Abstract
Seismic stochastic inversion method has received much attention because of its considerable advantage of having higher vertical resolution than deterministic inversions. However, due to the lack of cross-well data, the inversion results typically exhibit poor lateral continuity. Furthermore, the inversion efficiency is low, and the inversion result is highly random. Therefore, this study proposes a geostatistical seismic inversion method constrained by a seismic waveform. The correlation coefficient of seismic data is used to measure the similarity of the seismic waveforms, replacing the traditional variogram for sequential Gaussian simulation. Under the Bayesian framework, the Monte Carlo-Markov Chain (MCMC) algorithm is combined with the constraints of seismic data to randomly perturb and optimize the simulation results for obtaining the optimized parameter inversion results. The model data tests show that the initial model based on seismic waveform constraints can accurately describe the spatial structure of the subsurface reservoir. In addition, perturbing and optimizing initial model can increase the convergence speed of the Markov chain and effectively improve the accuracy of the inversion results. In this paper, the proposed geostatistical inversion method is applied to the actual seismic data of an oil field. Under the constraints of the stochastic simulation process and objective function, the geological information contained in the seismic waveforms is fully mined, and a theoretical foundation is provided for realizing the multidata joint-constrained seismic inversion.
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References
Abdel-Fattah, M. I., Pigott, J. D., and El-Sadek, M. S., 2020, Integrated seismic attributes and stochastic inversion for reservoir characterization: insights from Wadi field (NE Abu-Gharadig Basin, Egypt): Journal of African Earth Sciences, 161, 103661.1–103661.14.
Azevedo, L., Nunes, R., Soares, A., et al., 2015, Geostatistical seismic AVO inversion directly for facies-real case application: 77th Annual international Meeting, EAGE, Expanded Abstracts, 1–5.
Buland, A., and Omre, H., 2003, Bayesian linearized AVO inversion: Geophysics, 68(1), 185–198.
Chen, X., Zhao, M., Cui, J. B., et al., 2022, Seismic Motion Inversion Based on Geological Conditioning and Its Application in Thin Reservoir Prediction, Middle East: Annual international Meeting, SPE, Expanded Abstracts, 211631.
Chen, Y. H., Bi, J. J., Qiu, X. B., et al., 2020, A method of seismic meme inversion and its application: Petroleum Exploration and Development, 47(6), 1235–1245.
Contreras, A., Torres-Verdin, C., Kvien, K., et al., 2005, AVA stochastic inversion of pre-stack seismic data and well logs for 3D reservoir modeling: 67th Annual international Meeting, EAGE, Expanded Abstracts, cp-1-00310.
Debeye, H., Sabbah, E., and Van der Made, P. M., 1996, Stochastic inversion: EAGE Winter Symposium-Reservoir Geophysics, the Road Ahead, European Association of Geoscientists & Engineers, p 22–98.
Dubrule, O., Thibaut, M., Lamy, P., et al., 1998, Geostatistical reservoir characterization constrained by 3D seismic data: Petroleum Geoscience, 4(2), 121–128.
Durrani, M. ZA., Talib, M., Ali, A., et al., 2021, Characterization of carbonate reservoir using poststack global geostatistical acoustic inversion approach: A case study from a mature gas field, onshore Pakistan: Journal of Applied Geophysics, 188(1), 104313.
Escobar, I., Williamson, P., Cherrett, A., et al., 2006, Fast geostatistical stochastic inversion in a stratigraphic grid: 76th Annual international Meeting, SEG, Expanded Abstracts, 2067–2071.
Francis, A. M., 2006, Understanding stochastic inversion: Part 1: First Break, 24(11), 69–77.
Francis, A. M., 2006, Understanding stochastic inversion: part 2: First Break, 24(12), 79–84.
Gao, J., Bi, J. J., Zhao, H. S., et al., 2017, Seismic waveform inversion technology and application of thinner reservoir prediction: Progress in Geophysics, 32(1), 142–145.
Haas, A., and Dubrule, O., 1994, Geostatistical inversion-a sequential method of stochastic reservoir modelling constrained by seismic data: First break, 12(11), 561–569.
Hansen, T. M., Journel, A. G., Tarantola, A., et al., 2006, Linear inverse Gaussian theory and geostatistics: Geophysics, 71(6), R101–R111.
Hu, W., Qi, P., Yang, J. F., et al., 2018, Application of seismic motion inversion in identification of tight thin super deep reservoirs: Progress in Geophysics, 33(2), 620–624.
Kane, J., Rodi, W., Herrmann, F., et al., 1999, Geostatistical seismic inversion using well log constraints: 69th Annual international Meeting, SEG, Expanded Abstracts, 1504–1507.
