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dc.contributor.authorKaufman, G. M
dc.contributor.authorOlea, R. A
dc.contributor.authorFaith, R.
dc.contributor.authorBlondes, M. S
dc.date.accessioned2022-03-21T13:51:47Z
dc.date.available2021-09-20T17:16:54Z
dc.date.available2022-03-21T13:51:47Z
dc.date.issued2018-07
dc.date.submitted2016-10
dc.identifier.issn1874-8953
dc.identifier.issn1874-8961
dc.identifier.urihttps://hdl.handle.net/1721.1/131397.2
dc.description.abstractAbstract Commodities such as oil and gas occur in isolated reservoirs or accumulations, more generically called basic units here. To understand a study area’s economic potential and to craft plans for exploration and development, resource analysts often aggregate (sum, accumulate) basic unit magnitudes in distinct spatial subsets of the study area and then appraise the total area’s potential by summing these intermediate sums. In a probabilistic approach, magnitudes are modeled as random variables. Some have asked, “Do different methods of partitioning basic units into subsets lead to different probability distributions for the sum of all basic unit magnitudes?” Any method of aggregation of basic unit magnitudes which obeys the rules of probability leads to the same probability distribution of the sum of all unit magnitudes as that computed by direct summation of all basic unit magnitudes. A Monte Carlo simulation of a synthetic example in which the magnitude of resource in each unit is marginally lognormal and pairwise correlations among basic unit magnitudes are specified illustrates key features of probabilistic aggregation. The joint distribution of certain pairs of aggregates are closely approximated by a bivariate lognormal distribution.en_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/s11004-018-9747-9en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer Berlin Heidelbergen_US
dc.titleProbabilistic Aggregation of Uncertain Geological Resourcesen_US
dc.typeArticleen_US
dc.contributor.departmentSloan School of Management
dc.relation.journalMathematical Geosciencesen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-09-24T21:03:49Z
dc.language.rfc3066en
dc.rights.holderInternational Association for Mathematical Geosciences
dspace.embargo.termsY
dspace.date.submission2020-09-24T21:03:49Z
mit.journal.volume50en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work Neededen_US


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