{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:00:38Z","timestamp":1756310438216,"version":"3.37.3"},"reference-count":14,"publisher":"Geological Society of London","issue":"1","license":[{"start":{"date-parts":[[2015,9,21]],"date-time":"2015-09-21T00:00:00Z","timestamp":1442793600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-002"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PG"],"published-print":{"date-parts":[[2016,2]]},"abstract":"<jats:p>\n            Reservoir modelling studies are widespread and are often built into a formal gated process used for decision-making. Now ubiquitous, it is easy for the models to simply become tools for verification of a decision that has partially or wholly been made \u2013 \u2018modelling for comfort\u2019. This is particularly the case in mature fields, when the presence of an inherited model already anchors the view of the field, and the volume of production data discourages the practitioner from exploring uncertainties with multiple models. It is proposed that reservoir modelling offers most value when used to create some\n            <jats:italic>discomfort<\/jats:italic>\n            \u2013 a stress test for decision-making that can identify upsides and secure against loss. This requires an awareness of the biases at work in model design and a conscious choice to move away from the default of a single, detailed, full-field model. This ideally means moving away from base-case-led modelling altogether, and typically involves multi-scale model design and multiple models for uncertainty handling, based either on stochastic modelling or multi-deterministic, scenario-based approaches.\n          <\/jats:p>","DOI":"10.1144\/petgeo2014-089","type":"journal-article","created":{"date-parts":[[2015,9,22]],"date-time":"2015-09-22T01:43:28Z","timestamp":1442886208000},"page":"3-10","source":"Crossref","is-referenced-by-count":19,"title":["Modelling for comfort?"],"prefix":"10.1144","volume":"22","author":[{"given":"Mark","family":"Bentley","sequence":"first","affiliation":[{"name":"AGR TRACS Training, Union Plaza, Aberdeen AB10 1SL, UK"}]}],"member":"1881","published-online":{"date-parts":[[2015,9,21]]},"reference":[{"key":"e_1_3_1_2_1","doi-asserted-by":"crossref","unstructured":"Baddeley M.C. Curtis A. Wood R. 2004. An introduction to prior information derived from probabilistic judgements: elicitation of knowledge cognitive bias and herding. In : Curtis A. Wood R.\n (eds) Geological Prior Information: Informing Science and Engineering . Geological Society London Special Publications 239 15\u201327 http:\/\/dx.doi.org\/10.1144\/GSL.SP.2004.239.01.02.","DOI":"10.1144\/GSL.SP.2004.239.01.02"},{"key":"e_1_3_1_3_1","doi-asserted-by":"crossref","unstructured":"Bentley M. Smith S. 2008. Scenario-based reservoir modelling: the need for more determinism and less anchoring. In : Robinson A. Griffiths P. Price S. Hegre J. Muggeridge A.\n (eds) The Future of Geological Modelling in Hydrocarbon Development . 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