JOINT BAYESIAN STOCHASTIC INVERSION OF WELL LOGS AND SEISMIC DATA FOR VOLUMETRIC UNCERTAINTY ANALYSIS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MORADI MOSLEM; ASGHARI OMID; NOROOZI GHOLAMHOSSEIN; RIAHI MOHAMMAD ALI; SOKOUTI REZA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Mining and Geo-Engineering Year:2015 | Volume:49 | Issue:1 Page(s): 131-142

Download Full-Text

View:

423

Download:

157

Cites:

Information Journal Paper

Title

JOINT BAYESIAN STOCHASTIC INVERSION OF WELL LOGS AND SEISMIC DATA FOR VOLUMETRIC UNCERTAINTY ANALYSIS

Author(s)

Keywords

BAYESIAN THEORY

GEOSTATISTICS

STOCHASTIC SEISMIC INVERSION

UNCERTAINTY

Abstract

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.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

MORADI, MOSLEM, ASGHARI, OMID, NOROOZI, GHOLAMHOSSEIN, RIAHI, MOHAMMAD ALI, & SOKOUTI, REZA. (2015). JOINT BAYESIAN STOCHASTIC INVERSION OF WELL LOGS AND SEISMIC DATA FOR VOLUMETRIC UNCERTAINTY ANALYSIS. INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING, 49(1), 131-142. SID. https://sid.ir/paper/329927/en

Vancouver: Copy

MORADI MOSLEM, ASGHARI OMID, NOROOZI GHOLAMHOSSEIN, RIAHI MOHAMMAD ALI, SOKOUTI REZA. JOINT BAYESIAN STOCHASTIC INVERSION OF WELL LOGS AND SEISMIC DATA FOR VOLUMETRIC UNCERTAINTY ANALYSIS. INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING[Internet]. 2015;49(1):131-142. Available from: https://sid.ir/paper/329927/en

IEEE: Copy

MOSLEM MORADI, OMID ASGHARI, GHOLAMHOSSEIN NOROOZI, MOHAMMAD ALI RIAHI, and REZA SOKOUTI, “JOINT BAYESIAN STOCHASTIC INVERSION OF WELL LOGS AND SEISMIC DATA FOR VOLUMETRIC UNCERTAINTY ANALYSIS,” INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING, vol. 49, no. 1, pp. 131–142, 2015, [Online]. Available: https://sid.ir/paper/329927/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops