Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Abstract Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) amo...

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Autores principales:	Kyungbook Lee, Sungil Kim, Jonggeun Choe, Baehyun Min, Hyun Suk Lee
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2018
Materias:	History-matched facies probability map Training image rejection Iterative static modeling Channelized reservoirs Multiple-point statistics History matching Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/e5822848121e49acad9a3a9f2630e358
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spelling	oai:doaj.org-article:e5822848121e49acad9a3a9f2630e3582021-12-02T11:05:59ZIterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image10.1007/s12182-018-0254-x1672-51071995-8226https://doaj.org/article/e5822848121e49acad9a3a9f2630e3582018-08-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-018-0254-xhttps://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.Kyungbook LeeSungil KimJonggeun ChoeBaehyun MinHyun Suk LeeKeAi Communications Co., Ltd.articleHistory-matched facies probability mapTraining image rejectionIterative static modelingChannelized reservoirsMultiple-point statisticsHistory matchingScienceQPetrologyQE420-499ENPetroleum Science, Vol 16, Iss 1, Pp 127-147 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	History-matched facies probability map Training image rejection Iterative static modeling Channelized reservoirs Multiple-point statistics History matching Science Q Petrology QE420-499
spellingShingle	History-matched facies probability map Training image rejection Iterative static modeling Channelized reservoirs Multiple-point statistics History matching Science Q Petrology QE420-499 Kyungbook Lee Sungil Kim Jonggeun Choe Baehyun Min Hyun Suk Lee Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
description	Abstract Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.
format	article
author	Kyungbook Lee Sungil Kim Jonggeun Choe Baehyun Min Hyun Suk Lee
author_facet	Kyungbook Lee Sungil Kim Jonggeun Choe Baehyun Min Hyun Suk Lee
author_sort	Kyungbook Lee
title	Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
title_short	Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
title_full	Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
title_fullStr	Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
title_full_unstemmed	Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
title_sort	iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
publisher	KeAi Communications Co., Ltd.
publishDate	2018
url	https://doaj.org/article/e5822848121e49acad9a3a9f2630e358
work_keys_str_mv	AT kyungbooklee iterativestaticmodelingofchannelizedreservoirsusinghistorymatchedfaciesprobabilitydataandrejectionoftrainingimage AT sungilkim iterativestaticmodelingofchannelizedreservoirsusinghistorymatchedfaciesprobabilitydataandrejectionoftrainingimage AT jonggeunchoe iterativestaticmodelingofchannelizedreservoirsusinghistorymatchedfaciesprobabilitydataandrejectionoftrainingimage AT baehyunmin iterativestaticmodelingofchannelizedreservoirsusinghistorymatchedfaciesprobabilitydataandrejectionoftrainingimage AT hyunsuklee iterativestaticmodelingofchannelizedreservoirsusinghistorymatchedfaciesprobabilitydataandrejectionoftrainingimage
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Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

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