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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، شهریور 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 | ||
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