar ME, Groenenberg RM, Gilding DT (2015) Reservoir geology and geothermal potential of the Delft Sandstone member in the West Netherlands Basin. In: Proceedings world geothermal congress, pp 19–25
Dvorkin J, Gutierrez MA, Grana D (2014) Seismic reflections of rock properties. Cambridge University Press
GELPI GR, PÉREZ DO, VELIS DR (2020) Automatic well tying and wavelet phase estimation with no waveform stretching or squeezing. Geophysics 85:D83–D91
Herrera RH, Van Der Baan M (2012) Automated seismic-to-well ties using dynamic time warping. Vision, Canada, GeoConvention
Herrera RH, Van Der Baan M (2014) A semiautomatic method to tie well logs to seismic data. Geophysics 79:V47–V54
Herrera RH, Fomel S, Van Der Baan M (2014) Automatic approaches for seismic to well tying. Interpretation 2:SD9–SD17
Jahanjooy S, Hashemi H, Bagheri M (2024a) Fuzzy seismic inversion: a case study on channel features in johnson formation of browse basin. Australia. J Earth Space Phys 49
Jahanjooy S, Hashemi H, Bagheri M (2024b) Multi-dimensional, multi-constraint seismic inversion of acoustic impedance using fuzzy clustering concepts. Nonlinear Process Geophys Discuss 2024:1–25
Kieu DT, Kepic A (2015) Incorporating prior information into seismic impedance inversion using fuzzy clustering technique. In: 85th Annual International Meeting, SEG, expanded abstracts, pp 3451–3455
Liang J (2022) Confusion matrix: machine learning. POGIL Activity Clearinghouse 3(4)
Liu D, Wang X, Yang X, Mao H, Sacchi MD, Chen W (2022) Accelerating seismic scattered noise attenuation in offset-vector tile domain: application of deep learning. Geophysics 87:V505–V519
Maag E, Li Y (2018) Discrete-valued gravity inversion using the guided fuzzy c-means clustering technique. Geophysics 83:G59–G77
Moosavi N, Bagheri M, Nabi-Bidhendi M, Heidari R (2023) Porosity prediction using Fuzzy SVR and FCM SVR from well logs of an oil field in south of Iran. Acta Geophysica 71:769–782
Müller M (2007) Dynamic time warping. Information retrieval for music and motion, pp 69–84
Munoz A, Hale D (2012) Automatically tying well logs to seismic data. Tech. rep., CWP-725, Colorado School of Mines, Golden, CO
Nivlet P, Smith R, Albinhassan N (2020) Automated well-to-seismic tie using deep neural networks. SEG Tech Program Expand Abstr 2156–2160
Ou Q, Wang S, Yong R, Xiu Z, Han W, Zhang Z (2023) A method for clustering rock discontinuities with multiple properties based on an improved netting algorithm. Geomech Geophys Geo-Energy Geo-Resour 9:23
Paasche H, Tronicke J, Holliger K, Green AG, Maurer H (2006) Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy c-means cluster analyses. Geophysics 71:H33–H44
Saadat M, Hashemi H, Nabi-Bidhendi M (2024) Generalizable data driven full waveform inversion for complex structures and severe topographies. Petroleum 21(I6):4025–4033
Schimmel M (1999) Phase cross-correlations: design, comparisons, and applications. Bull Seismol Soc Am 89:1366–1378
Senin P (2008) Dynamic time warping algorithm review. Information and computer science Department University of Hawaii Manoa, vol 855. Honolulu, HI, USA, pp 1–23
Sun J, Li Y (2016) Joint inversion of multiple geophysical data using guided fuzzy c-means clustering. Geophysics 81:ID37–ID57
Ting KM (2010) Confusion Matrix. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer US, Boston, MA
Tylor-Jones T, Azevedo L (2022) A practical guide to seismic reservoir characterization. Advances in oil and gas exploration & production. Springer, Berlin
Van Der Baan M (2008) Time-varying wavelet estimation and deconvolution by kurtosis maximization. Geophysics 73:V11–V18
White R, Simm R (2003) Tutorial: good practice in a well-tied. First Break 21(10):75–83
ZHAN W, CHEN Y, LI LIUQ, SACCHI J, ZHUANG MD, LIU QH (2024) Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints. J Appl Geophys 224:105362
Zhang B, Yang Y, Pan Y, Wu H, Cao D (2020) Seismic well tie by aligning impedance log with inverted impedance from seismic data. Interpretation 8:T917–T925
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S.J. developed the methodology, conducted the practical work, and wrote the initial manuscript. D.B. assisted with the practical work and contributed to writing the manuscript. H.H. and M.B. supervised the overall project and reviewed the manuscript.
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Communicated by: Hassan Babaie
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Jahanjooy, S., Hashemi, H., Bagheri, M. et al. Seismic-well tie using fuzzy properties of acoustic impedance in the dynamic time warping. Earth Sci Inform 18, 205 (2025). https://doi.org/10.1007/s12145-025-01697-0
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DOI: https://doi.org/10.1007/s12145-025-01697-0