Stochastic clustering and pattern matching for real-time geosteering

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dc.citation.firstpageID13en_US
dc.citation.issueNumber5en_US
dc.citation.journalTitleGeophysicsen_US
dc.citation.lastpageID24en_US
dc.citation.volumeNumber84en_US
dc.contributor.authorWu, Mingqien_US
dc.contributor.authorMiao, Yinsenen_US
dc.contributor.authorPanchal, Neilkunalen_US
dc.contributor.authorKowal, Daniel R.en_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorVila, Jeremyen_US
dc.contributor.authorLiang, Famingen_US
dc.date.accessioned2019-11-05T17:30:04Zen_US
dc.date.available2019-11-05T17:30:04Zen_US
dc.date.issued2019en_US
dc.description.abstractWe have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally, an inference is made based on the samples simulated from the posterior distribution using stochastic approximation Monte Carlo in which we extract the most likely earth model and the associated credible intervals as a quantified confidence indicator. We extensively test our method using synthetic and real geosteering data sets. Our method consistently achieves good performance on synthetic data sets with high correlations between the interpreted and the reference logs and provides similar interpretations as the geosteering geologists on four real wells. We also conduct a reliability performance test of the method on a benchmark set of 200 horizontal wells randomly sampled from the Permian Basin. Our Bayesian framework informs geologists with key drilling decisions in real time and helps them navigate the drilling bit into the target formation with confidence.en_US
dc.identifier.citationWu, Mingqi, Miao, Yinsen, Panchal, Neilkunal, et al.. "Stochastic clustering and pattern matching for real-time geosteering." <i>Geophysics,</i> 84, no. 5 (2019) Society of Exploration Geophysicists: ID13-ID24. https://doi.org/10.1190/geo2018-0781.1.en_US
dc.identifier.digitalgeo2018-0781.1en_US
dc.identifier.doihttps://doi.org/10.1190/geo2018-0781.1en_US
dc.identifier.urihttps://hdl.handle.net/1911/107597en_US
dc.language.isoengen_US
dc.publisherSociety of Exploration Geophysicistsen_US
dc.rightsAll article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY-NC-ND). See http://creativecommons.org/licenses/by/4.0/ Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its digital object identifier (DOI). Commercial reuse and derivatives are not permitted.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.titleStochastic clustering and pattern matching for real-time geosteeringen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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