Li, B. J., Bi, J. J., Wei, S. D., et al., 2017, Application of waveform inversion technique in prediction of beach bar sandstone reservoir: 87th Annual international Meeting, SEG, Expanded Abstracts, 686–689.
Pereira, P., Azevedo, L., Nunes, R., et al., 2019, The impact of a priori elastic models into iterative geostatistical seismic inversion: Journal of Applied Geophysics, 170, 103850–103850.
Sams, M., and Saussus, D., 2008, Comparison of uncertainty estimates from deterministic and geostatistical inversion:78th Annual international Meeting, SEG, Expanded Abstracts, 1486–1490.
Sams, M., and Saussus, D, 2010, Comparison of lithology and net pay uncertainty between deterministic and geostatistical inversion workflows: First break, 28(2), 35–44.
Sams, M. S., Atkins, D., Said, N., et al., 1999, Stochastic inversion for high resolution reservoir characterisation in the Central Sumatra Basin: Annual international Meeting, SPE, Expanded Abstracts, 57260–57260.
Song, C. Y., Liu, Z. N., She, B., et al., 2019, Seismic waveform classification via a new similarity measure: 89th Annual international Meeting, SEG, Expanded Abstracts, 2438–2442.
Wang, B. L., Yin, X. Y., Ding, L. X., et al., 2015, Study of fast stochastic inversion based on FFT-MA spectrum simulation: Chinese Journal of Geophysics, 58(2), 664–673.
Wang, J. B., Li, Z. J., Chen, C., et al., 2022, Predicting gas content in coalbed methane reservoirs using seismic waveform indication inversion: a case study from the Upper Carboniferous Benxi Formation, eastern Ordos Basin, China: Acta Geophys, 70(2), 623–638.
Wang, J. H., and Zhao, W., 2010, Research on geostatistical modeling method constrained by seismic data: Offshore Oil (in chinese) 30(4), 46–49.
Wang, X. D., Yin, X. Y., Jin, H., et al., 2018, Pre-stack seismic stochastic inversion and application on real data: Progress in Geophysics, 33(6), 2471–2476.
Yang, X., and Mrinal K. S., 2016, Stochastic seismic inversion using greedy annealed importance sampling: Journal of Geophysics and Engineering, 13(5), 786–804.
Yin, X. Y., Sun, R. Y., Wang, B. L., et al., 2014, Simultaneous inversion of petrophysical parameters based on geostatistical a priori information: Applied Geophysics, 11(3), 311–320.
Zhang, S. H., Xu, Y., and Abu-Ali, M., 2016, Characterizing stratigraphic traps using improved waveform classification seismic facies analysis: an example from central Saudi Arabia: First Break 34(12), 77–84.
Zhao, Y. F., Sun, Z. Y., and Chen, J., 2010, Analysis and comparison in arithmetic for Kriging interpolation and sequential Gaussian conditional simulation: Journal of Geo-Information Science, 12(6), 767–776.
Zhou, S. S., Yin, X. Y., and Pei, S., 2021, Monte Carlo-Markov Chain stochastic inversion constrained by seismic waveform: Oil Geophysical Prospecting, 56(3), 543–554+592+413.
Acknowledgments
This work was kindly supported by the National Natural Science Foundation of China [Grant Nos. 42174146; 42074136;42174144], and Innovation Fund Project for Graduate Students of China University of Petroleum (East China) [Grant No. 23CX04015A]. Thanks to Geosoftware for software support. The authors would like to thank the editors and anonymous reviewers for their valuable comments.
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Ni Xue-Bin, a master’s student, is currently studying at the School of Earth Science and Technology, China University of Petroleum (East China). Her main interests are reservoir geophysics, seismic data processing and interpretation methods, and prestack reservoir prediction. Unit: School of Earth Science and Technology, China University of Petroleum (East China); Postcode: 266580; Tel: 17806281886; E-mail: Z21010085@s.upc.edu.cn
Zhang Jia-Jia is an associate professor at the China University of Petroleum (East China). He graduated from the Ocean University of China in 2007 with a bachelor’s degree in Earth Information Science and Technology, with a master’s degree in Earth Exploration and Information Technology from the Ocean University of China in 2010, and in 2013 with a doctorate degree in Earth Exploration and Information Technology from China Petroleum Exploration and Development Research Institute. His main research interests include seismic rock physics and prestack reservoir prediction. Unit: School of Earth Science and Technology, China University of Petroleum (East China); Postcode: 266580; Tel: 15205326882; E-mail: zhangjj@upc.edu.cn
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Ni, XB., Zhang, JJ., Chen, K. et al. Geostatistical inversion method based on seismic waveform similarity. Appl. Geophys. 20, 186–197 (2023). https://doi.org/10.1007/s11770-023-1052-9
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DOI: https://doi.org/10.1007/s11770-023-1052-9