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Geostatistical inversion can combine the advantages of two aspects: high horizontal resolution from deterministic seismic inversion and high vertical resolution from geostatistical simulation. Therefore, it becomes a new high resolution seismic inversion method. The method uses Bayesian inference to determine a posteriori probability density function of reservoir parameters based on seismic data, well logs and geostatistical information. Then, reservoir parameters are sampled and estimated using MCMC. The results have high resolution, including lithology, porosity, saturation and volume of shale et al., which can be used for reservoir evaluation, geological modeling and reservoir simulation. In this paper, the principle and workflow of geostatistical inversion based on Bayes-MCMC algorithm are firstly introduced. Then the integrated research of seismic inversion, geological modeling and reservoir simulation based on geostatistical inversion are applied to the development of Marine reservoirs in the South China Sea. This method improved the accuracy of geological modeling and efficiency of reservoir simulation.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1789--1797
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
- Shenyang Center of China Geological Survey, Shenyang 110034, China
- Northeast Geological S&T Innovation Center of China Geological Survey, Shenyang 110034, China
Bibliografia
- 1. Contreras A (2005) AVA stochastic inversion of pre-stack seismic inversion data and well logs for 3D reservoir modeling, EAGE 67th Annual Conference & Exhibition, June13–16
- 2. Dubrule O, Thibaut M, Lamy P (1998) Geostatistical reservoir aracterization constrained by 3d seismic data. Petrol Sci 1(4):121–128
- 3. Galuzzi BG, Candelieri A, Perego R et al (2018) Bayesian optimization for full waveform inversion. Internation Conference on Optimization and Decision Science
- 4. Gilks WR, Richardson S (1996) Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC, London
- 5. Guo P, Visser G, Saygin E (2020a) Bayesian trans-dimensional full waveform inversion: synthetic and field data application. Geophys J Int 222:610–627
- 6. Guo TC, Jiang MJ, Ji YZ et al (2020b) Application of prestack geostatistical inversion in prediction of shale sweet spot and thin interlayer: a case study of Block W, Western Canada Basin, Oil Geophysical Prospecting, V. 55(01), 10–11+180-188
- 7. Haas A, Dubrule O (1994) Geostatistical inversion-A sequential method for stochastic reservoir modeling constrained by seismic data. First Break 13(12):61–569
- 8. Hameed M, Al-Khaled O, Al-Qallaf H (2011) Highly detailed reservoir characterization through geostatistical inversion to assess porosity distribution in the Ratawi Limestone, Umm Gudair Field, Kuwait: 81st SEG Annual Meeting San Antonio, SEG
- 9. Kerry AR et al (2014) Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization. Geophys J Int 199(3):1847–1860
- 10. Li FM, Ji ZF, Zhao GL (2007) Geostatistical inversion of stochastic seismic inversion method: with Sudan M Basin P Oilfield as example. Petrol Explor Develop 34(4):451–455
- 11. Li S, Xu CQ, Cui F et al (2020) Application of geostatistical inversion in thin coal seam prediction. Safety Coal Mines 051(001):161–165
- 12. Li Z J, Chen F, Jiang J Y (2009) Fractured carbonate reservoir equivalent porosity model and quantitative research of reservoir modeling, CPS/SEG Beijing 2009 International Geophysical Conference & Exposition
- 13. Ray A, Sekar A, Hoversten GM et al (2016) Frequency domain full waveform elastic inversion of marine seismic data from the Alba field using a Bayesian trans-dimensional algorithm. Geophys J Int 205:915–937
- 14. Sun SM, Peng SM (2007) Geostatistical inversion method and its application in thin sand prediction. XI’AN Petrol Univ (Natural Science Edition) 22(1):41–44
- 15. Visser G, Guo P, Saygin E (2019) Bayesian transdimensional seismic full waveform inversion with a dipping layer parameterization. Geophysics 84:845–858
- 16. Wei XJ, Xiong LH, Wan M (2009) The application of method integrated of Markov Chain: Monte Carlo algorithm generic likelihood uncertainty estimation for hydrological models. J Hydraulic Eng 40(4):464–473
- 17. Yang JM, Quan B, Zhang WT et al (2020) Uncertainty analysis of three-dimensional geological model based on Monte Carlo simulation. Fault Block Oil Gas Field 027(003):309–312
- 18. Yi P, Lin GK (2005) Stochastic seismic inversion technique and its application in Wenchang 13–1 oil field. Oil Geophys Prospect 40(1):87–91
- 19. Zhang GZ, Wang DY, Yin XY (2011) Study on prestack seismic inversion using markov Chain monte carlo. Chinese J Geophys 54(11):2926–2932
Typ dokumentu
Bibliografia
Identyfikator YADDA
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