Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm

Hwasoo Suk; Baehyun Min; Joe M. Kang; Cheolkyun Jeong

doi:10.1115/OMAE2016-54773

Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm

Hwasoo Suk, Baehyun Min, Joe M. Kang, Cheolkyun Jeong

Department of Climate and Energy Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study determines facies distribution in a clastic reservoir using a hidden Markov model combined with an Expectation-Maximization algorithm. Iterating expectation and maximization steps of the algorithm builds the hidden Markov model by tuning the model parameters including initial state distribution, state transition probability distribution, and observable symbol probability distribution. Optimized model parameters contribute to improving the predictability of facies distribution along the well trajectory using core and logging data.

Original language	English
Title of host publication	Polar and Arctic Sciences and Technology; Petroleum Technology
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791849996
DOIs	https://doi.org/10.1115/OMAE2016-54773
State	Published - 2016
Event	ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016 - Busan, Korea, Republic of Duration: 19 Jun 2016 → 24 Jun 2016

Publication series

Name	Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
Volume	8

Conference

Conference	ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016
Country/Territory	Korea, Republic of
City	Busan
Period	19/06/16 → 24/06/16

Bibliographical note

Publisher Copyright:
Copyright © 2016 by ASME.

Access to Document

10.1115/OMAE2016-54773

Cite this

Suk, H., Min, B., Kang, J. M., & Jeong, C. (2016). Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm. In Polar and Arctic Sciences and Technology; Petroleum Technology Article V008T11A012 (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; Vol. 8). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/OMAE2016-54773

Suk, Hwasoo ; Min, Baehyun ; Kang, Joe M. et al. / Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm. Polar and Arctic Sciences and Technology; Petroleum Technology. American Society of Mechanical Engineers (ASME), 2016. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE).

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title = "Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm",

abstract = "This study determines facies distribution in a clastic reservoir using a hidden Markov model combined with an Expectation-Maximization algorithm. Iterating expectation and maximization steps of the algorithm builds the hidden Markov model by tuning the model parameters including initial state distribution, state transition probability distribution, and observable symbol probability distribution. Optimized model parameters contribute to improving the predictability of facies distribution along the well trajectory using core and logging data.",

author = "Hwasoo Suk and Baehyun Min and Kang, {Joe M.} and Cheolkyun Jeong",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 by ASME.; ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016 ; Conference date: 19-06-2016 Through 24-06-2016",

year = "2016",

doi = "10.1115/OMAE2016-54773",

language = "English",

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Suk, H, Min, B, Kang, JM & Jeong, C 2016, Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm. in Polar and Arctic Sciences and Technology; Petroleum Technology., V008T11A012, Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, vol. 8, American Society of Mechanical Engineers (ASME), ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016, Busan, Korea, Republic of, 19/06/16. https://doi.org/10.1115/OMAE2016-54773

Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm. / Suk, Hwasoo; Min, Baehyun; Kang, Joe M. et al.
Polar and Arctic Sciences and Technology; Petroleum Technology. American Society of Mechanical Engineers (ASME), 2016. V008T11A012 (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; Vol. 8).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This study determines facies distribution in a clastic reservoir using a hidden Markov model combined with an Expectation-Maximization algorithm. Iterating expectation and maximization steps of the algorithm builds the hidden Markov model by tuning the model parameters including initial state distribution, state transition probability distribution, and observable symbol probability distribution. Optimized model parameters contribute to improving the predictability of facies distribution along the well trajectory using core and logging data.

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Suk H, Min B, Kang JM, Jeong C. Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm. In Polar and Arctic Sciences and Technology; Petroleum Technology. American Society of Mechanical Engineers (ASME). 2016. V008T11A012. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE). doi: 10.1115/OMAE2016-54773

Prediction of facies distribution in a clastic reservoir using a hidden Markov model combined with an expectation-maximization algorithm

Abstract

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