Abstract
It is challenging to realize the gas saturation (GS) estimation via the integration of seismic data and more complementary data (e.g., elastic attributes) in a traditional physical framework. Machine learning, especially multi-task learning (MTL), provides an alternative way for the fuse of multiple information and simultaneous inversion of two or multiple reservoir parameters without model-driven limitations and interactive operators. To improve the estimation accuracy of GS, we propose the prestack simultaneous inversion of P-wave impedance (PI) and GS using the multi-task residual network (MT-ResNet). The designed MT-ResNet consists of two task-related subnets. The first subnet establishes the nonlinear links among low-frequency PI, prestack seismic data, and well-log derived PI. Furthermore, seismic data and the inverted PI via the first subnet are jointly entered into the second subnet and evolved into the well-log interpreted GS. A model based on measured petrophysical parameters associated with the field deep tight dolomite reservoir is used to test the proposed method. Tests on the synthetic data example and the field example demonstrate that the MT-ResNet can simultaneously estimate PI and GS models with the highest reliability, in comparison with single-task residual network (ST-ResNet) and the conventional seismic inversion and rock-physics equations-based method. And the MT-ResNet inverted PI can be utilized as complementary information for improving the prediction accuracy of MT-ResNet inverted GS. Our proposed MT-ResNet has the potential to guide the design of the MTL-based multiple reservoir parameters prediction and practical application.
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References
Ahmad M, Haghighi M (2013) Water saturation evaluation of Murteree and Roseneath shale gas reservoirs, Cooper Basin, Australia using wire-line logs, focused ion beam milling and scanning electron microscopy. SPE Unconv Resour Conf Exhib. https://doi.org/10.2118/167080-MS
Aki K, Richards PG (2002) Quantitative seismology. University Science Books
Aleardi M, Ciabarri F, Gukov T (2018) A two-step inversion approach for seismic-reservoir characterization and a comparison with a single-loop Markov-chain Monte Carlo algorithm. Geophysics 83(3):R227–R244. https://doi.org/10.1190/geo2017-0387.1
Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146:54–62. https://doi.org/10.2118/942054-G
Bosch M, Cara L, Rodrigues J, Navarro A, Díaz M (2007) A Monte Carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes. Geophysics 72(6):O29–O39. https://doi.org/10.1190/1.2783766
Bosch M, Mukerji T, Gonzalez EF (2010) Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review. Geophysics 75(5):75A165-75A176. https://doi.org/10.1190/1.3478209
Bürkle PY, Azevedo L, Vellasco L (2023) Deep physics-aware stochastic seismic inversion. Geophysics 88(1):R11–R24. https://doi.org/10.1190/geo2021-0551.1
Caruana R (1997) Multitask Learning. Mach Learn 28:41–75. https://doi.org/10.1023/A:1007379606734
Caruana R (1993) Multitask learning: a knowledge-based source of inductive bias. In: International conference on machine learning, 41–48. https://doi.org/10.1016/B978-1-55860-307-3.50012-5
Chen Y, Yu J, Zhao YT, Chen JY, Du XS (2022) Task’s choice: pruning based feature sharing (PBFS) for multi-task learning. Entropy 24(3):432. https://doi.org/10.3390/e24030432
Connolly P (1999) Elastic impedance. Lead Edge 18(4):438–452. https://doi.org/10.1190/1.1438307
Duong L, Cohn T, Bird S, Cook P (2015) Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, pp 845–850. https://doi.org/10.3115/v1/P15-2139
Eidsvik J, Avseth P, Omre H, Mukerji T, Mavko G (2004) Stochastic reservoir characterization using prestack seismic data. Geophysics 69(4):978–993. https://doi.org/10.1190/1.1778241
Figueiredo LPD, Grana D, Bordignon FL, Santos M, Roisenberg M, B. Rodrigues B, (2018) Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties. Geophysics 83(5):M49–M61. https://doi.org/10.1190/geo2017-0463.1
Gao JH, Song ZH, Gui JY, Yuan SY (2020) Gas-bearing prediction using transfer learning and CNNs: an application to a deep tight dolomite reservoir. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2020.3035568
