Application of pre-stack geostatistical inversion in prediction of thin high-quality conglomerate reservoir in Shawan Depression, Junggar Basin

Abstract

In the Baikouquan Formation of the Shawan Depression, there exist thin, high-quality conglomerate reservoirs with low porosity and low permeability. Only the underwater gray-green conglomerate with medium porosity is considered a high-quality reservoir. Due to the overlapping wave impedance between these thin high-quality reservoirs and tight layers, traditional post-stack inversion and pre-stack simultaneous inversion methods are ineffective in predicting such thin reservoirs. Pre-stack geostatistical inversion, which combines the advantages of simultaneous inversion and geostatistical inversion, has proven to be effective in this context. This study integrates core, logging, and test data to construct petrophysical charts using P-impedance and V_p/V_s ratios, idengtify key reservoir parameters, and apply pre-stack geostatistical inversion to predict thin high-quality conglomerate reservoirs. The results show that pre-stack geostatistical inversion can accurately identify thin high-quality reservoirs, providing a reliable basis for further exploration and development.

Graphical abstract

Keywords

Junggar Basin / Shawan Depression / tight conglomerate / high-quality reservoir / pre-stack geostatistical inversion

References

[1] Connolly P (1999). Elastic impedance.The Leading Edge (Tulsa Okla), 18(4): 438–452

[2] Cui Y Q, Shao L Y, Xie J R, Hu J L, Du W L, Feng X Y (2010). Several problems concerning reservoir prediction of fluvial sandstone.Geophys Geochem Explor, 34(1): 54–58

[3] Dong Q, Lu S F, Zhang X J, Shang J H (2013). Selection of parameters and reliability analysis of results in geostatistical inversion method.Geophys Geochem Explor, 37(2): 328–332,337

[4] Dubrule O, Thibaut M, Lamy P, Hass A (1998). Geostatistical reservoir characterization constrained by 3D seismic data.Petrol Science, 4(2): 121–128

[5] Fan P J, Ma L T, Wang Z J, Fan Y E, Gao Y F, Liang X (2017). Variogram geological implication and its calculating method discussing for geostatistical inversion.Progress Geophys, 32(6): 2444–2450

[6] Gogoi T, Chatterjee R (2019). Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India.Geosci Front, 10(3): 1113–1124

[7] Grana D, Della Rossa E (2010). Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion.Geophysics, 75(3): 1942–2156

[8] Grana D, Fjeldstad T, Omre H (2017). Bayesian Gaussian mixture linear inversion for geophysical inverse problems.Math Geosci, 49(4): 493–515

[9] Hass A, Dubrule O (1994). Geostatistical inversion-A sequential method for stochastic reservoir modeling constrained by seismic data.First Break, 13(12): 561–569

[10] He H H, Li S H, Du J Y, Li M, Wang Q (2011). The application of geostatistic inversion method to prediction the thin sandstone reservoir.Geophys Geochem Explor, 35(6): 804–808

[11] Li F M, Ji Z F, Zhao G L, Wu C J, Li Z M (2007). Methodology and application of stochastic seismic inversion: a case from P Oilfield, M Basin, Sudan.Petrol Explor Develop, 34(4): 451–455

[12] Liu W B, Wang Y N, Teng M, Tang J K (2011). Application of seismic inversion for reservoir prediction of Lamadian oilfield. J Oil Gas Technology 33(12): 63-67 (in Chinese)

[13] Luo K, Zong Z Y (2022). Probabilistic Fisher discriminant analysis based on Gaussian mixture model for estimating shale oil sweet spots.Front Earth Sci, 16(3): 557–567

[14] Qin Y S, Guo H X, Chen Y F (2009). Effect of variogram on seismic inversion accuracy.J Daqing Petrol Institute, 33(05): 37–40

[15] Rothman D H (1998). Geostatistical inversion of 3D seismic data for thin sand delineation.Geophysics, 51(2): 332–346

[16] Shen G Q, Meng X J, Wang Y M, Niu X M, Sun Z T (2004). Random seismic inversion and its application in Chengbei No. 35 well zone.Oil Geophys Prospect, 39(01): 75–81

[17] Su Y, Li L M, Zhong Z, Tang J (2009). Application of random inversion in reservoir prediction.Coal Geo Explor, 37(6): 63–66

[18] Wan H, Fan X Y, Liu T, Ren K Y, Liu Q M, Zhang H B, Cao C H (2012). Methods and applications for improving pre-stack seismic data revolution.Progress Geophys, 27(01): 304–311

[19] Wang X W, Liu H, Teng B B, Wang L Y (2012). Application of geostatistical inversion to thin reservoir prediction.Oil Gas Geol, 33(5): 730–735

[20] Wang X, Zhang Y Q, Yu Z L, Wang G M, Li X X (2011). Prediction of thin reservoir with pre-stack inversion combined with geostatistical simulation.Oil Geophys Prospect, 46(05): 744–748

[21] Wu H B, Zhu S J, Liu Q J, Dong S H, Huang Y H, Zhang P S (2023). Amplitude variation with offset and azimuth inversion to predict and evaluate coal seam fracture parameters.Front Earth Sci, 17(2): 505–513

[22] Zhao L (2010). Application of Monte Carlo simulation of Markov chain in seismic inversion. J Oil Gas Technol. 32(2): 249-252 (in Chinese)

[23] Zhong Y, Zhou L, Tan X, Lian C, Liu H, Liao J, Hu G, Liu M, Cao J (2017). Lithofacies paleogeography mapping and reservoir prediction in tight sandstone strata: a case study from central Sichuan Basin, China. Geosci Frontiers 8(5): 961–975

[24] Zong Z Y, Yin X Y, Zhang F, Wu G C (2012). Reflection coefficient and pre-stack seismic inversion with Young’s modulus and Poisson’s ratio.Chinese J Geophys, 55(11): 3786–3794

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