Moradi, Moslem, Asghari, Omid, Norouzi, Gholamhossein, Riahi, Mohammad, Sokooti, Reza. (1394). Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis. , 49(1), 131-142. doi: 10.22059/ijmge.2015.54636
Moslem Moradi; Omid Asghari; Gholamhossein Norouzi; Mohammad Riahi; Reza Sokooti. "Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis". , 49, 1, 1394, 131-142. doi: 10.22059/ijmge.2015.54636
Moradi, Moslem, Asghari, Omid, Norouzi, Gholamhossein, Riahi, Mohammad, Sokooti, Reza. (1394). 'Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis', , 49(1), pp. 131-142. doi: 10.22059/ijmge.2015.54636
Moradi, Moslem, Asghari, Omid, Norouzi, Gholamhossein, Riahi, Mohammad, Sokooti, Reza. Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis. , 1394; 49(1): 131-142. doi: 10.22059/ijmge.2015.54636

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

International Journal of Mining and Geo-Engineering
مقاله 12، دوره 49، شماره 1، 2015، صفحه 131-142    اصل مقاله (1.63 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijmge.2015.54636
نویسندگان
Moslem Moradi1؛ Omid Asghari* 1؛ Gholamhossein Norouzi1؛ Mohammad Riahi2؛ Reza Sokooti3
1Simulation and Data Processing Laboratory, Mining Engineering Department, University of Tehran, Iran
2Institute of Geophysics, University of Tehran, Iran
3NIOC Exploration Directorate, Iran
چکیده
Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior information in Bayesian statistics. Data integration leads to a probability density function (named as a posteriori probability) that can yield a model of subsurface. The Markov Chain Monte Carlo (MCMC) method is used to sample the posterior probability distribution, and the subsurface model characteristics can be extracted by analyzing a set of the samples. In this study, the theory of stochastic seismic inversion in a Bayesian framework was described and applied to infer P-impedance and porosity models. The comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more detailed information of subsurface character. Since multiple realizations are extracted by this method, an estimation of pore volume and uncertainty in the estimation were analyzed.
کلیدواژه‌ها
Bayesian theory؛ Geostatistics؛ stochastic seismic inversion؛ uncertainty
مراجع
آمار
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