Gao Y, Zhang JL, Li H, Li GF (2022) Incorporating structural constraint into the machine learning high-resolution seismic reconstruction. IEEE Trans Geosci Remote Sens 60:1–12. https://doi.org/10.1109/TGRS.2022.3157064
Goodway B, Chen TW, Downton J (1997) Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters; “λρ”, “μρ”, & “λ/μ fluid stack”, from P and S inversions. In: SEG technical program expanded abstracts, pp 183–186. https://doi.org/10.1190/1.1885795
Hampson DP, Russell BH, Bankhead B (2005) Simultaneous inversion of pre-stack seismic data. In: SEG Technical Program Expanded Abstracts, pp 1633–1637. https://doi.org/10.1190/1.2148008
He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning, pp 448–456. https://arxiv.org/abs/1502.03167v3
Kingma DP, Ba JL (2014) Adam: a method for stochastic optimization. 3rd International conference for learning representation. https://doi.org/10.48550/arXiv.1412.6980
Kuster GT, Toksöz MN (1974) Velocity and attenuation of seismic waves in two-phase media: Part I. Theoretical formulations. Geophysics 39(5):587–616. https://doi.org/10.1190/1.1440450
Li YS, Song JY, Lu WK, Monkam P, Ao YL (2021) Multitask learning for super-resolution of seismic velocity model. IEEE Trans Geosci Remote Sens 59(9):8022–8033. https://doi.org/10.1109/TGRS.2020.3034502
Li D, Peng SP, Guo YL, Lu YX, Cui XQ, Du WF (2023) Progressive multitask learning for high-resolution prediction of reservoir elastic parameters. Geophysics 88(2):M71–M86. https://doi.org/10.1190/geo2022-0275.1
Liu XB, Shen GQ, Chen JY (2022) Amplitude variation with incidence angle inversion of fluid parameters in porous media using an accurate estimation of the Jacobian matrix. Geophysics 87(6):N95–N113. https://doi.org/10.1190/geo2021-0612.1
Lucier AM, Hofmann R, Bryndzia LT (2011) Evaluation of variable gas saturation on acoustic log data from the Haynesville shale gas play, NW Louisiana, USA. Lead Edge 30(3):300–311. https://doi.org/10.1190/1.3567261
Ma M, Zhang R (2023) Block sparse Bayesian learning-based prestack seismic inversion with the correlation of velocities and density. Acta Geophys 71:261–274. https://doi.org/10.1007/s11600-022-00914-4
Martin GS, Wiley R, Marfurt KJ (2006) Marmousi2: An elastic upgrade for Marmousi. Lead Edge 25(2):156–166. https://doi.org/10.1190/1.2172306
Mazzotti A, Zamboni E (2003) Petrophysical inversion of AVA data. Geophys Prospect 51:517–530. https://doi.org/10.1046/j.1365-2478.2003.00389.x
Morgan EC, Vanneste M, Lecomte I, Baise LG, Longva O, McAdoo B (2012) Estimation of free gas saturation from seismic reflection surveys by the genetic algorithm inversion of a P-wave attenuation model. Geophysics 77(4):R175–R187. https://doi.org/10.1190/geo2011-0291.1
Qi Q, Müller TM, Pervukhina M (2017) Sonic Qp/Qs ratio as diagnostic tool for shale gas saturation. Geophysics 82(3):MR97–MR103. https://doi.org/10.1190/geo2016-0499.1
Qi QM, Fu LY, Deng JX, Cao JX (2021) Attenuation methods for quantifying gas saturation in organic-rich shale and tight gas formations. Geophysics 86(2):D65–D75. https://doi.org/10.1190/geo2020-0291.1
Radke CJ, Gillis JV (1990) A dual gas tracer technique for determining trapped gas saturation during steady foam flow in porous media. SPE Annu Tech Conf Exhib. https://doi.org/10.2118/20519-MS
Rezaee R (2015) Fundamentals of gas shale reservoirs. John Wiley & Sons, Hoboken
Ruder S (2017) An overview of multi-task learning in deep neural networks. https://doi.org/10.48550/arXiv.1706.05098
Sang WJ, Yuan SY, Yong XS, Jiao XQ, Wang SX (2021) DCNNs-based denoising with a novel data generation for multidimensional geological structures learning. IEEE Geosci Remote Sens Lett 10(4):1851–1865. https://doi.org/10.1109/LGRS.2020.3007819
Sang WJ, Yuan SY, Han HW, Liu HJ, Yu Y (2023) Porosity prediction using semi-supervised learning with biased well log data for improving estimation accuracy and reducing prediction uncertainty. Geophys J Int 232(2):940–957. https://doi.org/10.1093/gji/ggac371
Schlumberger (1989) Schlumberger log interpretation principles/applications. Schlumberger Education Services.
She B, Wang YJ, Liang JD, Liu ZN, Song CY, Hu GM (2018) Adata-driven amplitude variation with offset inversion method via learned dictionaries and sparse representation. Geophysics 83(6):R725–R748. https://doi.org/10.1190/geo2017-0615.1
Singh V, Hegazy M, Fontanelli L (2009) Assessment of reservoir uncertainties for development evaluation and risk analysis. Lead Edge 28(3):272–282. https://doi.org/10.1190/1.3104070
Song ZH, Yuan SY, Li ZM, Wang SX (2022) kNN-based gas-bearing prediction using local waveform similarity gas-indication attribute—an application to a tight sandstone reservoir. Interpretation 10(1):SA25–SA33. https://doi.org/10.1190/INT-2021-0045.1
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861
Weinzierl W, Wiese B (2021) Deep learning a poroelastic rock-physics model for pressure and saturation discrimination. Geophysics 86(1):MR53–MR66. https://doi.org/10.1190/geo2020-0049.1
Wu XM, Liang LM, Shi YZ, Geng ZC, Fomel S (2019) Multitask learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophys J Int 219:2097–2109. https://doi.org/10.1093/gji/ggz418
Wu XM, Yan SS, Bi ZF, Zhang SB, Si HJ (2021) Deep learning for multidimensional seismic impedance inversion. Geophysics 86(5):R735–R745. https://doi.org/10.1190/geo2020-0564.1
Xu JL, Xu L, Qin YX (2017) Two effective methods for calculating water saturations in shale-gas reservoirs. Geophysics 82(3):D187–D197. https://doi.org/10.1190/geo2016-0462.1
Yu SW, Ma JW, Wang WL (2019) Deep learning for denoising. Geophysics 84(6):V333–V350. https://doi.org/10.1190/geo2018-0668.1
Yu C, Zhang CM, Shi WR, Li ML, Yan W (2022) Research and comparison of evaluation methods for shale reservoir saturation in Longtan Formation of marine-continent transition facies. Geophysics 87(6):M293–M305. https://doi.org/10.1190/geo2021-0740.1
Yuan SY, Jiao XQ, Luo YN, Sang WJ, Wang SX (2022) Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recover. Geophysics 87(2):R165–R181. https://doi.org/10.1190/geo2020-0421.1
Zhang K, Lin NT, Tian GP, Yang JQ, Wang DY, Jin ZW (2022a) Unsupervised-learning based self-organizing neural network using multi-component seismic data: application to Xujiahe tight-sand gas reservoir in China. J Petrol Sci Eng 209:109964. https://doi.org/10.1016/j.petrol.2021.109964
Zhang K, Lin NT, Yang JQ, Jin ZW, Li GH, Ding RW (2022b) Predicting gas-bearing distribution using DNN based on multi-component seismic data: quality evaluation using structural and fracture factors. Pet Sci 19(4):1566–1581. https://doi.org/10.1016/j.petsci.2022.02.008
Zhang J, Zhao XY, Chen YK, Sun H (2023) Domain knowledge-guided data-driven prestack seismic inversion using deep learning. Geophysics 88(2):M31–M47. https://doi.org/10.1190/geo2021-0560.1
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (U19B6003-004), the National Key R&D Program of China (2018YFA0702505), the National Natural Science Foundation of China (41974140), and R&D Department of China National Petroleum Corporation (2022DQ0604-01).
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Sang, W., Ding, Z., Li, M. et al. Prestack simultaneous inversion of P-wave impedance and gas saturation using multi-task residual networks. Acta Geophys. 72, 875–892 (2024). https://doi.org/10.1007/s11600-023-01132-2
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DOI: https://doi.org/10.1007/s11600-023-01132-2