Prediction of Fluid–Brine Event Zones by Artificial Intelligence Methods Based on New Generation RTH Seismic Attributes and Drilling Data at the Kovykta Gas Condensate Field
A new method for predicting lithofacies, gas, fluid and brine zones, and zones with abnormally high reservoir pressure, as well as the petrophysical properties of rocks using artificial intelligence methods, based on a family of new seismic attributes of the RTH (reverse time holography) method and...
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| Published in: | Doklady earth sciences Vol. 514; no. 1; pp. 105 - 113 |
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| Format: | Journal Article |
| Language: | English |
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Pleiades Publishing
2024
Springer Nature B.V |
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| Abstract | A new method for predicting lithofacies, gas, fluid and brine zones, and zones with abnormally high reservoir pressure, as well as the petrophysical properties of rocks using artificial intelligence methods, based on a family of new seismic attributes of the RTH (reverse time holography) method and well drilling data is proposed. The main difference between RTH attributes and conventional ones obtained by migration transformation is their voxel nature and hyperattribution. This turned out to be a key advantage of the new approach to solving problems of geological prediction by artificial intelligence methods. The results of applying a new method for processing and interpreting modern 3D seismic data, as well as geological prediction based on it for the area of intense brine occurrence at the Kovykta gas condensate field, are presented. |
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| AbstractList | A new method for predicting lithofacies, gas, fluid and brine zones, and zones with abnormally high reservoir pressure, as well as the petrophysical properties of rocks using artificial intelligence methods, based on a family of new seismic attributes of the RTH (reverse time holography) method and well drilling data is proposed. The main difference between RTH attributes and conventional ones obtained by migration transformation is their voxel nature and hyperattribution. This turned out to be a key advantage of the new approach to solving problems of geological prediction by artificial intelligence methods. The results of applying a new method for processing and interpreting modern 3D seismic data, as well as geological prediction based on it for the area of intense brine occurrence at the Kovykta gas condensate field, are presented. |
| Author | Erokhin, G. N. Smirnov, A. S. Ryabykh, S. A. Bugaev, A. S. |
| Author_xml | – sequence: 1 givenname: A. S. surname: Bugaev fullname: Bugaev, A. S. organization: Kotelnikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences – sequence: 2 givenname: G. N. surname: Erokhin fullname: Erokhin, G. N. email: Gerokhin@kantiana.ru organization: Kant Baltic Federal University – sequence: 3 givenname: S. A. surname: Ryabykh fullname: Ryabykh, S. A. organization: OOO GIRS-M – sequence: 4 givenname: A. S. surname: Smirnov fullname: Smirnov, A. S. organization: Gazprom VNIIGAZ LLC |
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| Copyright | Pleiades Publishing, Ltd. 2024. ISSN 1028-334X, Doklady Earth Sciences, 2024, Vol. 514, Part 1, pp. 105–113. © Pleiades Publishing, Ltd., 2024. ISSN 1028-334X, Doklady Earth Sciences, 2024. © Pleiades Publishing, Ltd., 2024. |
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| References | VakhromeevA. G.SverkunovS. A.SmirnovA. S.GorlovI. V.Stroit. Neft. GazovSkvazhin Sushe More, No.20195111810.30713/0130-3872-2019-5-11-18 AminzadehF.TemizelC.HajizadehY.Artificial Intelligence and Data Analytics for Energy Exploration and Production202210.1002/9781119879893 A. S. Smirnov, A. G. Vakhromeev, G. N. Erokhin, and A. G. Dmitriev, Geofizika, No. 2, 93–101 (2023). https://doi.org/10.34926/geo.2023.18.86.011 LuanxiaoZ.CaifengZ.YuanyuanC.WenlongS.WangY.ChenH.GengJ.Geophysics202186M151M16510.1190/geo2020-0521.1 T. A. Dickens and G. A. Winbow, in SEG Techn. Progr. Expand. Abstr. (2011). https://doi.org/10.1190/1.3627841. PopoviciA.TanushevN.HardestyS.SEG Techn. Progr. Expand. Abstr.2016 VakhromeevA. G.KhokhlovG. A.Features of Hole Drilling and Finishing in Eastern Siberia and Yakutia1988Novosibirsk, IrkutskSiberian Res. Inst. Geol., Geophys., Miner. Raw Mater.[in Russian] AgafonovV. M.BugaevA. S.ErokhinG. N.RonzhinA. L.Geofizika.202267783 I. V. Buddo, A. S. Smirnov, N. V. Misiurkeeva, I. A. Shelohov, Y. A. Agafonov, M. A. Lushev, S. A. Korotkov, and E. Ju. Trjasin, in Proc. 8th Int. Conf. & Exhibition Innovations in Geosciences—Time for Breakthrough (St. Petersburg, Apr. 9–12, 2018). A. N. Kremlev, G. N. Erokhin, L. E. Starikov, and S. V. Rodin, in Proc. 73th EAGE Conf. & Exhibition (Vienna, 2011), Abstr. B024. HampsonD. P.SchuelkeJ. S.QuierinJ. A.Geophysics20016622023610.1190/1.1444899 S. Chopra and J. P. Castagna, AVO (Soc. Explor. Geophys., 2014). A. Tarantola, Geophysics 49 (8) (1984). VirieuxJ.OpertoS.Geophysics200974WCC1WCC2610.1190/1.3238367 KorenZ.RavveI.Geophysics201176S1S1310.1190/1.3511352 PriezzhevI. I.VeekenP. C. H.EgorovS. V.StreckeU.Leading Edge20193894995810.1190/tle38120949 BaysalE.KosloffD. D.SherwoodJ. W. C.Geophysics1983481514152410.1190/1.1441434 G. Erokhin, in SEG Techn. Progr. Expand. Abstr. (2019), pp. 4107–4111. https://doi.org/10.1190/segam2019-3201622.1. A. S. Smirnov, V. V. Kas’yanov, A. G. Vakhromeev, et al., RF Patent 2690089 IPC G01V 1/00 (2006.01), G01V 1/28 (2006.01), G01V 1/30 (2006.01), Byull. Izobret., No. 16 (2019). G. Erokhin, in Proc. 83rd EAGE Annu. Conf. & Exhibition (Madrid, June 2022), Vol. 2022, pp. 1‒5. https://doi.org/10.3997/2214-4609.202210094. 4299_CR8 J. Virieux (4299_CR15) 2009; 74 A. Popovici (4299_CR16) 2016 F. Aminzadeh (4299_CR18) 2022 4299_CR3 4299_CR4 4299_CR10 4299_CR7 Z. Koren (4299_CR11) 2011; 76 4299_CR14 4299_CR13 4299_CR12 A. G. Vakhromeev (4299_CR1) 1988 I. I. Priezzhev (4299_CR2) 2019; 38 A. G. Vakhromeev (4299_CR20) 2019; 5 V. M. Agafonov (4299_CR17) 2022; 6 Z. Luanxiao (4299_CR6) 2021; 86 D. P. Hampson (4299_CR5) 2001; 66 E. Baysal (4299_CR9) 1983; 48 4299_CR19 |
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| SubjectTerms | Artificial intelligence Brines Condensates Drilling Earth and Environmental Science Earth Sciences Geology Geophysics Holography Lithofacies Predictions Rock properties Seismic data Seismological data Well drilling |
| Title | Prediction of Fluid–Brine Event Zones by Artificial Intelligence Methods Based on New Generation RTH Seismic Attributes and Drilling Data at the Kovykta Gas Condensate Field |
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